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namespace QuantumRealEstate {
    open Microsoft.Quantum.Intrinsic;
    open Microsoft.Quantum.Measurement;
    open Microsoft.Quantum.Canon;
    open Microsoft.Quantum.Math;
    open Microsoft.Quantum.Convert;

    operation PrepareQuantumRegister(qubits : Qubit[]) : Unit {
        if (Length(qubits) == 0) {
            fail "Quantum register must contain at least one qubit.";
        }
        ApplyToEach(H, qubits);
    }

    operation MeasureQuantumRegister(qubits : Qubit[]) : Result[] {
        if (Length(qubits) == 0) {
            fail "No qubits provided for measurement.";
        }
        return ForEach(MResetZ, qubits);
    }

    operation OptimizeRealEstateInvestments(numProperties : Int, entangle : Bool) : Result[] {
        using (qubits = Qubit[numProperties]) {
            PrepareQuantumRegister(qubits);

            if (entangle) {
                for (i in 1..Length(qubits) - 1) {
                    Controlled X([qubits[i - 1]], qubits[i]);
                }
            }

            let investmentDecisions = MeasureQuantumRegister(qubits);
            ResetAll(qubits);
            return investmentDecisions;
        }
    }

    @EntryPoint()
    operation RunOptimization() : Unit {
        let numProperties = 5; // This can be dynamically set based on external data.
        let currentMarketCondition = true; // Assume a condition where entanglement is needed.
        let results = OptimizeRealEstateInvestments(numProperties, currentMarketCondition);
        Message($"Investment decisions: {results}");
    }

    operation TestOptimizationScenario() : Unit {
        let testResults = OptimizeRealEstateInvestments(5, true);
        if (Length(testResults) != 5) {
            fail "Test failed: Expected 5 results.";
        }
        Message("Test passed: Correct number of results obtained.");
    }
}

    // Function to prepare a quantum register in a superposition to represent investment decisions
    operation PrepareQuantumRegister(qubits : Qubit[]) : Unit {
        ApplyToEach(H, qubits);
    }

    // Function to measure the quantum register and interpret the results
    operation MeasureQuantumRegister(qubits : Qubit[]) : Result[] {
        return ForEach(MResetZ, qubits);
    }

    // Main operation to simulate optimal real estate investment decisions
    operation OptimizeRealEstateInvestments() : Result[] {
        // Initialize qubits for simulation
        using (qubits = Qubit[5]) { // Assume 5 properties for simplicity
            // Prepare qubits in a superposition state
            PrepareQuantumRegister(qubits);

            // Introduce entanglement between qubits to model correlation between properties
            for (i in 1..Length(qubits) - 1) {
                Controlled X([qubits[i-1]], qubits[i]);
            }

            // Measure qubits to collapse the state to classical information
            let investmentDecisions = MeasureQuantumRegister(qubits);

            // Reset and deallocate qubits
            ResetAll(qubits);

            return investmentDecisions;
        }
    }

    // Entry point for the simulation
    @EntryPoint()
    operation RunOptimization() : Unit {
        let results = OptimizeRealEstateInvestments();
        Message($"Investment decisions: {results}");
    }
}

  // Function to prepare a quantum register in a superposition to represent various investment options
    operation PrepareQuantumRegister(qubits : Qubit[], probabilities : Double[]) : Unit {
        for (i in 0..Length(qubits) - 1) {
            let angle = ArcCos(Sqrt(1.0 - probabilities[i]));
            Ry(2.0 * angle, qubits[i]);
        }
    }

    // Entangle qubits to represent correlations between different properties
    operation EntangleQubits(qubits : Qubit[]) : Unit {
        for (i in 0..Length(qubits) - 2) {
            Controlled X([qubits[i]], qubits[i + 1]);
        }
    }

    // Function to measure the quantum register and interpret the results
    operation MeasureQuantumRegister(qubits : Qubit[]) : Result[] {
        return ForEach(MResetZ, qubits);
    }

    // Operation to simulate investment decisions based on quantum probabilities
    operation SimulateInvestmentDecisions(qubits : Qubit[], probabilities : Double[]) : Result[] {
        PrepareQuantumRegister(qubits, probabilities);
        EntangleQubits(qubits);
        return MeasureQuantumRegister(qubits);
    }

    // Main operation to optimize real estate investments
    operation OptimizeRealEstateInvestments(probabilities : Double[]) : Result[] {
        using (qubits = Qubit[Length(probabilities)]) {
            let decisions = SimulateInvestmentDecisions(qubits, probabilities);
            ResetAll(qubits);
            return decisions;
        }
    }

    // Entry point for the simulation
    @EntryPoint()
    operation RunOptimization() : Unit {
        // Example: Probabilities of choosing to invest in each property (e.g., based on expected returns)
        let investmentProbabilities = [0.8, 0.6, 0.7, 0.5, 0.9];
        let results = OptimizeRealEstateInvestments(investmentProbabilities);
        Message($"Investment decisions: {results}");
    }
}



operation PrepareStateWithRotation(qubits : Qubit[], angles : Double[]) : Unit {
    for (idx in 0..Length(qubits) - 1) {
        Ry(2.0 * ArcSin(Sqrt(angles[idx])), qubits[idx]);
    }
}

operation EntangleQubitsInChain(qubits : Qubit[]) : Unit {
    for (idx in 0..Length(qubits) - 2) {
        CNOT(qubits[idx], qubits[idx + 1]);
    }
}

operation MeasureQubits(qubits : Qubit[]) : Result[] {
    mutable results = new Result[Length(qubits)];
    for (idx in 0..Length(qubits) - 1) {
        set results w/= idx <- MResetZ(qubits[idx]);
    }
    return results;
}

operation PerformQuantumDecisionAnalysis(qubits : Qubit[], probabilities : Double[]) : Result[] {
    let angles = Map(Sqrt, probabilities); // Assuming probabilities are provided in [0, 1] and need square root for angle calculation
    PrepareStateWithRotation(qubits, angles);
    EntangleQubitsInChain(qubits);
    return MeasureQubits(qubits);
}

operation RunRealEstateQuantumAnalysis(probabilities : Double[]) : Result[] {
    using (qubits = Qubit[Length(probabilities)]) {
        return PerformQuantumDecisionAnalysis(qubits, probabilities);
    }
}

@EntryPoint()
operation ExecuteRealEstateOptimization() : Unit {
    let investmentProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6];
    let results = RunRealEstateQuantumAnalysis(investmentProbabilities);
    Message($"Investment decision outcomes: {results}");
}
}

operation PrepareStateWithRotation(qubits : Qubit[], angles : Double[]) : Unit {
    for (idx in 0..Length(qubits) - 1) {
        Ry(2.0 * ArcSin(Sqrt(angles[idx])), qubits[idx]);
    }
}

operation EntangleQubitsInChain(qubits : Qubit[]) : Unit {
    for (idx in 0..Length(qubits) - 2) {
        CNOT(qubits[idx], qubits[idx + 1]);
    }
}

operation MeasureQubits(qubits : Qubit[]) : Result[] {
    mutable results = new Result[Length(qubits)];
    for (idx in 0..Length(qubits) - 1) {
        set results w/= idx <- MResetZ(qubits[idx]);
    }
    return results;
}

operation PerformQuantumDecisionAnalysis(qubits : Qubit[], probabilities : Double[]) : Result[] {
    let angles = Map(Sqrt, probabilities); // Adjust the calculation to use square roots for angle preparation
    PrepareStateWithRotation(qubits, angles);
    EntangleQubitsInChain(qubits);
    return MeasureQubits(qubits);
}

operation RunRealEstateQuantumAnalysis(probabilities : Double[]) : Result[] {
    using (qubits = Qubit[Length(probabilities)]) {
        return PerformQuantumDecisionAnalysis(qubits, probabilities);
    }
}

@EntryPoint()
operation ExecuteRealEstateOptimization() : Unit {
    let investmentProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6];
    let results = RunRealEstateQuantumAnalysis(investmentProbabilities);
    Message($"Investment decision outcomes: {results}");
}
}



    // Adjust probability inputs for more accurate state preparation
    function AdjustProbabilities(probabilities : Double[]) : Double[] {
        return Map(ArcSin, Map(Sqrt, probabilities));
    }

    // Enhance the entanglement process to represent different correlation strategies
    operation EntangleQubitsAdvanced(qubits : Qubit[], pattern : Int[]) : Unit {
        for (idx in 0..Length(pattern) - 2) {
            if (pattern[idx] == 1) {
                CNOT(qubits[idx], qubits[idx + 1]);
            }
            else {
                // Implement alternative entanglement strategy as needed
                CZ(qubits[idx], qubits[idx + 1]);
            }
        }
    }

    // Create a more dynamic investment analysis tool with parameterized entanglement
    operation PerformDynamicQuantumDecisionAnalysis(qubits : Qubit[], probabilities : Double[], pattern : Int[]) : Result[] {
        let adjustedProbabilities = AdjustProbabilities(probabilities);
        PrepareStateWithRotation(qubits, adjustedProbabilities);
        EntangleQubitsAdvanced(qubits, pattern);
        return MeasureQubits(qubits);
    }

    // Run the quantum analysis with a customizable entanglement pattern
    operation RunCustomizableRealEstateQuantumAnalysis(probabilities : Double[], pattern : Int[]) : Result[] {
        using (qubits = Qubit[Length(probabilities)]) {
            return PerformDynamicQuantumDecisionAnalysis(qubits, probabilities, pattern);
        }
    }

    // Entry point to execute with different entanglement strategies
    @EntryPoint()
    operation ExecuteFlexibleRealEstateOptimization() : Unit {
        let investmentProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6];
        let entanglementPattern = [1, 0, 1, 0]; // Define a pattern of entanglement
        let results = RunCustomizableRealEstateQuantumAnalysis(investmentProbabilities, entanglementPattern);
        Message($"Flexible investment decision outcomes: {results}");
    }
}



    // Implement a function to calculate entanglement angles based on market volatility
    function CalculateEntanglementAngles(marketVolatility : Double[]) : Double[] {
        return Map(x -> PI() * x, marketVolatility);
    }

    // Dynamic entanglement based on market conditions
    operation EntangleQubitsBasedOnMarket(qubits : Qubit[], marketVolatility : Double[]) : Unit {
        let angles = CalculateEntanglementAngles(marketVolatility);
        for (idx in 0..Length(qubits) - 2) {
            let angle = angles[idx];
            RY(angle, qubits[idx + 1]);
            CNOT(qubits[idx], qubits[idx + 1]);
            RY(-angle, qubits[idx + 1]);
        }
    }

