Quantum Expected Value Calculation with Noise-Reduced Simulation
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Solution Overview
Problem
Quantum computers, particularly noisy intermediate-scale quantum (NISQ) devices, face challenges in accurately calculating expected values due to noise, leading to decreased accuracy and difficulty in determining convergence of parameters, especially as the scale of quantum circuits increases.
Innovation Solution
An expected value calculation system that includes a quantum computer, an update circuit to iteratively update parameters based on calculated expected values, and a calculation circuit to improve accuracy using a quantum simulator that simulates quantum bit operations without noise, facilitating higher precision and faster convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a quantum computer is used to calculate expected values, then quantum calculation capability is achieved, but calculation accuracy decreases due to noise
Solution Approach 1:
A classical computer is introduced as an intermediary to receive quantum measurement results, calculate expected values through classical computation, and perform parameter optimization. This mediator processes the noisy quantum outputs through multiple iterations of expectation value calculation and parameter updates, ultimately producing accurate results by combining quantum measurement capabilities with classical computational precision.
2Adaptability or versatility
If the scale of quantum circuits is increased, then calculation capability is enhanced, but noise effects increase and accuracy decreases
Solution Approach 1:
The calculation process is segmented into distinct phases: quantum measurement phase (obtaining raw data from quantum circuits), classical processing phase (calculating expectation values and updating parameters on classical computer), and iteration phase (repeating the process with updated parameters). This segmentation allows the system to handle complex, large-scale quantum circuits while managing noise effects through iterative classical post-processing.
3Measurement precision
If parameters are updated iteratively to improve accuracy, then calculation precision increases, but convergence determination becomes difficult due to noise
Solution Approach 1:
The system implements feedback through iterative parameter updates based on calculated expectation values. The classical computer receives measurement results from the quantum computer, computes expectation values, determines parameter updates, and feeds these back to the quantum computer for the next iteration. This closed-loop feedback mechanism systematically improves precision while using classical computation to objectively determine convergence criteria, overcoming the difficulty of judging convergence in noisy quantum systems.
Data Source
AI summary
An expected value calculation system includes: a quantum computer that calculates an expected value of a calculation target by using a quantum circuit; an update circuit that obtains a specific parameter by performing, a predetermined number of times, update processing in which the expected value is acquired from the quantum computer, an updated parameter is obtained by updating a parameter of the quantum circuit based on the expected value, and the quantum computer is controlled so as to calculate the expected value by using the quantum circuit to which the updated parameter is applied; and a calculation circuit that calculates the expected value with higher accuracy than that of the quantum computer by using the quantum circuit to which the specific parameter is applied.