Quantum Circuit Simulation Using Tensor Data Reuse
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Solution Overview
Problem
Iterating quantum circuit simulation while changing parameter values imposes a high computational load on classical computers.
Innovation Solution
A method involving a non-transitory computer-readable storage medium that stores a computer program to perform a process including obtaining quantum circuit data, generating tensor data for a common part of two quantum circuits with different parameter values, and computing the computation results of both circuits using the generated tensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum circuit simulation is performed by computing each circuit independently, then computation accuracy is maintained, but computational load and computation time increase significantly
Solution Approach 1:
The quantum circuit is segmented into a common part and a variable part. The common part (quantum gates that do not change with parameter values) is computed once and stored as reusable tensor data, while only the variable part is recomputed for each parameter search iteration. This segmentation maintains computation accuracy while dramatically reducing redundant calculations.
Solution Approach 2:
The computation of the common part is performed in advance before the parameter search begins. The tensor data representing the common part's computation result is prepared beforehand and stored for reuse, eliminating the need to recompute it during iterative parameter optimization.
2Reliability
If parameter search in quantum circuit simulation is performed by full recomputation, then correctness is ensured, but computation time becomes prohibitively long
Solution Approach 1:
The circuit computation is divided into common and variable segments. By proving that the common part remains invariant across parameter changes, the patent enables selective recomputation of only the variable part, ensuring correctness while reducing time consumption.
Solution Approach 2:
The patent identifies which parts of the circuit actually change when parameters are modified. By analyzing the parameter dependency of each quantum gate, only the affected variable part is recomputed, while the parameter-independent common part is reused, optimizing the trade-off between correctness and efficiency.
3Measurement precision
If tensor network contraction is performed for entire quantum circuits repeatedly, then accurate results are obtained, but computational resources are excessively consumed
Solution Approach 1:
The tensor network contraction is applied only to the variable part of the circuit that changes with parameters. The common part's tensor representation is pre-computed and reused, significantly reducing the number of contraction operations required during parameter search iterations.
Solution Approach 2:
The tensor data for the common part is computed and stored in advance before parameter optimization begins. This preliminary computation eliminates redundant tensor contractions during iterative parameter tuning, reducing computational resource consumption while maintaining simulation accuracy.
Data Source
AI summary
A memory stores quantum circuit data defining a quantum circuit that varies with the value of a parameter. For a common part where the types and sequence of one or more quantum gates are common in a first quantum circuit generated with a first value input into the parameter and a second quantum circuit generated with a second value input into the parameter, a processor generates tensor data representing the computation result of the common part using tensor data representing the quantum operation performed by the one or more quantum gates included in the common part. The processor computes the computation result of the first quantum circuit and the computation result of the second quantum circuit using the generated tensor data.