    // Comprehensive quantum decision-making analysis considering market volatility
    operation ComprehensiveQuantumDecisionAnalysis(qubits : Qubit[], probabilities : Double[], marketVolatility : Double[]) : Result[] {
        let adjustedProbabilities = AdjustProbabilities(probabilities);
        PrepareStateWithRotation(qubits, adjustedProbabilities);
        EntangleQubitsBasedOnMarket(qubits, marketVolatility);
        return MeasureQubits(qubits);
    }

    // Run the analysis with market volatility adjustments
    operation RunMarketAdjustedQuantumAnalysis(probabilities : Double[], marketVolatility : Double[]) : Result[] {
        using (qubits = Qubit[Length(probabilities)]) {
            return ComprehensiveQuantumDecisionAnalysis(qubits, probabilities, marketVolatility);
        }
    }

    // Entry point for executing the quantum optimization with market considerations
    @EntryPoint()
    operation ExecuteMarketAdjustedOptimization() : Unit {
        let investmentProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6];
        let marketVolatility = [0.2, 0.3, 0.25, 0.15, 0.1]; // Hypothetical market volatility ratings
        let results = RunMarketAdjustedQuantumAnalysis(investmentProbabilities, marketVolatility);
        Message($"Market-adjusted investment decision outcomes: {results}");
    }
}
   // Introduce feedback mechanism to update probabilities based on previous outcomes
   function UpdateInvestmentProbabilities(previousResults : Result[], currentProbabilities : Double[]) : Double[] {
    mutable newProbabilities = currentProbabilities;
    for (idx in 0..Length(previousResults) - 1) {
        if (ResultAsInt(previousResults[idx]) == 1) {
            set newProbabilities w/= idx <- newProbabilities[idx] * 1.1; // Increase probability if the previous decision was to invest
        } else {
            set newProbabilities w/= idx <- newProbabilities[idx] * 0.9; // Decrease otherwise
        }
    }
    return newProbabilities;
}

// Re-run the quantum decision analysis with updated probabilities
operation AdaptiveQuantumDecisionAnalysis(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Result[] {
    mutable probabilities = initialProbabilities;
    mutable results = new Result[0];

    for (iteration in 1..iterations) {
        let currentResults = ComprehensiveQuantumDecisionAnalysis(qubits, probabilities, marketVolatility);
        set probabilities = UpdateInvestmentProbabilities(currentResults, probabilities);
        set results = currentResults;
    }

    return results;
}

// Entry point to execute the adaptive optimization
@EntryPoint()
operation ExecuteAdaptiveOptimization() : Unit {
    let initialProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6];
    let marketVolatility = [0.2, 0.3, 0.25, 0.15, 0.1]; // Market volatility ratings
    let iterations = 5; // Number of iterations to adaptively adjust the decision making

    using (qubits = Qubit[Length(initialProbabilities)]) {
        let finalResults = AdaptiveQuantumDecisionAnalysis(qubits, initialProbabilities, marketVolatility, iterations);
        Message($"Adaptive investment decision outcomes: {finalResults}");
    }
}
}



   // Additional function to normalize probabilities ensuring they remain valid between 0 and 1
   function NormalizeProbabilities(probabilities : Double[]) : Double[] {
    let total = Fold(PlusD, 0.0, probabilities);
    return Map(prob => prob / total, probabilities);
}

// Enhanced version of the update function to handle normalization
function EnhancedUpdateInvestmentProbabilities(previousResults : Result[], currentProbabilities : Double[]) : Double[] {
    mutable newProbabilities = currentProbabilities;
    for (idx in 0..Length(previousResults) - 1) {
        if (ResultAsInt(previousResults[idx]) == 1) {
            set newProbabilities w/= idx <- newProbabilities[idx] * 1.1; // Boost probability after positive outcomes
        } else {
            set newProbabilities w/= idx <- newProbabilities[idx] * 0.9; // Reduce after non-investment outcomes
        }
    }
    return NormalizeProbabilities(newProbabilities); // Normalize to keep probabilities valid
}

// Introducing quantum fluctuation to simulate real-world uncertainty
operation ApplyQuantumFluctuations(qubits : Qubit[], fluctuationRate : Double) : Unit {
    for (qubit in qubits) {
        let angle = 2.0 * PI() * fluctuationRate;
        R1(angle, qubit);
    }
}

// Comprehensive adaptive analysis with fluctuation consideration
operation ComprehensiveAdaptiveQuantumAnalysis(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], fluctuationRate : Double, iterations : Int) : Result[] {
    mutable probabilities = initialProbabilities;
    mutable results = new Result[0];

    for (iteration in 1..iterations) {
        ApplyQuantumFluctuations(qubits, fluctuationRate);
        let currentResults = ComprehensiveQuantumDecisionAnalysis(qubits, probabilities, marketVolatility);
        set probabilities = EnhancedUpdateInvestmentProbabilities(currentResults, probabilities);
        set results = currentResults;
    }

    return results;
}

// Entry point to execute the adaptive optimization with quantum fluctuations
@EntryPoint()
operation ExecuteComprehensiveAdaptiveOptimization() : Unit {
    let initialProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6];
    let marketVolatility = [0.2, 0.3, 0.25, 0.15, 0.1];
    let fluctuationRate = 0.05; // Quantum fluctuation rate
    let iterations = 10; // Number of iterations for adaptive adjustments

    using (qubits = Qubit[Length(initialProbabilities)]) {
        let finalResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, fluctuationRate, iterations);
        Message($"Final adaptive investment decision outcomes: {finalResults}");
    }
}
}





   // Operation to analyze the coherence time of qubits under different market conditions
   operation AnalyzeQubitCoherence(qubits : Qubit[], coherenceThresholds : Double[]) : Result[] {
    mutable coherenceResults = new Result[Length(qubits)];
    for (idx in 0..Length(qubits) - 1) {
        // Apply a phase rotation to simulate environmental interaction
        R1(coherenceThresholds[idx] * PI(), qubits[idx]);
        // Measure the qubit to determine if it has maintained its coherence
        set coherenceResults w/= idx <- MResetZ(qubits[idx]);
    }
    return coherenceResults;
}

// Function to dynamically adjust coherence thresholds based on market data
function AdjustCoherenceThresholds(marketData : Double[]) : Double[] {
    return Map(x -> x * 0.01, marketData); // Example: scale market data to represent coherence thresholds
}

// Operation to execute a full market simulation including coherence analysis
operation RunFullMarketSimulation(initialProbabilities : Double[], marketVolatility : Double[], marketData : Double[], iterations : Int) : Unit {
    using (qubits = Qubit[Length(initialProbabilities)]) {
        let coherenceThresholds = AdjustCoherenceThresholds(marketData);
        for (iteration in 1..iterations) {
            let simulationResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); // Run analysis for one cycle
            let coherenceResults = AnalyzeQubitCoherence(qubits, coherenceThresholds);
            Message($"Simulation results for iteration {iteration}: {simulationResults}");
            Message($"Coherence analysis results for iteration {iteration}: {coherenceResults}");
        }
    }
}

// EntryPoint to start the full simulation
@EntryPoint()
operation StartFullSimulation() : Unit {
    let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60];
    let marketVolatility = [0.20, 0.30, 0.25, 0.15, 0.10];
    let marketData = [500.0, 600.0, 550.0, 580.0, 610.0]; // Hypothetical market data points
    let iterations = 5; // Number of full simulations

    RunFullMarketSimulation(initialProbabilities, marketVolatility, marketData, iterations);
}
}



    // Enhance the coherence analysis to include feedback adjustment based on results
    operation AdjustQubitCoherenceBasedOnFeedback(qubits : Qubit[], coherenceResults : Result[], adjustmentFactors : Double[]) : Unit {
        for (idx in 0..Length(qubits) - 1) {
            let adjustment = adjustmentFactors[idx];
            if (ResultAsInt(coherenceResults[idx]) == 1) {
                // If the qubit maintained coherence, slightly decrease the adjustment factor
                set adjustmentFactors w/= idx <- adjustment * 0.95;
            } else {
                // If the qubit lost coherence, increase the adjustment factor
                set adjustmentFactors w/= idx <- adjustment * 1.05;
            }
            // Apply the adjusted phase rotation
            R1(adjustment * PI(), qubits[idx]);
        }
    }

    // Operation to run enhanced simulations incorporating dynamic coherence adjustments
    operation RunEnhancedMarketSimulations(initialProbabilities : Double[], marketVolatility : Double[], marketData : Double[], iterations : Int) : Unit {
        using (qubits = Qubit[Length(initialProbabilities)]) {
            let coherenceThresholds = AdjustCoherenceThresholds(marketData);
            mutable adjustmentFactors = coherenceThresholds; // Initialize adjustments based on initial thresholds

            for (iteration in 1..iterations) {
                let simulationResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); // Single cycle analysis
                let coherenceResults = AnalyzeQubitCoherence(qubits, coherenceThresholds);
                AdjustQubitCoherenceBasedOnFeedback(qubits, coherenceResults, adjustmentFactors);
                Message($"Enhanced simulation results for iteration {iteration}: {simulationResults}");
                Message($"Coherence adjustment factors for iteration {iteration}: {adjustmentFactors}");
            }
        }
    }

    // EntryPoint to start the enhanced simulations with dynamic coherence adjustments
    @EntryPoint()
    operation StartEnhancedSimulations() : Unit {
        let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60];
        let marketVolatility = [0.20, 0.30, 0.25, 0.15, 0.10];
        let marketData = [500.0, 600.0, 550.0, 580.0, 610.0]; // Example market data points
        let iterations = 10; // Number of enhanced simulations

        RunEnhancedMarketSimulations(initialProbabilities, marketVolatility, marketData, iterations);
    }
}





    // Enhance the adjustment mechanism to consider historical coherence data
    function UpdateAdjustmentFactorsBasedOnHistory(adjustmentFactors : Double[], historicalCoherence : Result[][], iteration : Int) : Double[] {
        mutable updatedFactors = adjustmentFactors;

        // Adjust based on historical results to find a trend in coherence maintenance
        for (idx in 0..Length(adjustmentFactors) - 1) {
            let historicalResults = [historicalCoherence[j][idx] for j in 0..iteration - 1];
            let successes = Fold(PlusI, 0, [ResultAsInt(result) for result in historicalResults]);

            // Apply a stronger adjustment if coherence was frequently lost
            if (2 * successes < iteration) { // Less than 50% success rate
                set updatedFactors w/= idx <- updatedFactors[idx] * 1.10;
            } else {
                set updatedFactors w/= idx <- updatedFactors[idx] * 0.90;
            }
        }
        return updatedFactors;
    }

