Scalable Simulation of Fermionic Encoding Performance on Noisy Quantum Computers

• URL: http://arxiv.org/abs/2506.06425v2
• Date: Mon, 07 Jul 2025 21:16:52 GMT
• Title: Scalable Simulation of Fermionic Encoding Performance on Noisy Quantum Computers
• Authors: Emiliia Dyrenkova, Raymond Laflamme, Michael Vasmer,
• Abstract summary: We study the performance of a local encoding known as the Derby-Klassen encoding.<n>We find that the high sampling requirements of postselection methods with the Derby-Klassen encoding pose a limitation to its applicability in near-term devices.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A compelling application of quantum computers with thousands of qubits is quantum simulation. Simulating fermionic systems is both a problem with clear real-world applications and a computationally challenging task. In order to simulate a system of fermions on a quantum computer, one has to first map the fermionic Hamiltonian to a qubit Hamiltonian. The most popular such mapping is the Jordan-Wigner encoding, which suffers from inefficiencies caused by the high weight of some encoded operators. As a result, alternative local encodings have been proposed that solve this problem at the expense of a constant factor increase in the number of qubits required. Some such encodings possess local stabilizers, i.e., Pauli operators that act as the logical identity on the encoded fermionic modes. A natural error mitigation approach in these cases is to measure the stabilizers and discard any run where a measurement returns a -1 outcome. Using a high-performance stabilizer simulator, we classically simulate the performance of a local encoding known as the Derby-Klassen encoding and compare its performance with the Jordan-Wigner encoding and the ternary tree encoding. Our simulations use more complex error models and significantly larger system sizes (up to $18\times18$) than in previous work. We find that the high sampling requirements of postselection methods with the Derby-Klassen encoding pose a limitation to its applicability in near-term devices and call for more encoding-specific circuit optimizations.

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