Backend-Aware Graph Learning for Denoising Outcome Distributions in Quantum Program Testing

• URL: http://arxiv.org/abs/2607.23211v1
• Date: Sat, 25 Jul 2026 14:02:15 GMT
• Title: Backend-Aware Graph Learning for Denoising Outcome Distributions in Quantum Program Testing
• Abstract summary: We present Q-BRIDGE, a graph learning-based approach that converts noisy observations into denoised distributions.<n>Q-BRIDGE achieves 93.97%-94.90% precision and 82.50%-83.51% recall in detecting bug-induced test failures.
• Score: 9.198836693023104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Testing quantum programs on NISQ (Noisy Intermediate-Scale Quantum) backends is challenging because the noise disturbs outcome distributions and can affect pass/fail decisions. We present Q-BRIDGE, a graph learning-based approach that converts noisy observations into denoised distributions suitable for oracle-based verification. Q-BRIDGE uses a graph transformer architecture to encode a transpiled quantum circuit, capturing the characteristics of its gates and their connectivity; the physical backend information is encoded together with the logical structure of the circuit. An additional conditioning layer, based on FiLM (Feature-Wise Linear Modulation), takes the encoding as input and integrates noisy observations to produce denoised outcomes. We evaluate Q-BRIDGE on 23 IBM noise backends and 6 circuit families representative of practical workloads. In the first setting, we train a separate Q-BRIDGE model for each backend; in the second setting, we train a single general model shared across all backends. Across both settings, Q-BRIDGE outperforms the state-of-the-art baseline in noise mitigation by a large margin. In testing scenarios with noisy executions, Q-BRIDGE achieves 93.97%-94.90% precision and 82.50%-83.51% recall in detecting bug-induced test failures, significantly outperforming the state-of-the-art baseline. These results indicate that considering the graph structure of the transpiled circuits and the physical characteristics of specific quantum backends is a practical route to more reliable noise-aware quantum program testing.

Related papers

• Fault-Tolerant Logical Operations and Efficient State Preparation in Modular Quantum Architectures with Noisy Interfaces [0.12314765641075438]
  We show that interfaces can tolerate noise up to an order of magnitude higher than intra-QPU noise, with only a minor reduction in the fault-tolerance threshold.<n>We develop a protocol for preparing fault-tolerant logical GHZ states, reducing overhead, time, and nonlocal Bellpair consumption.<n>Our results provide quantitative evidence that distributed quantum error can enable scalable, fault-tolerant quantum computation in modular architectures.
  arXiv  Detail & Related papers  (2026-07-29T17:59:57Z)
• GroverFigureOfMerit: An Agnostic Figure of Merit for Quantum Backend Characterization in the NISQ Era [41.99844472131922]
  We propose a Figure of Merit based on Grover's algorithm as an algorithmic stress test evaluating quantum backend performance holistically.<n>The metric combines success probability on target states with penalties for non-uniform amplification and leakage to non-marked states, yielding a unified score across hardware architectures.
  arXiv  Detail & Related papers  (2026-07-09T16:08:48Z)
• Demonstrating Noise-adapted Quantum Error Correction With Break-Even Performance [2.7568215535428937]
  We develop a noise-adapted 3-qubit quantum error correction scheme for IBM quantum hardware.<n>We show that the scheme can break-even against native amplitude-damping (AD) noise.<n>Our analysis suggests that the performance of our protocol is limited primarily by the measurement readout fidelity.
  arXiv  Detail & Related papers  (2026-03-04T19:50:16Z)
• 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)
• 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)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates [2.271697926182248]
  We present an automated framework that discovers and refines variational quantum circuits.<n>Circuits are represented as graphs and mutated and selected via an expected improvement acquisition function.<n>The GNN-guided pipeline consistently finds circuits with lower complexity and competitive or superior classification accuracy.
  arXiv  Detail & Related papers  (2025-12-10T12:23:04Z)
• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.<n>We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.<n>We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• 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)
• Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks [0.6062751776009752]
  The fidelity of quantum circuits (QC) is influenced by several factors, including hardware characteristics, calibration status, and the transpilation process.<n>Q-fid is introduced, a Long Short-Term Memory (LSTM) based fidelity prediction system accompanied by a novel metric designed to quantify the fidelity of quantum circuits.<n>Q-fid achieves a high prediction accuracy with an average RMSE of 0.0515, up to 24.7x more accurate than the Qiskit transpile tool mapomatic.
  arXiv  Detail & Related papers  (2023-03-30T16:44:12Z)
• Measuring NISQ Gate-Based Qubit Stability Using a 1+1 Field Theory and Cycle Benchmarking [50.8020641352841]
  We study coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application. We identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor. This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
  arXiv  Detail & Related papers  (2022-01-08T23:12:55Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.