    // Implement a feedback loop to adaptively adjust the coherence strategies over multiple iterations
    operation RunAdaptiveCoherenceSimulations(initialProbabilities : Double[], marketVolatility : Double[], marketData : Double[], iterations : Int) : Unit {
        using (qubits = Qubit[Length(initialProbabilities)]) {
            let initialCoherenceThresholds = AdjustCoherenceThresholds(marketData);
            mutable adjustmentFactors = initialCoherenceThresholds;
            mutable historicalCoherence = new Result[][iterations];

            for (iteration in 1..iterations) {
                let simulationResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1);
                let coherenceResults = AnalyzeQubitCoherence(qubits, Map(idx -> adjustmentFactors[idx] * PI(), initialCoherenceThresholds));
                set historicalCoherence w/= iteration - 1 <- coherenceResults;
                set adjustmentFactors = UpdateAdjustmentFactorsBasedOnHistory(adjustmentFactors, historicalCoherence, iteration);
                Message($"Iteration {iteration}: Simulation results - {simulationResults}, Coherence results - {coherenceResults}");
            }
        }
    }

    // EntryPoint to execute the adaptive coherence simulations
    @EntryPoint()
    operation ExecuteAdaptiveCoherenceSimulations() : Unit {
        let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60];
        let marketVolatility = [0.20, 0.30, 0.25, 0.15, 0.10];
        let marketData = [500.0, 600.0, 550.0, 580.0, 610.0];
        let iterations = 20;

        RunAdaptiveCoherenceSimulations(initialProbabilities, marketVolatility, marketData, iterations);
    }
}


// Include a module for visualizing the results of simulations open Microsoft.Quantum.Diagnostics;

// Function to convert results into percentage of coherence function CoherencePercentage(historicalCoherence : Result[][], iterations : Int) : Double[] { let totalQubits = Length(historicalCoherence[0]); mutable percentages = new Double[totalQubits];


for (qubitIdx in 0..totalQubits - 1) {
    let coherenceCount = Fold(PlusI, 0, [ResultAsInt(historicalCoherence[iter][qubitIdx]) for iter in 0..iterations - 1]);
    let percentage = (100.0 * IntAsDouble(coherenceCount)) / IntAsDouble(iterations);
    set percentages w/= qubitIdx <- percentage;
}

return percentages;
}

// Visualize coherence over multiple iterations to monitor stability and performance operation VisualizeCoherenceTrends(historicalCoherence : Result[][], iterations : Int) : Unit { let coherencePercentages = CoherencePercentage(historicalCoherence, iterations); Message("Coherence percentages over iterations:"); for (idx in 0..Length(coherencePercentages) - 1) { Message($"Qubit {idx}: {coherencePercentages[idx]}%"); } }

// Enhance decision making with a Quantum Annealer approach for solving complex optimization problems operation QuantumAnnealerOptimization(initialProbabilities : Double[], iterations : Int) : Result[] { using (qubits = Qubit[Length(initialProbabilities)]) { mutable bestResult = new Result[Length(qubits)]; mutable bestScore = 0.0;


    for (iter in 1..iterations) {
        let results = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, [0.05], 0.05, 1);
        let currentScore = Fold(PlusD, 0.0, [ResultAsDouble(result) for result in results]);

        if (currentScore > bestScore) {
            set bestScore = currentScore;
            set bestResult = results;
        }
    }

    return bestResult;
}
}

// EntryPoint for a comprehensive quantum real estate optimization including annealing simulation @EntryPoint() operation ExecuteQuantumAnnealingOptimization() : Unit { let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60]; let iterations = 100; let optimalResults = QuantumAnnealerOptimization(initialProbabilities, iterations);


Message($"Optimal investment decisions after quantum annealing: {optimalResults}");
}






// Integrate environmental factors into quantum analysis for real-time adjustments function AdjustProbabilitiesForEnvironmentalFactors(probabilities : Double[], environmentalFactors : Double[]) : Double[] { return Zip(probabilities, environmentalFactors, (prob, env) -> prob * env); }

// Update probabilities based on environmental and market conditions operation UpdateProbabilitiesWithRealTimeData(initialProbabilities : Double[], marketData : Double[], environmentalData : Double[]) : Double[] { let marketAdjustedProbabilities = AdjustCoherenceThresholds(marketData); let environmentAdjustedProbabilities = AdjustProbabilitiesForEnvironmentalFactors(marketAdjustedProbabilities, environmentalData); return NormalizeProbabilities(environmentAdjustedProbabilities); }

// Run a simulation that adapts to changing real estate market and environmental conditions operation RunDynamicSimulation(initialProbabilities : Double[], marketVolatility : Double[], marketData : Double[], environmentalData : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { mutable dynamicProbabilities = initialProbabilities;


    for (iteration in 1..iterations) {
        let updatedProbabilities = UpdateProbabilitiesWithRealTimeData(dynamicProbabilities, marketData, environmentalData);
        let simulationResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, updatedProbabilities, marketVolatility, 0.05, 1);
        Message($"Dynamic simulation results for iteration {iteration}: {simulationResults}");

        // Update probabilities based on the latest simulation results and real-time data
        set dynamicProbabilities = updatedProbabilities;
    }
}
}

// EntryPoint to execute dynamic simulations accounting for market and environmental changes @EntryPoint() operation ExecuteDynamicSimulations() : Unit { let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60]; let marketVolatility = [0.20, 0.30, 0.25, 0.15, 0.10]; let marketData = [500.0, 600.0, 550.0, 580.0, 610.0]; // Simulated market data points let environmentalData = [1.0, 0.8, 0.9, 1.1, 0.85]; // Environmental impact factors let iterations = 15;


RunDynamicSimulation(initialProbabilities, marketVolatility, marketData, environmentalData, iterations);
}




or (iteration in 1..iterations) {
    let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1);
    let currentRisk = CalculateRiskFromResults(currentResults);

    if (currentRisk < bestRisk) {
        set bestRisk = currentRisk;
        set bestDecisions = currentResults;
    }

    Message($"Iteration {iteration}: Current Risk = {currentRisk}");
}

Message($"Best investment decisions with lowest risk: {bestDecisions}");
}
}

// EntryPoint to run the risk-adjusted simulation for real estate investment @EntryPoint() operation ExecuteRiskAdjustedSimulations() : Unit { let initialProbabilities = [0.70, 0.60, 0.65, 0.80, 0.75]; let marketVolatility = [0.25, 0.35, 0.30, 0.20, 0.25]; let iterations = 20;


RiskAdjustedInvestmentDecisions(initialProbabilities, marketVolatility, iterations);


// Introduce a feedback mechanism to refine investment strategies over time operation FeedbackBasedRefinement(initialResults : Result[], initialRisk : Double, qubits : Qubit[], probabilities : Double[], marketVolatility : Double[], iterations : Int) : (Result[], Double) { mutable refinedResults = initialResults; mutable refinedRisk = initialRisk;

for (iteration in 1..iterations) {
    let updatedProbabilities = UpdateProbabilitiesWithRealTimeData(probabilities, Map(x -> x + 0.01 * iteration, marketVolatility), probabilities); // Slightly adjust market data
    let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, updatedProbabilities, marketVolatility, 0.05, 1);
    let currentRisk = CalculateRiskFromResults(currentResults);

    // Check if the new strategy offers a lower risk
    if (currentRisk < refinedRisk) {
        set refinedRisk = currentRisk;
        set refinedResults = currentResults;
    }
}

return (refinedResults, refinedRisk);

}

// Implement a decision loop that adapts based on feedback over multiple market scenarios operation DecisionFeedbackLoop(initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { let (initialResults, initialRisk) = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); let (optimalResults, optimalRisk) = FeedbackBasedRefinement(initialResults, initialRisk, qubits, initialProbabilities, marketVolatility, iterations);

Message($"Optimal risk-adjusted decisions after feedback loop: {optimalResults}");
Message($"Achieved risk level: {optimalRisk}");
}


}

// EntryPoint to execute the entire decision process including dynamic feedback @EntryPoint() operation StartDecisionFeedbackProcess() : Unit { let initialProbabilities = [0.75, 0.70, 0.65, 0.60, 0.55]; let marketVolatility = [0.20, 0.22, 0.25, 0.28, 0.30]; let iterations = 10;

DecisionFeedbackLoop(initialProbabilities, marketVolatility, iterations);






// Enhance the feedback loop with environmental considerations function IncorporateEnvironmentalFactors(probabilities : Double[], environmentalImpact : Double[]) : Double[] { return Zip(probabilities, environmentalImpact, (prob, impact) -> prob * impact); }

// Modify the decision process to include environmental data in the feedback loop operation EnvironmentalFeedbackDecisionLoop(initialProbabilities : Double[], marketVolatility : Double[], environmentalData : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { // Initial comprehensive analysis to set baseline results and risk let (baselineResults, baselineRisk) = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1);

mutable optimalResults = baselineResults;
mutable optimalRisk = baselineRisk;

for (iteration in 1..iterations) {
    // Adjust probabilities for market and environmental factors each iteration
    let adjustedProbabilities = UpdateProbabilitiesWithRealTimeData(initialProbabilities, marketVolatility, environmentalData);
    let envAdjustedProbabilities = IncorporateEnvironmentalFactors(adjustedProbabilities, environmentalData);

    let (currentResults, currentRisk) = FeedbackBasedRefinement(baselineResults, baselineRisk, qubits, envAdjustedProbabilities, marketVolatility, 1);

    // Evaluate if current iteration provides better risk management
    if (currentRisk < optimalRisk) {
        set optimalRisk = currentRisk;
        set optimalResults = currentResults;
    }
}

Message($"Optimal decisions with environmental considerations: {optimalResults}");
Message($"Lowest achieved risk: {optimalRisk}");
}


}

// EntryPoint to initiate the environmentally adjusted decision feedback loop @EntryPoint() operation ExecuteEnvironmentallySensitiveOptimizations() : Unit { let initialProbabilities = [0.65, 0.60, 0.70, 0.75, 0.80]; let marketVolatility = [0.25, 0.20, 0.30, 0.35, 0.25]; let environmentalData = [0.95, 1.05, 1.00, 0.90, 0.85]; // Example environmental impacts let iterations = 15;

EnvironmentalFeedbackDecisionLoop(initialProbabilities, marketVolatility, environmentalData, iterations);



