Quantum Circuit Value-at-Risk Estimation
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Solution Overview
Problem
Current value-at-risk estimation methods using classical Monte Carlo methods are inefficient due to the need for large sample sizes, leading to slow and heavy calculations.
Innovation Solution
A quantum circuit-based method that obtains 2N sampling points from a distribution profile of value fluctuation ratios, prepares these points to a quantum state corresponding to N target qubits, and uses quantum circuits to determine target probabilities and update fluctuation ratio reference values until matching a preset probability threshold, thereby calculating the value-at-risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the classical Monte Carlo method is used for value-at-risk estimation, then the estimation can be performed with conventional computing resources, but the calculation is heavy and slow due to the requirement of large sample sizes
Solution Approach 1:
The patent replaces the classical Monte Carlo method with a quantum circuit-based approach. The quantum computer performs parallel computation of multiple sampling points simultaneously using quantum superposition, substituting the sequential mechanical computation of classical systems. This enables the quantum system to evaluate 2^N sampling points in parallel rather than sequentially processing each sample, dramatically improving estimation efficiency and reducing calculation time.
2Measurement precision
If a large sample size is used in the classical Monte Carlo method, then the accuracy of value-at-risk estimation is improved, but the calculation becomes heavier and slower
Solution Approach 1:
The patent transitions from classical computational dimension to quantum computational dimension by utilizing quantum bits (qubits) and quantum superposition. Instead of increasing sample size linearly in the classical domain, the quantum system achieves exponential scaling of sampling capacity through the dimensionality of quantum states. N qubits can represent 2^N sampling points simultaneously, providing high estimation accuracy without linearly increasing computational complexity.
3Productivity
If the quantum circuit method is used for value-at-risk estimation, then the efficiency and accuracy are improved, but the quantum circuit design and implementation becomes more complex
Solution Approach 1:
The patent segments the value-at-risk estimation process into distinct quantum circuit modules: (1) a quantum state preparation circuit that generates the probability distribution of asset returns, (2) a quantum comparator circuit that evaluates sampling points against the value-at-risk threshold, and (3) a measurement circuit that extracts the final estimation. This modular segmentation simplifies the overall quantum circuit design by breaking down the complex estimation task into manageable, reusable components.
Data Source
AI summary
Disclosed is a quantum circuit-based value-at-risk estimation method, device, medium and electronic device, which represents correspondingly sampled value fluctuation ratios and fluctuation ratio probabilities based on the N target qubits, then builds a corresponding quantum circuit based on the target qubits, obtains every target probability whose value fluctuation ratio is less than a fluctuation ratio reference value through the quantum circuit, then determines the target value fluctuation ratio according to the target probability and the probability threshold, and finally calculates the target value-at-risk of the target object according to the target value fluctuation ratio.