Improving the efficiency of learning-based error mitigation

Journal Article · 05 May 2025 · Quantum

DOI: https://doi.org/10.22331/q-2025-05-05-1727 · OSTI ID:2564312

Czarnik, Piotr ^[1]; McKerns, Michael ^[2]; Sornborger, Andrew T. ^[2]; Cincio, Lukasz ^[3]

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1. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA., Institute of Theoretical Physics, Jagiellonian University, Krakow, Poland.
2. Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA.
3. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA., Quantum Science Center, Oak Ridge, TN 37931, USA.

Error mitigation will play an important role in practical applications of near-term noisy quantum computers. Current error mitigation methods typically concentrate on correction quality at the expense of frugality (as measured by the number of additional calls to quantum hardware). To fill the need for highly accurate, yet inexpensive techniques, we introduce an error mitigation scheme that builds on Clifford data regression (CDR). The scheme improves the frugality by carefully choosing the training data and exploiting the symmetries of the problem. We test our approach by correcting long range correlators of the ground state of XY Hamiltonian on IBM Toronto quantum computer. We find that our method is an order of magnitude cheaper while maintaining the same accuracy as the original CDR approach. The efficiency gain enables us to obtain a factor of $10$ improvement on the unmitigated results with the total budget as small as $2\cdot {10}^5$ shots. Furthermore, we demonstrate orders of magnitude improvements in frugality for mitigation of energy of the LiH ground state simulated with IBM's Ourense-derived noise model.

• Sponsoring Organization: USDOE
• OSTI ID: 2564312
• Journal Information: Quantum, Journal Name: Quantum Vol. 9; ISSN 2521-327X
• Publisher: Verein zur Forderung des Open Access Publizierens in den QuantenwissenschaftenCopyright Statement
• Country of Publication: Austria
• Language: English