// Advanced function to dynamically adjust market volatility based on external economic indicators
function DynamicallyAdjustMarketVolatility(marketVolatility : Double[], economicIndicators : Double[]) : Double[] {
    return Zip(marketVolatility, economicIndicators, (volatility, indicator) -> volatility * indicator);
}

// Integrate advanced economic indicators into the decision-making process
operation IntegrateEconomicIndicators(initialProbabilities : Double[], marketVolatility : Double[], economicIndicators : Double[], environmentalData : Double[], iterations : Int) : Unit {
    using (qubits = Qubit[Length(initialProbabilities)]) {
        // Initial setup with environmental and economic adjustments
        let envAdjustedProbabilities = IncorporateEnvironmentalFactors(initialProbabilities, environmentalData);
        let econAdjustedVolatility = DynamicallyAdjustMarketVolatility(marketVolatility, economicIndicators);

        mutable optimalResults = new Result[Length(qubits)];
        mutable optimalRisk = 1.0; // Start with the highest possible risk

        for (iteration in 1..iterations) {
            let currentProbabilities = UpdateProbabilitiesWithRealTimeData(envAdjustedProbabilities, econAdjustedVolatility, environmentalData);
            let (currentResults, currentRisk) = ComprehensiveAdaptiveQuantumAnalysis(qubits, currentProbabilities, econAdjustedVolatility, 0.05, 1);

            // Check for optimal risk and results
            if (currentRisk < optimalRisk) {
                set optimalRisk = currentRisk;
                set optimalResults = currentResults;
            }

            // Provide feedback for each iteration
            Message($"Iteration {iteration}: Current Risk = {currentRisk}, Results = {currentResults}");
        }

        // Final output after iterations
        Message($"Final optimal decisions with economic indicators considered: {optimalResults}");
        Message($"Final optimal risk level: {optimalRisk}");
    }
}

// EntryPoint to execute the simulation with economic and environmental feedback
@EntryPoint()
operation ExecuteAdvancedEconomicOptimization() : Unit {
    let initialProbabilities = [0.70, 0.65, 0.60, 0.55, 0.50];
    let marketVolatility = [0.22, 0.28, 0.25, 0.21, 0.27];
    let economicIndicators = [1.02, 0.98, 1.05, 0.99, 1.03]; // Simulated economic factors
    let environmentalData = [0.88, 0.92, 0.91, 0.89, 0.90]; // Environmental impacts
    let iterations = 20;

    IntegrateEconomicIndicators(initialProbabilities, marketVolatility, economicIndicators, environmentalData, iterations);
}


// Optimize feedback mechanism to refine probability adjustments based on both risk and return
function RefineProbabilitiesBasedOnPerformance(results : Result[], risks : Double[], returns : Double[]) : Double[] {
    mutable refinedProbabilities = new Double[Length(results)];

    for (idx in 0..Length(results) - 1) {
        if (ResultAsInt(results[idx]) == 1) {
            // Increase investment probability if the result was positive and adjust based on risk/return ratio
            set refinedProbabilities w/= idx <- returns[idx] / risks[idx];
        } else {
            // Decrease probability to align with lower performance or higher risk
            set refinedProbabilities w/= idx <- returns[idx] * risks[idx];
        }
    }

    return NormalizeProbabilities(refinedProbabilities);
}

// Include an analysis phase to evaluate returns vs. risks before adjusting probabilities
operation AnalyzeAndAdjustInvestments(qubits : Qubit[], probabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit {
    mutable bestResults = new Result[Length(qubits)];
    mutable bestPerformance = 0.0;

    for (iteration in 1..iterations) {
        let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, probabilities, marketVolatility, 0.05, 1);
        let currentRisks = CalculateRiskFromResults(currentResults);
        let currentReturns = CalculateReturnsFromResults(currentResults); // Assume this function exists
        let performanceScore = Fold(PlusD, 0.0, Map(idx -> currentReturns[idx] / currentRisks[idx], Indices(currentReturns)));

        if (performanceScore > bestPerformance) {
            set bestPerformance = performanceScore;
            set bestResults = currentResults;
        }

        // Adjust probabilities based on new insights from the latest iteration
        set probabilities = RefineProbabilitiesBasedOnPerformance(currentResults, currentRisks, currentReturns);
    }

    Message($"Optimal decisions based on refined adjustments: {bestResults}");
    Message($"Best performance score: {bestPerformance}");
}

// EntryPoint to execute the refined adjustment process for quantum-based real estate investments
@EntryPoint()
operation ExecuteRefinedAdjustmentProcess() : Unit {
    let initialProbabilities = [0.68, 0.73, 0.77, 0.65, 0.70];
    let marketVolatility = [0.23, 0.19, 0.26, 0.22, 0.24];
    let iterations = 15;

    using (qubits = Qubit[Length(initialProbabilities)]) {
        AnalyzeAndAdjustInvestments(qubits, initialProbabilities, marketVolatility, iterations);
    }
}





// Function to simulate market fluctuations and adjust probabilities dynamically
function SimulateMarketFluctuations(currentProbabilities : Double[], fluctuationRate : Double) : Double[] {
    return Map(prob -> prob * (1.0 + (RandomReal() - 0.5) * fluctuationRate), currentProbabilities);
}

// Operation to perform a full market simulation including dynamic adjustments for market fluctuations
operation PerformFullMarketSimulation(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit {
    mutable probabilities = initialProbabilities;

    for (iteration in 1..iterations) {
        // Simulate market fluctuations at each iteration
        set probabilities = SimulateMarketFluctuations(probabilities, 0.1); // 10% fluctuation rate

        let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, probabilities, marketVolatility, 0.05, 1);
        Message($"Results for iteration {iteration}: {currentResults}");

        // Update probabilities based on performance analysis
        let currentRisks = CalculateRiskFromResults(currentResults);
        let currentReturns = CalculateReturnsFromResults(currentResults); // Assume function calculates returns
        set probabilities = RefineProbabilitiesBasedOnPerformance(currentResults, currentRisks, currentReturns);
    }

    Message($"Final probabilities after full market simulation: {probabilities}");
}

// EntryPoint to initiate a comprehensive simulation with fluctuation adjustments
@EntryPoint()
operation StartComprehensiveMarketSimulation() : Unit {
    let initialProbabilities = [0.60, 0.65, 0.70, 0.75, 0.80];
    let marketVolatility = [0.24, 0.22, 0.25, 0.23, 0.21];
    let iterations = 20;

    using (qubits = Qubit[Length(initialProbabilities)]) {
        PerformFullMarketSimulation(qubits, initialProbabilities, marketVolatility, iterations);
    }
}



// Function to simulate market fluctuations and adjust probabilities dynamically
function SimulateMarketFluctuations(currentProbabilities : Double[], fluctuationRate : Double) : Double[] {
    return Map(prob -> prob * (1.0 + (RandomReal() - 0.5) * fluctuationRate), currentProbabilities);
}

// Operation to perform a full market simulation including dynamic adjustments for market fluctuations
operation PerformFullMarketSimulation(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit {
    mutable probabilities = initialProbabilities;

    for (iteration in 1..iterations) {
        // Simulate market fluctuations at each iteration
        set probabilities = SimulateMarketFluctuations(probabilities, 0.1); // 10% fluctuation rate

        let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, probabilities, marketVolatility, 0.05, 1);
        Message($"Results for iteration {iteration}: {currentResults}");

        // Update probabilities based on performance analysis
        let currentRisks = CalculateRiskFromResults(currentResults);
        let currentReturns = CalculateReturnsFromResults(currentResults); // Assume function calculates returns
        set probabilities = RefineProbabilitiesBasedOnPerformance(currentResults, currentRisks, currentReturns);
    }

    Message($"Final probabilities after full market simulation: {probabilities}");
}

// EntryPoint to initiate a comprehensive simulation with fluctuation adjustments
@EntryPoint()
operation StartComprehensiveMarketSimulation() : Unit {
    let initialProbabilities = [0.60, 0.65, 0.70, 0.75, 0.80];
    let marketVolatility = [0.24, 0.22, 0.25, 0.23, 0.21];
    let iterations = 20;

    using (qubits = Qubit[Length(initialProbabilities)]) {
        PerformFullMarketSimulation(qubits, initialProbabilities, marketVolatility, iterations);
    }
}




// Implement additional analysis to evaluate the stability of investment decisions over time
function EvaluateDecisionStability(historicalResults : Result[][], iterations : Int) : Double {
    mutable stabilityScores = new Double[Length(historicalResults[0])];

    for (qubitIdx in 0..Length(historicalResults[0]) - 1) {
        let decisions = [ResultAsInt(historicalResults[iter][qubitIdx]) for iter in 0..iterations - 1];
        let mean = Average(decisions);
        let variance = Fold(PlusD, 0.0, [PowD(dec - mean, 2.0) for dec in decisions]) / (Length(decisions) - 1.0);
        set stabilityScores w/= qubitIdx <- 1.0 - Sqrt(variance); // Higher score indicates more stability
    }

    return Average(stabilityScores);
}

// Run simulations to assess and enhance decision-making stability
operation StabilityEnhancedQuantumSimulation(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit {
    mutable probabilities = initialProbabilities;
    mutable historicalResults = new Result[][iterations];

    for (iteration in 1..iterations) {
        let results = ComprehensiveAdaptiveQuantumAnalysis(qubits, probabilities, marketVolatility, 0.05, 1);
        set historicalResults w/= iteration - 1 <- results;
        set probabilities = LearnFromPastSimulations(historicalResults, probabilities); // Dynamic learning
    }

    let stabilityScore = EvaluateDecisionStability(historicalResults, iterations);
    Message($"Stability score for investment decisions over {iterations} iterations: {stabilityScore}");
}

// EntryPoint to execute stability-focused simulations
@EntryPoint()
operation ExecuteStabilityFocusedSimulation() : Unit {
    let initialProbabilities = [0.71, 0.67, 0.74, 0.70, 0.73];
    let marketVolatility = [0.23, 0.21, 0.24, 0.22, 0.25];
    let iterations = 30;

    using (qubits = Qubit[Length(initialProbabilities)]) {
        StabilityEnhancedQuantumSimulation(qubits, initialProbabilities, marketVolatility, iterations);
    }
}




// Enhance the economic shock adjustment by considering a time-decaying factor on the shock's impact
function ApplyTimeDecayingEconomicImpact(probabilities : Double[], economicShockData : Double[], iteration : Int) : Double[] {
    return Zip(probabilities, economicShockData, (prob, shock) -> {
        // Decrease the shock impact over time using an exponential decay model
        let decayFactor = Exp(-0.1 * IntAsDouble(iteration)); // Decay rate example
        return prob * (1.0 - decayFactor * shock / 100.0);
    });
}

