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On the Importance of Error Mitigation for Quantum Computation

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arxiv 2503.17243 v1 pith:Z2CLKUED submitted 2025-03-21 quant-ph cond-mat.str-elcs.CC

classification quant-phcond-mat.str-elcs.CC
keywords quantumerrorroleexpectedfirstachievingbenefitsclear
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantum error mitigation (EM) is a family of hybrid quantum-classical methods for eliminating or reducing the effect of noise and decoherence on quantum algorithms run on quantum hardware, without applying quantum error correction (EC). While EM has many benefits compared to EC, specifically that it requires no (or little) qubit overhead, this benefit comes with a painful price: EM seems to necessitate an overhead in quantum run time which grows as a (mild) exponent. Accordingly, recent results show that EM alone cannot enable exponential quantum advantages (QAs), for an average variant of the expectation value estimation problem. These works raised concerns regarding the role of EM in the road map towards QAs. We aim to demystify the discussion and provide a clear picture of the role of EM in achieving QAs, both in the near and long term. We first propose a clear distinction between finite QA and asymptotic QA, which is crucial to the understanding of the question, and present the notion of circuit volume boost, which we claim is an adequate way to quantify the benefits of EM. Using these notions, we can argue straightforwardly that EM is expected to have a significant role in achieving QAs. Specifically, that EM is likely to be the first error reduction method for useful finite QAs, before EC; that the first such QAs are expected to be achieved using EM in the very near future; and that EM is expected to maintain its important role in quantum computation even when EC will be routinely used - for as long as high-quality qubits remain a scarce resource.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Drift-resilient mid-circuit measurement and state preparation error mitigation for dynamic circuits

    quant-ph 2025-06 accept novelty 8.0 of 10

    Parity of repeated measurements realizes an amplified readout-error channel, enabling drift-resilient, characterization-free mitigation of mid-circuit and terminating measurement and preparation errors.

  2. Mitigating errors in state preparation and measurement with noncomputational states

    quant-ph 2025-06 conditional novelty 7.0 of 10

    Using extra transmon levels to measure state-preparation error lets a noise-learning protocol separate state-preparation, gate, and measurement errors, including for mid-circuit measurements.

  3. Quantum Error Management in Practice: A Cross-Stack Benchmark

    quant-ph 2026-08 conditional novelty 6.0 of 10

    On a 156-qubit IBM Heron processor, Q-CTRL and Qedma QESEM reduced estimation error by 3.1x and 4.7x versus raw IBM execution, with QESEM using 7.5 to 11.1x the QPU time of Q-CTRL.

  4. Syndrome aware mitigation of logical errors

    quant-ph 2025-12 conditional novelty 6.0 of 10

    Conditioning logical error mitigation on the measured error-correcting syndromes cuts sampling overhead exponentially and can make error correction useful above its standard pseudo-threshold.

  5. Faster Probabilistic Error Cancellation

    quant-ph 2025-06 conditional novelty 6.0 of 10

    A binomial-expansion reformulation of PEC, with deterministic shot allocation and truncation-based bias control, reduces the sampling overhead of quantum error mitigation.

  6. A Framework for Quantum Advantage

    quant-ph 2025-06 conditional novelty 4.0 of 10

    A framework defining quantum advantage as verifiable plus classically superior, with a conclusion that random circuit sampling is not yet a satisfactory path.

  7. Quantum Algorithm Software for Condensed Matter Physics

    cond-mat.str-el 2025-06 reject novelty 2.0 of 10

    A review of quantum algorithm software that advertises a benchmark suite, yet the body contains no benchmarks, data, or code.

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