Error Mitigation of Fault-Tolerant Quantum Circuits with Soft Information

• URL: http://arxiv.org/abs/2512.09863v1
• Date: Wed, 10 Dec 2025 17:49:06 GMT
• Title: Error Mitigation of Fault-Tolerant Quantum Circuits with Soft Information
• Authors: Zeyuan Zhou, Shaun Pexton, Aleksander Kubica, Yongshan Ding,
• Abstract summary: We show that Quantum error mitigation (QEM) can continue to provide substantial benefits in the era of quantum error correction (QEC)<n>We develop and analyze three logical-level QEM techniques: post-selection and runtime abort policies, probabilistic error cancellation, and zero-noise extrapolation.<n>Our techniques reduce logical error rates by more than 100x while discarding fewer than 0.1% of shots.
• Score: 39.64742821341961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum error mitigation (QEM) is typically viewed as a suite of practical techniques for today's noisy intermediate-scale quantum devices, with limited relevance once fault-tolerant quantum computers become available. In this work, we challenge this conventional wisdom by showing that QEM can continue to provide substantial benefits in the era of quantum error correction (QEC), and in an even more efficient manner than it does on current devices. We introduce a framework for logical-level QEM that leverages soft information naturally produced by QEC decoders, requiring no additional data, hardware modifications, or runtime overhead beyond what QEC protocols already provide. Within this framework, we develop and analyze three logical-level QEM techniques: post-selection and runtime abort policies, probabilistic error cancellation, and zero-noise extrapolation. Our techniques reduce logical error rates by more than 100x while discarding fewer than 0.1% of shots; they also provide in situ characterization of logical channels for QEM protocols. As a proof of principle, we benchmark our approach using a surface-code architecture and two state-of-the-art decoders based on tensor-network contraction and minimum-weight perfect matching. We evaluate logical-level QEM on random Clifford circuits and molecular simulation algorithms and find that, compared to previous approaches relying on QEC only or QEC combined with QEM, we can achieve up to 87.4% spacetime overhead savings. Our results demonstrate that logical-level QEM with QEC decoder soft information can reliably improve logical performance, underscoring the efficiency and usefulness of QEM techniques for fault-tolerant quantum computers.

Related papers

• AQER: a scalable and efficient data loader for digital quantum computers [62.40228216126285]
  We develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states.<n>We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits.
  arXiv  Detail & Related papers  (2026-02-02T14:39:42Z)
• Quantum Error Correction and Detection for Quantum Machine Learning [0.788073088949581]
  Quantum machine learning (QML) is poised to revolutionize artificial intelligence.<n>The vulnerability of the current generation of quantum computers to noise and computational error poses a significant barrier to this vision.<n>We examine strategies for integrating quantum error correction (QEC) and quantum error detection (QED) into QML under realistic resource constraints.
  arXiv  Detail & Related papers  (2026-01-12T05:41:10Z)
• Extending coherence time beyond break-even point using only drives and dissipation [12.35444043278315]
  Quantum error correction (QEC) aims to mitigate the loss of quantum information to the environment.<n>We propose an AQEC protocol, derived from quantum channel simulation, that is applicable to arbitrary error-correcting codes.<n>This is the first experimental realization of an AQEC-protected bosonic logical qubit beyond the break-even point.
  arXiv  Detail & Related papers  (2025-09-26T10:50:27Z)
• Compilation-informed probabilistic quantum error cancellation [2.079863206645103]
  We introduce a quantum error mitigation (QEM) scheme against both compilation errors and logical-gate noise that is circuit-, QEC code-, and compiler-agnostic.<n>It features maximal circuit size and QEC code-distance both independent of the target precision, in contrast to strategies based on QEC alone.<n>Our method significantly reduces quantum resource requirements for high-precision estimations, offering a practical route towards fault-tolerant quantum computation with precision-independent overheads.
  arXiv  Detail & Related papers  (2025-08-27T18:00:10Z)
• Variational Quantum Machine Learning with Quantum Error Detection [0.6435156676256051]
  Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior extraction over classical counterparts.<n>Its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes.<n>Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML.
  arXiv  Detail & Related papers  (2025-04-09T10:56:21Z)
• Fault-tolerant logical state construction based on cavity-QED network [0.196629787330046]
  We propose and evaluate a scalable and practical architecture with a cavity-quantum-electrodynamics (CQED) network.<n>Our architecture takes advantage of the stability of neutral atoms and the flexibility of a CQED network.
  arXiv  Detail & Related papers  (2025-03-14T15:26:29Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.