// Run a refined simulation to model the decay of economic shocks over multiple iterations
operation SimulateEconomicShockDecay(qubits : Qubit[], initialProbabilities : Double[], economicShockData : Double[], iterations : Int) : Unit {
    mutable probabilities = initialProbabilities;

    for (iteration in 1..iterations) {
        // Apply the time-decaying impact of economic shocks
        let timeAdjustedProbabilities = ApplyTimeDecayingEconomicImpact(probabilities, economicShockData, iteration);
        let results = ComprehensiveAdaptiveQuantumAnalysis(qubits, timeAdjustedProbabilities, [0.05], 0.05, 1);

        // Log results to observe the change over time
        Message($"Iteration {iteration}: Adjusted Results = {results}");
        
        // Update probabilities based on simulation feedback, assuming continual learning
        set probabilities = LearnFromPastSimulations([results], probabilities);
    }
}

// EntryPoint to execute the time-decay simulation for economic shocks
@EntryPoint()
operation ExecuteTimeDecayEconomicShockSimulation() : Unit {
    let initialProbabilities = [0.62, 0.78, 0.83, 0.69, 0.72];
    let economicShockData = [3.5, 2.0, 4.0, 3.0, 2.5]; // Initial shock values in percentage
    let iterations = 25;

    using (qubits = Qubit[Length(initialProbabilities)]) {
        SimulateEconomicShockDecay(qubits, initialProbabilities, economicShockData, iterations);
    }
}

