Selective VQE Parameter Updates for Quantum Chemical Computation
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Solution Overview
Problem
The processing time for VQE computation increases with the number of parameters defining a variational quantum circuit, leading to longer computation times and increased iterations.
Innovation Solution
An information processing program that specifies patterns representing parameter combinations, selects parameters with high contribution to computation based on a cost function, and controls an arithmetic device to execute iterations using these selected parameters, thereby reducing the number of parameters updated during VQE computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of parameters defining a variational quantum circuit is increased to improve computation accuracy, then the computation accuracy is improved, but the processing time taken for VQE computation increases
Solution Approach 1:
The patent segments the parameters into two groups: target parameters whose values are updated during VQE computation, and non-target parameters whose values are fixed. This segmentation allows the system to maintain computation accuracy by focusing optimization efforts only on the essential target parameters, thereby reducing processing time without sacrificing the quality of computational results.
Solution Approach 2:
The patent applies local quality by differentiating the treatment of different parameters - target parameters are actively optimized while non-target parameters are held fixed. This localized optimization approach ensures that computational resources are concentrated on parameters that most significantly impact the variational quantum circuit's performance, achieving accurate results with reduced processing time.
2Measurement precision
If the number of parameters defining a variational quantum circuit is increased to improve computation accuracy, then the computation accuracy is improved, but the number of times of executing iteration increases
Solution Approach 1:
The patent segments parameters into target and non-target groups, where only target parameters are updated during iterations. This reduces the effective number of parameters that require optimization in each iteration cycle, allowing the system to reach convergence faster and reduce the total number of iterations while maintaining accurate computational results.
Solution Approach 2:
The patent changes the status of parameters from all parameters being potentially updated to a selective subset (target parameters) being updated. This parameter change strategy allows the optimization process to focus on the most impactful parameters, reducing the number of iterations needed to achieve convergence while preserving computation accuracy.
3Measurement precision
If all parameters are updated during VQE computation to ensure accurate results, then computation accuracy is maintained, but processing time increases
Solution Approach 1:
The patent segments parameters into target and non-target categories, updating only the target parameters during VQE computation. This segmentation enables the system to maintain computation accuracy by focusing updates on the most critical parameters while improving productivity by avoiding unnecessary updates to non-target parameters.
Solution Approach 2:
The patent implements local quality through selective parameter updates, where target parameters receive full optimization attention and non-target parameters are held fixed. This localized approach ensures accurate computational results from target parameters while improving overall computational efficiency by excluding non-essential parameters from the optimization process.
Data Source
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AI summary
A program including instructions which, when executed by a computer, cause the computer to execute: specifying two or more patterns that represent combinations of values of respective parameters of multiple parameters defining a variational quantum circuit to be used for quantum chemical computation in computation by a variational quantum eigensolver method; selecting one or more first parameters determined to have a relatively high degree of contribution to the computation from the multiple parameters using a calculation result of a cost function used for the quantum chemical computation for each pattern of the two or more patterns that have been specified; and controlling, by setting the one or more first parameters as target parameters whose value is to be updated in the computation, an arithmetic device that executes the variational quantum circuit such that at least one iteration of the computation is executed using the one or more first parameters.