Physics-Guided Linear Mapper for Quantum Error Mitigation

• URL: http://arxiv.org/abs/2608.23109v1
• Date: Mon, 24 Aug 2026 11:19:10 GMT
• Title: Physics-Guided Linear Mapper for Quantum Error Mitigation
• Abstract summary: We introduce a novel physics-guided linear mapper (PGLM) for quantum error mitigation.<n>The goal is to provide a data-efficient, interpretable, and low-latency alternative to the black-box machine learning for quantum error mitigation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel physics-guided linear mapper (PGLM) for quantum error mitigation that uses seven distinct interpretable features derived from circuit complexity and device calibration data. The goal is to provide a data-efficient, interpretable, and low-latency alternative to the black-box machine learning for quantum error mitigation in noisy-intermediate scale quantum devices. Evaluated on 52 simulated benchmark circuits (1--4 qubits), PGLM demonstrates strong performance in noise-accumulation regimes: 50.1% RMSE reduction on 3-qubit circuits and 32.3% on 4-qubit circuits, while single-qubit circuits show degraded performance. A circuit-size-aware deployment policy achieves 32.6% aggregate improvement. Sub-millisecond inference enables integration into variational algorithms, and analysis of learned coefficients reveals that circuit depth and CNOT count dominate error prediction, consistent with decoherence mechanisms. Results are simulator-based with idealized noise models; hardware validation remains essential future work.

Related papers

• Machine Learning-based Quantum Error Mitigation for Variational Algorithms [0.0]
  Machine Learning-based quantum error mitigation (ML-QEM) has emerged as a promising approach for improving the performance of noisy quantum algorithms.<n>We propose a practical ML-QEM protocol tailored to variational quantum algorithms, which generates training data by simulating (near-)Clifford circuits.<n>We benchmark the proposed method on the Variational Quantum Eigensolver (VQE) task for the Sherrington-Kirkpatrick Hamiltonian of up to $n=12$ qubits under various noise models, analyzing its effect on trainability and comparing its performance against standard Zero-Noise Extrapolation (ZNE
  arXiv  Detail & Related papers  (2026-06-01T18:00:00Z)
• Quantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization [0.0]
  Gate Assessment and Threshold Evaluation (GATE) is presented as a circuit optimization methodology.<n>GATE quantifies the relevance of each gate by combining fidelity, entanglement, and sensitivity.<n>It is formulated for both simulator/emulator environments, where quantum states are accessible, and for real hardware.
  arXiv  Detail & Related papers  (2026-03-20T09:44:07Z)
• Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness [30.900274564864223]
  QuFid is an adaptive and noise-aware framework that determines measurement budgets online.<n>We show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines.
  arXiv  Detail & Related papers  (2026-01-21T07:04:05Z)
• Variational noise mitigation in quantum circuits: the case of Quantum Fourier Transform [35.18016233072556]
  We perform numerical simulations for two qubits under both coherent and incoherent noise.<n>Our results show that the variational circuit can reproduce the QFT with higher fidelity in scenarios dominated by coherent noise.<n>This demonstrates the potential of the approach as an effective error-mitigation strategy for small- to medium-scale quantum systems.
  arXiv  Detail & Related papers  (2025-11-07T14:35:55Z)
• Analog Quantum Feature Selection with Neutral-Atom Quantum Processors [31.458406135473805]
  We present a quantum-native approach to quantum feature selection (QFS) based on analog quantum simulation with neutral atom arrays.<n>The protocol is evaluated through simulations on three benchmark binary classification datasets: Adult Income, Bank Marketing, and Telco Churn.<n>For compact subsets of 2-5 features, analog QFS improves mean AUC scores by 1.5-2.3% while reducing the number of features by 75-84%.
  arXiv  Detail & Related papers  (2025-10-23T17:57:34Z)
• A Useful Metric for the NISQ Era: Qubit Error Probability and Its Role in Zero Noise Extrapolation [0.0]
  We propose the qubit error probability (QEP), a device specific metric that combines relaxation, dephasing, gate, and measurement contributions into a single per qubit figure of merit computable before execution.<n>In regimes where the raw circuits exhibit a finite mean QEP, the method suppresses observable errors beyond those attainable with circuit depth scaled ZNE.<n>These results demonstrate that QEP serves as a transparent and efficient error metric, and that its integration into ZNE provides a practical route to reliability gains on current superconducting hardware.
  arXiv  Detail & Related papers  (2025-03-13T09:42:03Z)
• Non-parametric Greedy Optimization of Parametric Quantum Circuits [2.77390041716769]
  This work aims to reduce depth and gate count of PQCs by replacing parametric gates with their approximate fixed non-parametric representations. We observe roughly 14% reduction in depth and 48% reduction in gate count at the cost of 3.33% reduction in inferencing accuracy.
  arXiv  Detail & Related papers  (2024-01-27T15:29:38Z)
• Fast Flux-Activated Leakage Reduction for Superconducting Quantum Circuits [84.60542868688235]
  leakage out of the computational subspace arising from the multi-level structure of qubit implementations. We present a resource-efficient universal leakage reduction unit for superconducting qubits using parametric flux modulation. We demonstrate that using the leakage reduction unit in repeated weight-two stabilizer measurements reduces the total number of detected errors in a scalable fashion.
  arXiv  Detail & Related papers  (2023-09-13T16:21:32Z)
• Classical simulations of noisy variational quantum circuits [0.0]
  Noisely affects quantum computations so that they not only become less accurate but also easier to simulate classically as systems scale up. We construct a classical simulation algorithm, LOWESA, for estimating expectation values of noisy parameterised quantum circuits.
  arXiv  Detail & Related papers  (2023-06-08T17:52:30Z)
• QuEst: Graph Transformer for Quantum Circuit Reliability Estimation [26.963991539714236]
  Python library called TorchQuantum can construct, simulate, and train PQC for machine learning tasks.<n>We propose to leverage a graph transformer model to predict noise impact on circuit fidelity.<n>Compared with circuit simulators, the predictor has over 200X speedup for estimating the fidelity.
  arXiv  Detail & Related papers  (2022-10-30T02:35:31Z)
• Efficient and robust certification of genuine multipartite entanglement in noisy quantum error correction circuits [58.720142291102135]
  We introduce a conditional witnessing technique to certify genuine multipartite entanglement (GME) We prove that the detection of entanglement in a linear number of bipartitions by a number of measurements scales linearly, suffices to certify GME. We apply our method to the noisy readout of stabilizer operators of the distance-three topological color code and its flag-based fault-tolerant version.
  arXiv  Detail & Related papers  (2020-10-06T18:00:07Z)
• Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets [47.00474212574662]
  Variational quantum algorithms are believed to be promising for solving computationally hard problems. In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances. Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
  arXiv  Detail & Related papers  (2020-05-11T17:20:51Z)

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.