namespace QuantumRealEstate { open Microsoft.Quantum.Intrinsic; open Microsoft.Quantum.Measurement; open Microsoft.Quantum.Canon; open Microsoft.Quantum.Math; open Microsoft.Quantum.Convert; operation PrepareQuantumRegister(qubits : Qubit[]) : Unit { if (Length(qubits) == 0) { fail “Quantum register must contain at least one qubit.”; } ApplyToEach(H, qubits); } operation MeasureQuantumRegister(qubits : Qubit[]) : Result[] { if (Length(qubits) == 0) { fail “No qubits provided for measurement.”; } return ForEach(MResetZ, qubits); } operation OptimizeRealEstateInvestments(numProperties : Int, entangle : Bool) : Result[] { using (qubits = Qubit[numProperties]) { PrepareQuantumRegister(qubits); if (entangle) { for (i in 1..Length(qubits) – 1) { Controlled X([qubits[i – 1]], qubits[i]); } } let investmentDecisions = MeasureQuantumRegister(qubits); ResetAll(qubits); return investmentDecisions; } } @EntryPoint() operation RunOptimization() : Unit { let numProperties = 5; // This can be dynamically set based on external data. let currentMarketCondition = true; // Assume a condition where entanglement is needed. let results = OptimizeRealEstateInvestments(numProperties, currentMarketCondition); Message($”Investment decisions: {results}”); } operation TestOptimizationScenario() : Unit { let testResults = OptimizeRealEstateInvestments(5, true); if (Length(testResults) != 5) { fail “Test failed: Expected 5 results.”; } Message(“Test passed: Correct number of results obtained.”); } } // Function to prepare a quantum register in a superposition to represent investment decisions operation PrepareQuantumRegister(qubits : Qubit[]) : Unit { ApplyToEach(H, qubits); } // Function to measure the quantum register and interpret the results operation MeasureQuantumRegister(qubits : Qubit[]) : Result[] { return ForEach(MResetZ, qubits); } // Main operation to simulate optimal real estate investment decisions operation OptimizeRealEstateInvestments() : Result[] { // Initialize qubits for simulation using (qubits = Qubit[5]) { // Assume 5 properties for simplicity // Prepare qubits in a superposition state PrepareQuantumRegister(qubits); // Introduce entanglement between qubits to model correlation between properties for (i in 1..Length(qubits) – 1) { Controlled X([qubits[i-1]], qubits[i]); } // Measure qubits to collapse the state to classical information let investmentDecisions = MeasureQuantumRegister(qubits); // Reset and deallocate qubits ResetAll(qubits); return investmentDecisions; } } // Entry point for the simulation @EntryPoint() operation RunOptimization() : Unit { let results = OptimizeRealEstateInvestments(); Message($”Investment decisions: {results}”); } } // Function to prepare a quantum register in a superposition to represent various investment options operation PrepareQuantumRegister(qubits : Qubit[], probabilities : Double[]) : Unit { for (i in 0..Length(qubits) – 1) { let angle = ArcCos(Sqrt(1.0 – probabilities[i])); Ry(2.0 * angle, qubits[i]); } } // Entangle qubits to represent correlations between different properties operation EntangleQubits(qubits : Qubit[]) : Unit { for (i in 0..Length(qubits) – 2) { Controlled X([qubits[i]], qubits[i + 1]); } } // Function to measure the quantum register and interpret the results operation MeasureQuantumRegister(qubits : Qubit[]) : Result[] { return ForEach(MResetZ, qubits); } // Operation to simulate investment decisions based on quantum probabilities operation SimulateInvestmentDecisions(qubits : Qubit[], probabilities : Double[]) : Result[] { PrepareQuantumRegister(qubits, probabilities); EntangleQubits(qubits); return MeasureQuantumRegister(qubits); } // Main operation to optimize real estate investments operation OptimizeRealEstateInvestments(probabilities : Double[]) : Result[] { using (qubits = Qubit[Length(probabilities)]) { let decisions = SimulateInvestmentDecisions(qubits, probabilities); ResetAll(qubits); return decisions; } } // Entry point for the simulation @EntryPoint() operation RunOptimization() : Unit { // Example: Probabilities of choosing to invest in each property (e.g., based on expected returns) let investmentProbabilities = [0.8, 0.6, 0.7, 0.5, 0.9]; let results = OptimizeRealEstateInvestments(investmentProbabilities); Message($”Investment decisions: {results}”); } } operation PrepareStateWithRotation(qubits : Qubit[], angles : Double[]) : Unit { for (idx in 0..Length(qubits) – 1) { Ry(2.0 * ArcSin(Sqrt(angles[idx])), qubits[idx]); } } operation EntangleQubitsInChain(qubits : Qubit[]) : Unit { for (idx in 0..Length(qubits) – 2) { CNOT(qubits[idx], qubits[idx + 1]); } } operation MeasureQubits(qubits : Qubit[]) : Result[] { mutable results = new Result[Length(qubits)]; for (idx in 0..Length(qubits) – 1) { set results w/= idx <- MResetZ(qubits[idx]); } return results; } operation PerformQuantumDecisionAnalysis(qubits : Qubit[], probabilities : Double[]) : Result[] { let angles = Map(Sqrt, probabilities); // Assuming probabilities are provided in [0, 1] and need square root for angle calculation PrepareStateWithRotation(qubits, angles); EntangleQubitsInChain(qubits); return MeasureQubits(qubits); } operation RunRealEstateQuantumAnalysis(probabilities : Double[]) : Result[] { using (qubits = Qubit[Length(probabilities)]) { return PerformQuantumDecisionAnalysis(qubits, probabilities); } } @EntryPoint() operation ExecuteRealEstateOptimization() : Unit { let investmentProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6]; let results = RunRealEstateQuantumAnalysis(investmentProbabilities); Message($"Investment decision outcomes: {results}"); } } operation PrepareStateWithRotation(qubits : Qubit[], angles : Double[]) : Unit { for (idx in 0..Length(qubits) - 1) { Ry(2.0 * ArcSin(Sqrt(angles[idx])), qubits[idx]); } } operation EntangleQubitsInChain(qubits : Qubit[]) : Unit { for (idx in 0..Length(qubits) - 2) { CNOT(qubits[idx], qubits[idx + 1]); } } operation MeasureQubits(qubits : Qubit[]) : Result[] { mutable results = new Result[Length(qubits)]; for (idx in 0..Length(qubits) - 1) { set results w/= idx <- MResetZ(qubits[idx]); } return results; } operation PerformQuantumDecisionAnalysis(qubits : Qubit[], probabilities : Double[]) : Result[] { let angles = Map(Sqrt, probabilities); // Adjust the calculation to use square roots for angle preparation PrepareStateWithRotation(qubits, angles); EntangleQubitsInChain(qubits); return MeasureQubits(qubits); } operation RunRealEstateQuantumAnalysis(probabilities : Double[]) : Result[] { using (qubits = Qubit[Length(probabilities)]) { return PerformQuantumDecisionAnalysis(qubits, probabilities); } } @EntryPoint() operation ExecuteRealEstateOptimization() : Unit { let investmentProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6]; let results = RunRealEstateQuantumAnalysis(investmentProbabilities); Message($"Investment decision outcomes: {results}"); } } // Adjust probability inputs for more accurate state preparation function AdjustProbabilities(probabilities : Double[]) : Double[] { return Map(ArcSin, Map(Sqrt, probabilities)); } // Enhance the entanglement process to represent different correlation strategies operation EntangleQubitsAdvanced(qubits : Qubit[], pattern : Int[]) : Unit { for (idx in 0..Length(pattern) - 2) { if (pattern[idx] == 1) { CNOT(qubits[idx], qubits[idx + 1]); } else { // Implement alternative entanglement strategy as needed CZ(qubits[idx], qubits[idx + 1]); } } } // Create a more dynamic investment analysis tool with parameterized entanglement operation PerformDynamicQuantumDecisionAnalysis(qubits : Qubit[], probabilities : Double[], pattern : Int[]) : Result[] { let adjustedProbabilities = AdjustProbabilities(probabilities); PrepareStateWithRotation(qubits, adjustedProbabilities); EntangleQubitsAdvanced(qubits, pattern); return MeasureQubits(qubits); } // Run the quantum analysis with a customizable entanglement pattern operation RunCustomizableRealEstateQuantumAnalysis(probabilities : Double[], pattern : Int[]) : Result[] { using (qubits = Qubit[Length(probabilities)]) { return PerformDynamicQuantumDecisionAnalysis(qubits, probabilities, pattern); } } // Entry point to execute with different entanglement strategies @EntryPoint() operation ExecuteFlexibleRealEstateOptimization() : Unit { let investmentProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6]; let entanglementPattern = [1, 0, 1, 0]; // Define a pattern of entanglement let results = RunCustomizableRealEstateQuantumAnalysis(investmentProbabilities, entanglementPattern); Message($"Flexible investment decision outcomes: {results}"); } } // Implement a function to calculate entanglement angles based on market volatility function CalculateEntanglementAngles(marketVolatility : Double[]) : Double[] { return Map(x -> PI() * x, marketVolatility); } // Dynamic entanglement based on market conditions operation EntangleQubitsBasedOnMarket(qubits : Qubit[], marketVolatility : Double[]) : Unit { let angles = CalculateEntanglementAngles(marketVolatility); for (idx in 0..Length(qubits) – 2) { let angle = angles[idx]; RY(angle, qubits[idx + 1]); CNOT(qubits[idx], qubits[idx + 1]); RY(-angle, qubits[idx + 1]); } } // Comprehensive quantum decision-making analysis considering market volatility operation ComprehensiveQuantumDecisionAnalysis(qubits : Qubit[], probabilities : Double[], marketVolatility : Double[]) : Result[] { let adjustedProbabilities = AdjustProbabilities(probabilities); PrepareStateWithRotation(qubits, adjustedProbabilities); EntangleQubitsBasedOnMarket(qubits, marketVolatility); return MeasureQubits(qubits); } // Run the analysis with market volatility adjustments operation RunMarketAdjustedQuantumAnalysis(probabilities : Double[], marketVolatility : Double[]) : Result[] { using (qubits = Qubit[Length(probabilities)]) { return ComprehensiveQuantumDecisionAnalysis(qubits, probabilities, marketVolatility); } } // Entry point for executing the quantum optimization with market considerations @EntryPoint() operation ExecuteMarketAdjustedOptimization() : Unit { let investmentProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6]; let marketVolatility = [0.2, 0.3, 0.25, 0.15, 0.1]; // Hypothetical market volatility ratings let results = RunMarketAdjustedQuantumAnalysis(investmentProbabilities, marketVolatility); Message($”Market-adjusted investment decision outcomes: {results}”); } } // Introduce feedback mechanism to update probabilities based on previous outcomes function UpdateInvestmentProbabilities(previousResults : Result[], currentProbabilities : Double[]) : Double[] { mutable newProbabilities = currentProbabilities; for (idx in 0..Length(previousResults) – 1) { if (ResultAsInt(previousResults[idx]) == 1) { set newProbabilities w/= idx <- newProbabilities[idx] * 1.1; // Increase probability if the previous decision was to invest } else { set newProbabilities w/= idx <- newProbabilities[idx] * 0.9; // Decrease otherwise } } return newProbabilities; } // Re-run the quantum decision analysis with updated probabilities operation AdaptiveQuantumDecisionAnalysis(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Result[] { mutable probabilities = initialProbabilities; mutable results = new Result[0]; for (iteration in 1..iterations) { let currentResults = ComprehensiveQuantumDecisionAnalysis(qubits, probabilities, marketVolatility); set probabilities = UpdateInvestmentProbabilities(currentResults, probabilities); set results = currentResults; } return results; } // Entry point to execute the adaptive optimization @EntryPoint() operation ExecuteAdaptiveOptimization() : Unit { let initialProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6]; let marketVolatility = [0.2, 0.3, 0.25, 0.15, 0.1]; // Market volatility ratings let iterations = 5; // Number of iterations to adaptively adjust the decision making using (qubits = Qubit[Length(initialProbabilities)]) { let finalResults = AdaptiveQuantumDecisionAnalysis(qubits, initialProbabilities, marketVolatility, iterations); Message($"Adaptive investment decision outcomes: {finalResults}"); } } } // Additional function to normalize probabilities ensuring they remain valid between 0 and 1 function NormalizeProbabilities(probabilities : Double[]) : Double[] { let total = Fold(PlusD, 0.0, probabilities); return Map(prob => prob / total, probabilities); } // Enhanced version of the update function to handle normalization function EnhancedUpdateInvestmentProbabilities(previousResults : Result[], currentProbabilities : Double[]) : Double[] { mutable newProbabilities = currentProbabilities; for (idx in 0..Length(previousResults) – 1) { if (ResultAsInt(previousResults[idx]) == 1) { set newProbabilities w/= idx <- newProbabilities[idx] * 1.1; // Boost probability after positive outcomes } else { set newProbabilities w/= idx <- newProbabilities[idx] * 0.9; // Reduce after non-investment outcomes } } return NormalizeProbabilities(newProbabilities); // Normalize to keep probabilities valid } // Introducing quantum fluctuation to simulate real-world uncertainty operation ApplyQuantumFluctuations(qubits : Qubit[], fluctuationRate : Double) : Unit { for (qubit in qubits) { let angle = 2.0 * PI() * fluctuationRate; R1(angle, qubit); } } // Comprehensive adaptive analysis with fluctuation consideration operation ComprehensiveAdaptiveQuantumAnalysis(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], fluctuationRate : Double, iterations : Int) : Result[] { mutable probabilities = initialProbabilities; mutable results = new Result[0]; for (iteration in 1..iterations) { ApplyQuantumFluctuations(qubits, fluctuationRate); let currentResults = ComprehensiveQuantumDecisionAnalysis(qubits, probabilities, marketVolatility); set probabilities = EnhancedUpdateInvestmentProbabilities(currentResults, probabilities); set results = currentResults; } return