Aug 2026· IEEE Journal on Selected Areas in Communications· Vol 44, pp. 5600-5614· 1 citation· 44 references
Computer SciencePhysics
TL;DR
This work introduces a novel adaptive noise mitigation framework for VQCs that integrates ZNE with contextual multi-armed bandits (CMAB), enabling dynamic, context-aware selection of circuit-folding levels based on ansatz parameters and the evolving noise environment.
Abstract
Variational quantum circuits (VQCs) are central to near-term quantum computing, yet their practical deployment is severely hindered by noise. While existing error mitigation methods, such as zero-noise extrapolation (ZNE), typically assume static noise, real noisy intermediate-scale quantum (NISQ) systems exhibit dynamic, time-varying noise that remains largely unaddressed. To overcome this critical gap, our work introduces a novel adaptive noise mitigation framework for VQCs that integrates ZNE with contextual multi-armed bandits (CMAB), enabling dynamic, context-aware selection of circuit-folding levels based on ansatz parameters (e.g., depth, parameter count) and the evolving noise environment. Unlike fixed-fold or heuristic ZNE, our approach uses online adaptation to improve the accuracy of ZNE and reduce redundant quantum circuit executions. Our extensive simulations and experiments on real quantum hardware reveal the following important properties: (i) deeper VQCs accumulate noise, degrading accuracy and increasing the number of quantum circuit executions; (ii) ZNE restores estimator fidelity when the folding level is chosen appropriately; and (iii) CMAB-guided folding cuts quantum circuit execution round trips by up to 40%, bytes exchanged by up to 35%, and end-to-end cost by up to 30% under a 10 Mbps budget, with up to 6.9% higher estimator fidelity (CIFAR-10, depth 3, noise band $\eta =0.05$ ), versus fixed-fold and grid-search ZNE. These results demonstrate substantial performance gains over existing noise mitigation methods, underscoring the effectiveness of our design in supporting robust noise mitigation for VQCs. The source code is also publicly released to support reproducibility.
Quantum noise poses a significant challenge for current near-term quantum computing. Quantum error mitigation (QEM) has therefore emerged as a key strategy, offering a practical and effective solution to reduce error impacts and enhance the performance of near-term variational quantum circuits (VQC). Given the lack of a comprehensive survey on this important topic in the literature, this article provides a dedicated overview of QEM, including both during and after the training of VQC. Specifically, during VQC training, we explore and discuss key QEM techniques such as optimal control and dynamical decoupling. For the post-processing stage of VQC, we will examine and discuss mitigation techniques such as zero-noise extrapolation (ZNE) and probabilistic error cancellation (PEC). For each of these QEM techniques, we will investigate the fundamentals, mitigation concepts, and recent advances. Subsequently, we also explore research toolboxes, including Mitiq and Qiskit Aer, as well as provide a case study that demonstrates contextual multi-armed bandit-guided ZNE. Finally, we discuss ongoing problems and future research initiatives, including ML-assisted mitigation and integration with error correction. This survey paper aims to synthesize the state-of-the-art in QEM, offer organized insights across approaches, and propose potential paths towards error-resilient quantum computing.
Ratun Rahman, Dinh C. Nguyen· ACM Computing Surveys· 0 citations
The performance of quantum algorithms on near-term devices is heavily constrained by hardware noise, yet systematic comparisons of algorithmic vulnerability across diverse noise types remain limited. In this study, we benchmark three representative algorithms: quantum teleportation, Grover’s search, and the Quantum Approximate Optimization Algorithm (QAOA), under depolarizing, amplitude damping, phase damping, and thermal relaxation channels. Using exact density-matrix simulations, we characterize algorithmic performance by evaluating state fidelity decay as a function of noise strength. To enable fair, structure-aware comparisons across circuits of varying depths, we introduce a novel metric, the per-gate decay rate. Our analysis reveals that teleportation exhibits the lowest per-gate vulnerability, whereas Grover’s algorithm demonstrates substantially larger, super-linear decay driven by its repeated oracle–diffuser architecture. QAOA displays intermediate and stable noise resilience across system sizes. Finally, we validate our simulation framework through hardware experiments on an IBM Heron processor, demonstrating strong agreement with theoretical predictions (Pearson’s correlation coefficient r = 0.981). This work establishes a quantitative, structure-aware framework for assessing circuit-level noise sensitivity and offers actionable insights for algorithm selection in the noisy intermediate-scale quantum era.
Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale. In practice, however, performance is constrained not only by physical noise but also by the latency of classical decoders processing rapidly generated syndrome data. This challenge is exacerbated by hardware noise that is strong, heterogeneous, and nonstationary, as well as by the simulation-to-hardware distribution shift that can substantially degrade fixed neural decoders. We present QAdapt, a noise-adaptive neural pre-decoding framework for surface-code quantum error correction. QAdapt captures local spatiotemporal correlations in syndrome data, sequentially adapts to evolving noise conditions while mitigating catastrophic forgetting, and forwards the residual syndrome to a conventional global decoder. Across 110 synthetic out-of-distribution noise configurations for rotated surface-code memory circuits, QAdapt consistently reduces the logical error rate relative to the neural pre-decoding baseline. On Google's Willow benchmark data, without target-domain fine-tuning, it achieves reductions of up to 5.79 percent in logical error rate and 9.32 percent in backend decoding latency on the residual syndrome. These results demonstrate that QAdapt provides a practical and decoder-compatible approach to improving the robustness and backend decoding efficiency of quantum error correction under evolving hardware noise.
Ran Miao, Rui Luo, Xiaohan Shan et al.· arXiv.org· 0 citations
Hardware noise has been shown to significantly impact the accuracy of ADAPT-VQE, a ground state preparation algorithm. While previous work has studied the impact of noise on its parameter optimization step, its impact on the critical operator selection step remains comparatively unexplored. In this work, we examine the impact of a variety of noise channels on this step, using a linear H$_3$ molecule as a test case. We show that, despite the selection criterion's natural resilience to some noise, both coherent and incoherent noise can prevent convergence for sufficiently high noise rates. We employ quantum error mitigation techniques--dynamical decoupling, zero noise extrapolation, and Pauli twirling--and show that when combined appropriately, these techniques are capable of restoring a successful convergence profile. Our results highlight how error mitigation can improve the performance of ADAPT-VQE and enable convergence in the presence of hardware noise, offering valuable insights into the implementation of the algorithm on near-term quantum hardware.
Quantum error mitigation relies on accurate noise characterization, but mismatches between the actual and characterized noise can be amplified and drive a sharp threshold between successful and failed mitigation. In random circuits, this threshold maps onto a random-field Ising transition, but previous exact numerics were limited to small one-dimensional and all-to-all systems, leaving explicit two-dimensional architectures unresolved. We develop a fixed-bond-dimension matrix-product-state method for the replicated transfer dynamics that extends threshold calculations beyond exact propagation while retaining the finite-size signatures of the transition. At system sizes beyond previous exact studies, we recover the predicted absence of a threshold for quenched disorder in 1D, obtain a sharper annealed all-to-all critical point, and resolve architecture-dependent finite-depth thresholds in 2D square and heavy-hex circuits. These results establish replicated tensor-network dynamics as a practical tool for probing error-mitigation thresholds in large and higher-dimensional noisy circuits.
Jia-Yao Zhao, Zhi-Yuan Wei, M. Gullans· 0 citations
A novel physics-guided linear mapper for quantum error mitigation that uses seven distinct interpretable features derived from circuit complexity and device calibration data, which reveals that circuit depth and CNOT count dominate error prediction, consistent with decoherence mechanisms.
Tulsi Chaudhari, Krish Bhatia, Shalini Devendrababu et al.· Lecture Notes in Networks an...· 0 citations
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