Abstract
Quantum simulation on classical computers is one of the main approaches to evaluate quantum computation devices and develop quantum algorithms. Some quantum simulators have been proposed, mainly divided into two categories: full-state simulators and tensor network simulators. The former consumes a lot of memory to hold the quantum state vectors. Therefore, the time overheads cost by calculation are much lower than that cost by memory-accesses and communications. Traditional optimization techniques such as latency hiding are not suitable for quantum simulation, and high-performance devices like GPGPUs cannot be fully utilized. This paper proposes DLC (Data Locality and Communication) optimizer to perform data locality and data layout optimizations. Both optimizations are based on the identification of amplitudes that can be processed by a sequence of quantum gates. They not only increase the data locality on the GPU side, but also reduces the date communication overheads and the times of data exchanges. In addition, layout data dynamically can significantly reduce the memory space on the GPGPU side for data communication. We evaluate our scheme on a small-scale CPU + GPU cluster. Experimental results show that for quantum circuits having 30–34 qubits, the ratio of communication to calculation increases from 12 to 79%, and a performance improvement 1.25–7× is achieved. Theoretically, our optimizations will be more effective as the number of qubits increases.
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Qin, Z., Li, T., Shen, L. (2022). DLC: An Optimization Framework for Full-State Quantum Simulation. In: Liu, S., Wei, X. (eds) Network and Parallel Computing. NPC 2022. Lecture Notes in Computer Science, vol 13615. Springer, Cham. https://doi.org/10.1007/978-3-031-21395-3_19
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DOI: https://doi.org/10.1007/978-3-031-21395-3_19
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