results; } // Entry point to execute the adaptive optimization with quantum fluctuations @EntryPoint() operation ExecuteComprehensiveAdaptiveOptimization() : Unit { let initialProbabilities = [0.8, 0.65, 0.55, 0.75, 0.6]; let marketVolatility = [0.2, 0.3, 0.25, 0.15, 0.1]; let fluctuationRate = 0.05; // Quantum fluctuation rate let iterations = 10; // Number of iterations for adaptive adjustments using (qubits = Qubit[Length(initialProbabilities)]) { let finalResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, fluctuationRate, iterations); Message($"Final adaptive investment decision outcomes: {finalResults}"); } } } // Operation to analyze the coherence time of qubits under different market conditions operation AnalyzeQubitCoherence(qubits : Qubit[], coherenceThresholds : Double[]) : Result[] { mutable coherenceResults = new Result[Length(qubits)]; for (idx in 0..Length(qubits) - 1) { // Apply a phase rotation to simulate environmental interaction R1(coherenceThresholds[idx] * PI(), qubits[idx]); // Measure the qubit to determine if it has maintained its coherence set coherenceResults w/= idx <- MResetZ(qubits[idx]); } return coherenceResults; } // Function to dynamically adjust coherence thresholds based on market data function AdjustCoherenceThresholds(marketData : Double[]) : Double[] { return Map(x -> x * 0.01, marketData); // Example: scale market data to represent coherence thresholds } // Operation to execute a full market simulation including coherence analysis operation RunFullMarketSimulation(initialProbabilities : Double[], marketVolatility : Double[], marketData : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { let coherenceThresholds = AdjustCoherenceThresholds(marketData); for (iteration in 1..iterations) { let simulationResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); // Run analysis for one cycle let coherenceResults = AnalyzeQubitCoherence(qubits, coherenceThresholds); Message($”Simulation results for iteration {iteration}: {simulationResults}”); Message($”Coherence analysis results for iteration {iteration}: {coherenceResults}”); } } } // EntryPoint to start the full simulation @EntryPoint() operation StartFullSimulation() : Unit { let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60]; let marketVolatility = [0.20, 0.30, 0.25, 0.15, 0.10]; let marketData = [500.0, 600.0, 550.0, 580.0, 610.0]; // Hypothetical market data points let iterations = 5; // Number of full simulations RunFullMarketSimulation(initialProbabilities, marketVolatility, marketData, iterations); } } // Enhance the coherence analysis to include feedback adjustment based on results operation AdjustQubitCoherenceBasedOnFeedback(qubits : Qubit[], coherenceResults : Result[], adjustmentFactors : Double[]) : Unit { for (idx in 0..Length(qubits) – 1) { let adjustment = adjustmentFactors[idx]; if (ResultAsInt(coherenceResults[idx]) == 1) { // If the qubit maintained coherence, slightly decrease the adjustment factor set adjustmentFactors w/= idx <- adjustment * 0.95; } else { // If the qubit lost coherence, increase the adjustment factor set adjustmentFactors w/= idx <- adjustment * 1.05; } // Apply the adjusted phase rotation R1(adjustment * PI(), qubits[idx]); } } // Operation to run enhanced simulations incorporating dynamic coherence adjustments operation RunEnhancedMarketSimulations(initialProbabilities : Double[], marketVolatility : Double[], marketData : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { let coherenceThresholds = AdjustCoherenceThresholds(marketData); mutable adjustmentFactors = coherenceThresholds; // Initialize adjustments based on initial thresholds for (iteration in 1..iterations) { let simulationResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); // Single cycle analysis let coherenceResults = AnalyzeQubitCoherence(qubits, coherenceThresholds); AdjustQubitCoherenceBasedOnFeedback(qubits, coherenceResults, adjustmentFactors); Message($"Enhanced simulation results for iteration {iteration}: {simulationResults}"); Message($"Coherence adjustment factors for iteration {iteration}: {adjustmentFactors}"); } } } // EntryPoint to start the enhanced simulations with dynamic coherence adjustments @EntryPoint() operation StartEnhancedSimulations() : Unit { let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60]; let marketVolatility = [0.20, 0.30, 0.25, 0.15, 0.10]; let marketData = [500.0, 600.0, 550.0, 580.0, 610.0]; // Example market data points let iterations = 10; // Number of enhanced simulations RunEnhancedMarketSimulations(initialProbabilities, marketVolatility, marketData, iterations); } } // Enhance the adjustment mechanism to consider historical coherence data function UpdateAdjustmentFactorsBasedOnHistory(adjustmentFactors : Double[], historicalCoherence : Result[][], iteration : Int) : Double[] { mutable updatedFactors = adjustmentFactors; // Adjust based on historical results to find a trend in coherence maintenance for (idx in 0..Length(adjustmentFactors) - 1) { let historicalResults = [historicalCoherence[j][idx] for j in 0..iteration - 1]; let successes = Fold(PlusI, 0, [ResultAsInt(result) for result in historicalResults]); // Apply a stronger adjustment if coherence was frequently lost if (2 * successes < iteration) { // Less than 50% success rate set updatedFactors w/= idx <- updatedFactors[idx] * 1.10; } else { set updatedFactors w/= idx <- updatedFactors[idx] * 0.90; } } return updatedFactors; } // Implement a feedback loop to adaptively adjust the coherence strategies over multiple iterations operation RunAdaptiveCoherenceSimulations(initialProbabilities : Double[], marketVolatility : Double[], marketData : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { let initialCoherenceThresholds = AdjustCoherenceThresholds(marketData); mutable adjustmentFactors = initialCoherenceThresholds; mutable historicalCoherence = new Result[][iterations]; for (iteration in 1..iterations) { let simulationResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); let coherenceResults = AnalyzeQubitCoherence(qubits, Map(idx -> adjustmentFactors[idx] * PI(), initialCoherenceThresholds)); set historicalCoherence w/= iteration – 1 <- coherenceResults; set adjustmentFactors = UpdateAdjustmentFactorsBasedOnHistory(adjustmentFactors, historicalCoherence, iteration); Message($"Iteration {iteration}: Simulation results - {simulationResults}, Coherence results - {coherenceResults}"); } } } // EntryPoint to execute the adaptive coherence simulations @EntryPoint() operation ExecuteAdaptiveCoherenceSimulations() : Unit { let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60]; let marketVolatility = [0.20, 0.30, 0.25, 0.15, 0.10]; let marketData = [500.0, 600.0, 550.0, 580.0, 610.0]; let iterations = 20; RunAdaptiveCoherenceSimulations(initialProbabilities, marketVolatility, marketData, iterations); } } // Include a module for visualizing the results of simulations open Microsoft.Quantum.Diagnostics; // Function to convert results into percentage of coherence function CoherencePercentage(historicalCoherence : Result[][], iterations : Int) : Double[] { let totalQubits = Length(historicalCoherence[0]); mutable percentages = new Double[totalQubits]; for (qubitIdx in 0..totalQubits - 1) { let coherenceCount = Fold(PlusI, 0, [ResultAsInt(historicalCoherence[iter][qubitIdx]) for iter in 0..iterations - 1]); let percentage = (100.0 * IntAsDouble(coherenceCount)) / IntAsDouble(iterations); set percentages w/= qubitIdx <- percentage; } return percentages; } // Visualize coherence over multiple iterations to monitor stability and performance operation VisualizeCoherenceTrends(historicalCoherence : Result[][], iterations : Int) : Unit { let coherencePercentages = CoherencePercentage(historicalCoherence, iterations); Message("Coherence percentages over iterations:"); for (idx in 0..Length(coherencePercentages) - 1) { Message($"Qubit {idx}: {coherencePercentages[idx]}%"); } } // Enhance decision making with a Quantum Annealer approach for solving complex optimization problems operation QuantumAnnealerOptimization(initialProbabilities : Double[], iterations : Int) : Result[] { using (qubits = Qubit[Length(initialProbabilities)]) { mutable bestResult = new Result[Length(qubits)]; mutable bestScore = 0.0; for (iter in 1..iterations) { let results = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, [0.05], 0.05, 1); let currentScore = Fold(PlusD, 0.0, [ResultAsDouble(result) for result in results]); if (currentScore > bestScore) { set bestScore = currentScore; set bestResult = results; } } return bestResult; } } // EntryPoint for a comprehensive quantum real estate optimization including annealing simulation @EntryPoint() operation ExecuteQuantumAnnealingOptimization() : Unit { let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60]; let iterations = 100; let optimalResults = QuantumAnnealerOptimization(initialProbabilities, iterations); Message($”Optimal investment decisions after quantum annealing: {optimalResults}”); } // Integrate environmental factors into quantum analysis for real-time adjustments function AdjustProbabilitiesForEnvironmentalFactors(probabilities : Double[], environmentalFactors : Double[]) : Double[] { return Zip(probabilities, environmentalFactors, (prob, env) -> prob * env); } // Update probabilities based on environmental and market conditions operation UpdateProbabilitiesWithRealTimeData(initialProbabilities : Double[], marketData : Double[], environmentalData : Double[]) : Double[] { let marketAdjustedProbabilities = AdjustCoherenceThresholds(marketData); let environmentAdjustedProbabilities = AdjustProbabilitiesForEnvironmentalFactors(marketAdjustedProbabilities, environmentalData); return NormalizeProbabilities(environmentAdjustedProbabilities); } // Run a simulation that adapts to changing real estate market and environmental conditions operation RunDynamicSimulation(initialProbabilities : Double[], marketVolatility : Double[], marketData : Double[], environmentalData : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { mutable dynamicProbabilities = initialProbabilities; for (iteration in 1..iterations) { let updatedProbabilities = UpdateProbabilitiesWithRealTimeData(dynamicProbabilities, marketData, environmentalData); let simulationResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, updatedProbabilities, marketVolatility, 0.05, 1); Message($”Dynamic simulation results for iteration {iteration}: {simulationResults}”); // Update probabilities based on the latest simulation results and real-time data set dynamicProbabilities = updatedProbabilities; } } } // EntryPoint to execute dynamic simulations accounting for market and environmental changes @EntryPoint() operation ExecuteDynamicSimulations() : Unit { let initialProbabilities = [0.80, 0.65, 0.55, 0.75, 0.60]; let marketVolatility = [0.20, 0.30, 0.25, 0.15, 0.10]; let marketData = [500.0, 600.0, 550.0, 580.0, 610.0]; // Simulated market data points let environmentalData = [1.0, 0.8, 0.9, 1.1, 0.85]; // Environmental impact factors let iterations = 15; RunDynamicSimulation(initialProbabilities, marketVolatility, marketData, environmentalData, iterations); } or (iteration in 1..iterations) { let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); let currentRisk = CalculateRiskFromResults(currentResults); if (currentRisk < bestRisk) { set bestRisk = currentRisk; set bestDecisions = currentResults; } Message($"Iteration {iteration}: Current Risk = {currentRisk}"); } Message($"Best investment decisions with lowest risk: {bestDecisions}"); } } // EntryPoint to run the risk-adjusted simulation for real estate investment @EntryPoint() operation ExecuteRiskAdjustedSimulations() : Unit { let initialProbabilities = [0.70, 0.60, 0.65, 0.80, 0.75]; let marketVolatility = [0.25, 0.35, 0.30, 0.20, 0.25]; let iterations = 20; RiskAdjustedInvestmentDecisions(initialProbabilities, marketVolatility, iterations); // Introduce a feedback mechanism to refine investment strategies over time operation FeedbackBasedRefinement(initialResults : Result[], initialRisk : Double, qubits : Qubit[], probabilities : Double[], marketVolatility : Double[], iterations : Int) : (Result[], Double) { mutable refinedResults = initialResults; mutable refinedRisk = initialRisk; for (iteration in 1..iterations) { let updatedProbabilities = UpdateProbabilitiesWithRealTimeData(probabilities, Map(x -> x + 0.01 * iteration, marketVolatility), probabilities); // Slightly adjust market data let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, updatedProbabilities, marketVolatility, 0.05, 1); let currentRisk = CalculateRiskFromResults(currentResults); // Check if the new strategy offers a lower risk if (currentRisk < refinedRisk) { set refinedRisk = currentRisk; set refinedResults = currentResults; } } return (refinedResults, refinedRisk); } // Implement a decision loop that adapts based on feedback over multiple market scenarios operation DecisionFeedbackLoop(initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { let (initialResults, initialRisk) = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); let (optimalResults, optimalRisk) = FeedbackBasedRefinement(initialResults, initialRisk, qubits, initialProbabilities, marketVolatility, iterations); Message($"Optimal risk-adjusted decisions after feedback loop: {optimalResults}"); Message($"Achieved risk level: {optimalRisk}"); } } // EntryPoint to execute the entire decision process including dynamic feedback @EntryPoint() operation StartDecisionFeedbackProcess() : Unit { let initialProbabilities = [0.75, 0.70, 0.65, 0.60, 0.55]; let marketVolatility = [0.20, 0.22, 0.25, 0.28, 0.30]; let iterations = 10; DecisionFeedbackLoop(initialProbabilities, marketVolatility, iterations); // Enhance the feedback loop with environmental