Error mitigation for logical circuits using decoder confidence

• URL: http://arxiv.org/abs/2512.15689v1
• Date: Wed, 17 Dec 2025 18:45:27 GMT
• Title: Error mitigation for logical circuits using decoder confidence
• Abstract summary: A decoder confidence score (DCS) may provide a decoder confidence score (DCS) to gauge its success.<n>We adopt a swim distance DCS, computed from the shortest path between syndrome clusters.<n>We find that both reliably estimate the logical error probability (LEP) in a decoding window.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fault-tolerant quantum computers use decoders to monitor for errors and find a plausible correction. A decoder may provide a decoder confidence score (DCS) to gauge its success. We adopt a swim distance DCS, computed from the shortest path between syndrome clusters. By contracting tensor networks, we compare its performance to the well-known complementary gap and find that both reliably estimate the logical error probability (LEP) in a decoding window. We explore ways to use this to mitigate the LEP in entire circuits. For shallow circuits, we just abort if any decoding window produces an exceptionally low DCS: for a distance-13 surface code, rejecting a mere 0.1% of possible DCS values improves the entire circuit's LEP by more than 5 orders of magnitude. For larger algorithms comprising up to trillions of windows, DCS-based rejection remains effective for enhancing observable estimation. Moreover, one can use DCS to assign each circuit's output a unique LEP, and use it as a basis for maximum likelihood inference. This can reduce the effects of noise by an order of magnitude at no quantum cost; methods can be combined for further improvements.

Related papers

• Machine Learning-Based Reconstruction for Resistive Silicon Sensors [53.365453214937496]
  We study machine-learning-based reconstruction and compression for resistive silicon sensors.<n>We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information.<n>We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction.
  arXiv  Detail & Related papers  (2026-07-13T14:03:26Z)
• Degeneracy Cutting: A Local and Efficient Post-Processing for Belief Propagation Decoding of Quantum Low-Density Parity-Check Codes [0.9558392439655014]
  Quantum low-density parity-check (qLDPC) codes are promising for realizing scalable fault-tolerant quantum computation.<n>A common approach to decoding qLDPC codes is to use the belief propagation (BP) decoder, followed by a post-processing step to enhance decoding accuracy.<n>For real-time decoding, the post-processing algorithm is desirable to have a small computational cost and rely only on local operations on the Tanner graph to facilitate parallel implementation.<n>We propose degeneracy cutting (DC), an efficient post-processing technique for the BP decoder that operates on information restricted to the support of each stabilizer
  arXiv  Detail & Related papers  (2025-10-09T18:01:30Z)
• Fast correlated decoding of transversal logical algorithms [67.01652927671279]
  Quantum error correction (QEC) is required for large-scale computation, but incurs a significant resource overhead.<n>Recent advances have shown that by jointly decoding logical qubits in algorithms composed of logical gates, the number of syndrome extraction rounds can be reduced.<n>Here, we reform the problem of decoding circuits by directly decoding relevant logical operator products as they propagate through the circuit.
  arXiv  Detail & Related papers  (2025-05-19T18:00:00Z)
• Efficient Approximate Degenerate Ordered Statistics Decoding for Quantum Codes via Reliable Subset Reduction [5.625796693054094]
  We introduce the concept of approximate degenerate decoding and integrate it with ordered statistics decoding (OSD)<n>We present an ADOSD algorithm that significantly improves OSD efficiency in the code capacity noise model.
  arXiv  Detail & Related papers  (2024-12-30T17:45:08Z)
• LUCI in the Surface Code with Dropouts [0.3105392140394824]
  We present LUCI, a framework for constructing fault-tolerant circuits. LUCI can be used to adapt surface code circuits with latticesically imperfect qubit couplers. We show that LUCI requires roughly fewer physical qubits to reach relevant one-in-a-trillion logical codeblock error rates.
  arXiv  Detail & Related papers  (2024-10-18T22:29:37Z)
• Ambiguity Clustering: an accurate and efficient decoder for qLDPC codes [0.0]
  We introduce the Ambiguity Clustering decoder (AC) which divides measurement data into clusters that can be decoded independently.<n>With 0.3% circuit-level depolarising noise, AC is up to 27x faster than BP-OSD with matched accuracy.<n>Our implementation decodes the 144-qubit Gross code in 135us per round of syndrome extraction on an M2 CPU.
  arXiv  Detail & Related papers  (2024-06-20T17:39:31Z)
• Estimating the Decoding Failure Rate of Binary Regular Codes Using Iterative Decoding [84.0257274213152]
  We propose a new technique to provide accurate estimates of the DFR of a two-iterations (parallel) bit flipping decoder.<n>We validate our results, providing comparisons of the modeled and simulated weight of the syndrome, incorrectly-guessed error bit distribution at the end of the first iteration, and two-itcrypteration Decoding Failure Rates (DFR)
  arXiv  Detail & Related papers  (2024-01-30T11:40:24Z)
• Improved Noisy Syndrome Decoding of Quantum LDPC Codes with Sliding Window [0.0]
  We study sliding-window decoding, which corrects errors from previous syndrome measurement rounds while leaving the most recent errors for future correction. Remarkably, we find that this improvement may not cost a larger decoding complexity.
  arXiv  Detail & Related papers  (2023-11-06T17:56:49Z)
• 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)
• Improved decoding of circuit noise and fragile boundaries of tailored surface codes [61.411482146110984]
  We introduce decoders that are both fast and accurate, and can be used with a wide class of quantum error correction codes. Our decoders, named belief-matching and belief-find, exploit all noise information and thereby unlock higher accuracy demonstrations of QEC. We find that the decoders led to a much higher threshold and lower qubit overhead in the tailored surface code with respect to the standard, square surface code.
  arXiv  Detail & Related papers  (2022-03-09T18:48:54Z)
• Performance of teleportation-based error correction circuits for bosonic codes with noisy measurements [58.720142291102135]
  We analyze the error-correction capabilities of rotation-symmetric codes using a teleportation-based error-correction circuit. We find that with the currently achievable measurement efficiencies in microwave optics, bosonic rotation codes undergo a substantial decrease in their break-even potential.
  arXiv  Detail & Related papers  (2021-08-02T16:12:13Z)
• Pruning Neural Belief Propagation Decoders [77.237958592189]
  We introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We achieve performance within 0.27 dB and 1.5 dB of the ML performance while reducing the complexity of the decoder.
  arXiv  Detail & Related papers  (2020-01-21T12:05:46Z)

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.