Quantum Computation Support Using Regression Models for NISQ Noise
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Solution Overview
Problem
Quantum computations using noisy intermediate-scale quantum computers (NISQ) are prone to noise-induced errors, leading to deviations from true optimal solutions, particularly in methods like VQE and QAOA, which affect the accuracy of results.
Innovation Solution
A regression model is trained using a Gaussian process or kernel method to reduce the influence of noise by analyzing the correspondence between computation results and parameter values, allowing for the specification of solutions with reduced error through optimization history information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum computation is performed using NISQ devices, then quantum computation capability is achieved, but noise-induced errors occur leading to reduced accuracy
Solution Approach 1:
A regression model is introduced as an intermediary between the noisy quantum computation results and the final solution. The model learns the mapping from parameter values to computation results and provides denoised predictions, effectively mediating the harmful noise effects while preserving the quantum computation capability
Solution Approach 2:
Instead of directly trusting the noisy quantum computation results, the invention creates a classical copy or model (regression model) that replicates the quantum computation's input-output behavior. This classical copy can then be queried multiple times without additional quantum noise to obtain refined solutions
2Measurement precision
If multiple times of quantum computation are performed for each parameter value, then noise influence is reduced, but computation time increases
Solution Approach 1:
The regression model is trained in advance using a limited set of quantum computation results. Once trained, the model can provide accurate predictions without requiring repeated quantum computations for each query, thus performing the noise-reduction action preliminarily and efficiently
Solution Approach 2:
Instead of performing excessive quantum computations for every solution query, the invention performs a sufficient but limited number of quantum computations during the training phase. The model then handles subsequent queries with classical computation, avoiding the time cost of repeated quantum executions
Data Source
AI summary
A non-transitory computer-readable recording medium stores a quantum computation support program for causing a computer to execute a process including: training a regression model in which parameter values are explanatory variables and computation results of quantum computation are objective variables, based on a correspondence relationship between the computation results for each of a plurality of times of quantum computation for each of the parameter values according to a quantum circuit that includes parameters and the parameter values set in the quantum computation; and specifying a solution of the quantum computation by using the regression model.