Data-Efficient Error Mitigation for Physical and Algorithmic Errors in a Hamiltonian Simulation

• URL: http://arxiv.org/abs/2503.05052v1
• Date: Fri, 07 Mar 2025 00:05:52 GMT
• Title: Data-Efficient Error Mitigation for Physical and Algorithmic Errors in a Hamiltonian Simulation
• Authors: Shigeo Hakkaku, Yasunari Suzuki, Yuuki Tokunaga, Suguru Endo,
• Abstract summary: We propose a data-efficient 1D extrapolation method to mitigate physical and algorithmic errors of Trotterized quantum circuits.<n>We numerically demonstrate our proposed methods and confirm that our proposed extrapolation method suppresses both statistical and systematic errors more than the previous extrapolation method.
• Score: 0.17999333451993949
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Quantum dynamics simulation via Hamilton simulation algorithms is one of the most crucial applications in the quantum computing field. While this task has been relatively considered the target in the fault-tolerance era, the experiment for demonstrating utility by an IBM team simulates the dynamics of an Ising-type quantum system with the Trotter-based Hamiltonian simulation algorithm with the help of quantum error mitigation. In this study, we propose the data-efficient 1D extrapolation method to mitigate not only physical errors but also algorithmic errors of Trotterized quantum circuits in both the near-term and early fault-tolerant eras. Our proposed extrapolation method uses expectation values obtained by Trotterized circuits, where the Trotter number is selected to minimize both physical and algorithmic errors according to the circuit's physical error rate. We also propose a method that combines the data-efficient 1D extrapolation with purification QEM methods, which improves accuracy more at the expense of multiple copies of quantum states or the depth of the quantum circuit. Using the 1D transverse-field Ising model, we numerically demonstrate our proposed methods and confirm that our proposed extrapolation method suppresses both statistical and systematic errors more than the previous extrapolation method.

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