considerations function IncorporateEnvironmentalFactors(probabilities : Double[], environmentalImpact : Double[]) : Double[] { return Zip(probabilities, environmentalImpact, (prob, impact) -> prob * impact); } // Modify the decision process to include environmental data in the feedback loop operation EnvironmentalFeedbackDecisionLoop(initialProbabilities : Double[], marketVolatility : Double[], environmentalData : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { // Initial comprehensive analysis to set baseline results and risk let (baselineResults, baselineRisk) = ComprehensiveAdaptiveQuantumAnalysis(qubits, initialProbabilities, marketVolatility, 0.05, 1); mutable optimalResults = baselineResults; mutable optimalRisk = baselineRisk; for (iteration in 1..iterations) { // Adjust probabilities for market and environmental factors each iteration let adjustedProbabilities = UpdateProbabilitiesWithRealTimeData(initialProbabilities, marketVolatility, environmentalData); let envAdjustedProbabilities = IncorporateEnvironmentalFactors(adjustedProbabilities, environmentalData); let (currentResults, currentRisk) = FeedbackBasedRefinement(baselineResults, baselineRisk, qubits, envAdjustedProbabilities, marketVolatility, 1); // Evaluate if current iteration provides better risk management if (currentRisk < optimalRisk) { set optimalRisk = currentRisk; set optimalResults = currentResults; } } Message($"Optimal decisions with environmental considerations: {optimalResults}"); Message($"Lowest achieved risk: {optimalRisk}"); } } // EntryPoint to initiate the environmentally adjusted decision feedback loop @EntryPoint() operation ExecuteEnvironmentallySensitiveOptimizations() : Unit { let initialProbabilities = [0.65, 0.60, 0.70, 0.75, 0.80]; let marketVolatility = [0.25, 0.20, 0.30, 0.35, 0.25]; let environmentalData = [0.95, 1.05, 1.00, 0.90, 0.85]; // Example environmental impacts let iterations = 15; EnvironmentalFeedbackDecisionLoop(initialProbabilities, marketVolatility, environmentalData, iterations); // Advanced function to dynamically adjust market volatility based on external economic indicators function DynamicallyAdjustMarketVolatility(marketVolatility : Double[], economicIndicators : Double[]) : Double[] { return Zip(marketVolatility, economicIndicators, (volatility, indicator) -> volatility * indicator); } // Integrate advanced economic indicators into the decision-making process operation IntegrateEconomicIndicators(initialProbabilities : Double[], marketVolatility : Double[], economicIndicators : Double[], environmentalData : Double[], iterations : Int) : Unit { using (qubits = Qubit[Length(initialProbabilities)]) { // Initial setup with environmental and economic adjustments let envAdjustedProbabilities = IncorporateEnvironmentalFactors(initialProbabilities, environmentalData); let econAdjustedVolatility = DynamicallyAdjustMarketVolatility(marketVolatility, economicIndicators); mutable optimalResults = new Result[Length(qubits)]; mutable optimalRisk = 1.0; // Start with the highest possible risk for (iteration in 1..iterations) { let currentProbabilities = UpdateProbabilitiesWithRealTimeData(envAdjustedProbabilities, econAdjustedVolatility, environmentalData); let (currentResults, currentRisk) = ComprehensiveAdaptiveQuantumAnalysis(qubits, currentProbabilities, econAdjustedVolatility, 0.05, 1); // Check for optimal risk and results if (currentRisk < optimalRisk) { set optimalRisk = currentRisk; set optimalResults = currentResults; } // Provide feedback for each iteration Message($"Iteration {iteration}: Current Risk = {currentRisk}, Results = {currentResults}"); } // Final output after iterations Message($"Final optimal decisions with economic indicators considered: {optimalResults}"); Message($"Final optimal risk level: {optimalRisk}"); } } // EntryPoint to execute the simulation with economic and environmental feedback @EntryPoint() operation ExecuteAdvancedEconomicOptimization() : Unit { let initialProbabilities = [0.70, 0.65, 0.60, 0.55, 0.50]; let marketVolatility = [0.22, 0.28, 0.25, 0.21, 0.27]; let economicIndicators = [1.02, 0.98, 1.05, 0.99, 1.03]; // Simulated economic factors let environmentalData = [0.88, 0.92, 0.91, 0.89, 0.90]; // Environmental impacts let iterations = 20; IntegrateEconomicIndicators(initialProbabilities, marketVolatility, economicIndicators, environmentalData, iterations); } // Optimize feedback mechanism to refine probability adjustments based on both risk and return function RefineProbabilitiesBasedOnPerformance(results : Result[], risks : Double[], returns : Double[]) : Double[] { mutable refinedProbabilities = new Double[Length(results)]; for (idx in 0..Length(results) - 1) { if (ResultAsInt(results[idx]) == 1) { // Increase investment probability if the result was positive and adjust based on risk/return ratio set refinedProbabilities w/= idx <- returns[idx] / risks[idx]; } else { // Decrease probability to align with lower performance or higher risk set refinedProbabilities w/= idx <- returns[idx] * risks[idx]; } } return NormalizeProbabilities(refinedProbabilities); } // Include an analysis phase to evaluate returns vs. risks before adjusting probabilities operation AnalyzeAndAdjustInvestments(qubits : Qubit[], probabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit { mutable bestResults = new Result[Length(qubits)]; mutable bestPerformance = 0.0; for (iteration in 1..iterations) { let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, probabilities, marketVolatility, 0.05, 1); let currentRisks = CalculateRiskFromResults(currentResults); let currentReturns = CalculateReturnsFromResults(currentResults); // Assume this function exists let performanceScore = Fold(PlusD, 0.0, Map(idx -> currentReturns[idx] / currentRisks[idx], Indices(currentReturns))); if (performanceScore > bestPerformance) { set bestPerformance = performanceScore; set bestResults = currentResults; } // Adjust probabilities based on new insights from the latest iteration set probabilities = RefineProbabilitiesBasedOnPerformance(currentResults, currentRisks, currentReturns); } Message($”Optimal decisions based on refined adjustments: {bestResults}”); Message($”Best performance score: {bestPerformance}”); } // EntryPoint to execute the refined adjustment process for quantum-based real estate investments @EntryPoint() operation ExecuteRefinedAdjustmentProcess() : Unit { let initialProbabilities = [0.68, 0.73, 0.77, 0.65, 0.70]; let marketVolatility = [0.23, 0.19, 0.26, 0.22, 0.24]; let iterations = 15; using (qubits = Qubit[Length(initialProbabilities)]) { AnalyzeAndAdjustInvestments(qubits, initialProbabilities, marketVolatility, iterations); } } // Function to simulate market fluctuations and adjust probabilities dynamically function SimulateMarketFluctuations(currentProbabilities : Double[], fluctuationRate : Double) : Double[] { return Map(prob -> prob * (1.0 + (RandomReal() – 0.5) * fluctuationRate), currentProbabilities); } // Operation to perform a full market simulation including dynamic adjustments for market fluctuations operation PerformFullMarketSimulation(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit { mutable probabilities = initialProbabilities; for (iteration in 1..iterations) { // Simulate market fluctuations at each iteration set probabilities = SimulateMarketFluctuations(probabilities, 0.1); // 10% fluctuation rate let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, probabilities, marketVolatility, 0.05, 1); Message($”Results for iteration {iteration}: {currentResults}”); // Update probabilities based on performance analysis let currentRisks = CalculateRiskFromResults(currentResults); let currentReturns = CalculateReturnsFromResults(currentResults); // Assume function calculates returns set probabilities = RefineProbabilitiesBasedOnPerformance(currentResults, currentRisks, currentReturns); } Message($”Final probabilities after full market simulation: {probabilities}”); } // EntryPoint to initiate a comprehensive simulation with fluctuation adjustments @EntryPoint() operation StartComprehensiveMarketSimulation() : Unit { let initialProbabilities = [0.60, 0.65, 0.70, 0.75, 0.80]; let marketVolatility = [0.24, 0.22, 0.25, 0.23, 0.21]; let iterations = 20; using (qubits = Qubit[Length(initialProbabilities)]) { PerformFullMarketSimulation(qubits, initialProbabilities, marketVolatility, iterations); } } // Function to simulate market fluctuations and adjust probabilities dynamically function SimulateMarketFluctuations(currentProbabilities : Double[], fluctuationRate : Double) : Double[] { return Map(prob -> prob * (1.0 + (RandomReal() – 0.5) * fluctuationRate), currentProbabilities); } // Operation to perform a full market simulation including dynamic adjustments for market fluctuations operation PerformFullMarketSimulation(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit { mutable probabilities = initialProbabilities; for (iteration in 1..iterations) { // Simulate market fluctuations at each iteration set probabilities = SimulateMarketFluctuations(probabilities, 0.1); // 10% fluctuation rate let currentResults = ComprehensiveAdaptiveQuantumAnalysis(qubits, probabilities, marketVolatility, 0.05, 1); Message($”Results for iteration {iteration}: {currentResults}”); // Update probabilities based on performance analysis let currentRisks = CalculateRiskFromResults(currentResults); let currentReturns = CalculateReturnsFromResults(currentResults); // Assume function calculates returns set probabilities = RefineProbabilitiesBasedOnPerformance(currentResults, currentRisks, currentReturns); } Message($”Final probabilities after full market simulation: {probabilities}”); } // EntryPoint to initiate a comprehensive simulation with fluctuation adjustments @EntryPoint() operation StartComprehensiveMarketSimulation() : Unit { let initialProbabilities = [0.60, 0.65, 0.70, 0.75, 0.80]; let marketVolatility = [0.24, 0.22, 0.25, 0.23, 0.21]; let iterations = 20; using (qubits = Qubit[Length(initialProbabilities)]) { PerformFullMarketSimulation(qubits, initialProbabilities, marketVolatility, iterations); } } // Implement additional analysis to evaluate the stability of investment decisions over time function EvaluateDecisionStability(historicalResults : Result[][], iterations : Int) : Double { mutable stabilityScores = new Double[Length(historicalResults[0])]; for (qubitIdx in 0..Length(historicalResults[0]) – 1) { let decisions = [ResultAsInt(historicalResults[iter][qubitIdx]) for iter in 0..iterations – 1]; let mean = Average(decisions); let variance = Fold(PlusD, 0.0, [PowD(dec – mean, 2.0) for dec in decisions]) / (Length(decisions) – 1.0); set stabilityScores w/= qubitIdx <- 1.0 - Sqrt(variance); // Higher score indicates more stability } return Average(stabilityScores); } // Run simulations to assess and enhance decision-making stability operation StabilityEnhancedQuantumSimulation(qubits : Qubit[], initialProbabilities : Double[], marketVolatility : Double[], iterations : Int) : Unit { mutable probabilities = initialProbabilities; mutable historicalResults = new Result[][iterations]; for (iteration in 1..iterations) { let results = ComprehensiveAdaptiveQuantumAnalysis(qubits, probabilities, marketVolatility, 0.05, 1); set historicalResults w/= iteration - 1 <- results; set probabilities = LearnFromPastSimulations(historicalResults, probabilities); // Dynamic learning } let stabilityScore = EvaluateDecisionStability(historicalResults, iterations); Message($"Stability score for investment decisions over {iterations} iterations: {stabilityScore}"); } // EntryPoint to execute stability-focused simulations @EntryPoint() operation ExecuteStabilityFocusedSimulation() : Unit { let initialProbabilities = [0.71, 0.67, 0.74, 0.70, 0.73]; let marketVolatility = [0.23, 0.21, 0.24, 0.22, 0.25]; let iterations = 30; using (qubits = Qubit[Length(initialProbabilities)]) { StabilityEnhancedQuantumSimulation(qubits, initialProbabilities, marketVolatility, iterations); } } // Enhance the economic shock adjustment by considering a time-decaying factor on the shock's impact function ApplyTimeDecayingEconomicImpact(probabilities : Double[], economicShockData : Double[], iteration : Int) : Double[] { return Zip(probabilities, economicShockData, (prob, shock) -> { // Decrease the shock impact over time using an exponential decay model let decayFactor = Exp(-0.1 * IntAsDouble(iteration)); // Decay rate example return prob * (1.0 – decayFactor * shock / 100.0); }); } // Run a refined simulation to model the decay of economic shocks over multiple iterations operation SimulateEconomicShockDecay(qubits : Qubit[], initialProbabilities : Double[], economicShockData : Double[], iterations : Int) : Unit { mutable probabilities = initialProbabilities; for (iteration in 1..iterations) { // Apply the time-decaying impact of economic shocks let timeAdjustedProbabilities = ApplyTimeDecayingEconomicImpact(probabilities, economicShockData, iteration); let results = ComprehensiveAdaptiveQuantumAnalysis(qubits, timeAdjustedProbabilities, [0.05], 0.05, 1); // Log results to observe the change over time Message($”Iteration {iteration}: Adjusted Results = {results}”); // Update probabilities based on simulation feedback, assuming continual learning set probabilities = LearnFromPastSimulations([results], probabilities); } } // EntryPoint to execute the time-decay simulation for economic shocks @EntryPoint() operation ExecuteTimeDecayEconomicShockSimulation() : Unit { let initialProbabilities = [0.62, 0.78, 0.83, 0.69, 0.72]; let economicShockData = [3.5, 2.0, 4.0, 3.0, 2.5]; // Initial shock values in percentage let iterations = 25; using (qubits = Qubit[Length(initialProbabilities)]) { SimulateEconomicShockDecay(qubits, initialProbabilities, economicShockData, iterations); } }