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Accelerating Quantum Circuit Simulations with Data Compression
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DOI：10.1002/qute.202500223.png)
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Abstract
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Full-state quantum circuit simulators play a significant role in the design and validation of quantum algorithms. With an increasing demand for the number of quantum bits (qubits), achieving efficient simulations under memory constraints emerges as a technical challenge since a state vector with 2 n $2^n$ elements should be maintained in each simulation step, where n $n$ denotes the number of qubits. Previous work, CompQSim, partitions a state vector into blocks and uses block compression to conduct larger-scale in-memory simulations on supercomputers. Since the effectiveness of compressors depends on the distinctive characteristics exhibited by the circuits during simulations, an adaptive algorithm is designed to employ data compression and secondary storage astutely. The costs associated with the block processing, including the (de)compression and I/O, constitute a major portion of simulation time. To minimize such costs, two novel simulation algorithms, called BlkQSim and HyQSim are proposed. BlkQSim uses a block-oriented simulation order to reduce the block processing costs, while HyQSim can further reduce these costs by employing different simulation algorithms on qubits. This study conducts rigorous cost analysis and presents extensive experimental results, which show that compared with CompQSim, BlkQSim and HyQSim can achieve more than 4 × $\times$ and 107 × $\times$ speedups in block processing time, respectively.
Keywords:
data compression
quantum computing
quantum circuit simulations
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A
IF:
4.3
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470
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3.2K
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