Clifford circuit based heuristic optimization of fermion-to-qubit mappings

• URL: http://arxiv.org/abs/2502.11933v1
• Date: Mon, 17 Feb 2025 15:44:23 GMT
• Title: Clifford circuit based heuristic optimization of fermion-to-qubit mappings
• Authors: Jeffery Yu, Yuan Liu, Sho Sugiura, Troy Van Voorhis, Sina Zeytinoğlu,
• Abstract summary: Simulation of interacting fermionic Hamiltonians is one of the most promising applications of quantum computers.<n>Fermion-to-qubit mappings encode non-local fermionic degrees of freedom in local qubit degrees of freedom.
• Score: 3.1981483719988235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation of interacting fermionic Hamiltonians is one of the most promising applications of quantum computers. However, the feasibility of analysing fermionic systems with a quantum computer hinges on the efficiency of fermion-to-qubit mappings that encode non-local fermionic degrees of freedom in local qubit degrees of freedom. While recent works have highlighted the importance of designing fermion-to-qubit mappings that are tailored to specific problem Hamiltonians, the methods proposed so far are either restricted to a narrow class of mappings or they use computationally expensive and unscalable brute-force search algorithms. Here, we address this challenge by designing a $\mathrm{\textbf{heuristic}}$ numerical optimization framework for fermion-to-qubit mappings. To this end, we first translate the fermion-to-qubit mapping problem to a Clifford circuit optimization problem, and then use simulated annealing to optimize the average Pauli weight of the problem Hamiltonian. For all fermionic Hamiltonians we have considered, the numerically optimized mappings outperform their conventional counterparts, including ternary-tree-based mappings that are known to be optimal for single creation and annihilation operators. We find that our optimized mappings yield between $15\%$ to $40\%$ improvements on the average Pauli weight when the simulation Hamiltonian has an intermediate level of complexity. Most remarkably, the optimized mappings improve the average Pauli weight for $6 \times 6$ nearest-neighbor hopping and Hubbard models by more than $40\%$ and $20\%$, respectively. Surprisingly, we also find specific interaction Hamiltonians for which the optimized mapping outperform $\mathrm{\textbf{any}}$ ternary-tree-based mapping. Our results establish heuristic numerical optimization as an effective method for obtaining mappings tailored for specific fermionic Hamiltonian.

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