Fast Classical Simulation of Hamiltonian Dynamics by Simultaneous Diagonalization Using Clifford Transformation with Parallel Computation

• URL: http://arxiv.org/abs/2206.11664v1
• Date: Thu, 23 Jun 2022 12:39:54 GMT
• Title: Fast Classical Simulation of Hamiltonian Dynamics by Simultaneous Diagonalization Using Clifford Transformation with Parallel Computation
• Authors: Yoshiaki Kawase and Keisuke Fujii
• Abstract summary: We propose a technique to accelerate simulation of quantum dynamics via simultaneous diagonalization of mutually commuting Pauli groups. Compared to an implementation using one of the fastest simulators of quantum computers, our method provides a few tens of times acceleration.
• Score: 0.8206877486958002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating quantum many-body dynamics is important both for fundamental understanding of physics and practical applications for quantum information processing. Therefore, classical simulation methods have been developed so far. Specifically, the Trotter-Suzuki decomposition can analyze a highly complex quantum dynamics, if the number of qubits is sufficiently small so that main memory can store the state vector. However, simulation of quantum dynamics via Trotter-Suzuki decomposition requires huge number of steps, each of which accesses the state vector, and hence the simulation time becomes impractically long. To settle this issue, we propose a technique to accelerate simulation of quantum dynamics via simultaneous diagonalization of mutually commuting Pauli groups, which is also attracting a lot of attention to reduce the measurement overheads in quantum algorithms. We group the Hamiltonian into mutually commputing Pauli strings, and each of them are diagonalized in the computational basis via a Clifford transformation. Since diagonal operators are applied on the state vector simultaneously with minimum memory access, this method successfully use performance of highly parallel processors such as Graphics Processing Units (GPU). Compared to an implementation using one of the fastest simulators of quantum computers, the numerical experiments have shown that our method provides a few tens of times acceleration.

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