Formal Verification of Noisy Quantum Reinforcement Learning Policies

• URL: http://arxiv.org/abs/2512.01502v1
• Date: Mon, 01 Dec 2025 10:26:33 GMT
• Title: Formal Verification of Noisy Quantum Reinforcement Learning Policies
• Authors: Dennis Gross,
• Abstract summary: Quantum reinforcement learning (QRL) aims to use quantum effects to create sequential decision-making policies.<n> QRL policies face uncertainty from quantum measurements and hardware noise, such as bit-flip, phase-flip, and depolarizing errors.<n>We introduce QVerifier, a formal verification method that applies probabilistic model checking to analyze trained QRL policies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum reinforcement learning (QRL) aims to use quantum effects to create sequential decision-making policies that achieve tasks more effectively than their classical counterparts. However, QRL policies face uncertainty from quantum measurements and hardware noise, such as bit-flip, phase-flip, and depolarizing errors, which can lead to unsafe behavior. Existing work offers no systematic way to verify whether trained QRL policies meet safety requirements under specific noise conditions. We introduce QVerifier, a formal verification method that applies probabilistic model checking to analyze trained QRL policies with and without modeled quantum noise. QVerifier builds a complete model of the policy-environment interaction, incorporates quantum uncertainty directly into the transition probabilities, and then checks safety properties using the Storm model checker. Experiments across multiple QRL environments show that QVerifier precisely measures how different noise models influence safety, revealing both performance degradation and cases where noise can help. By enabling rigorous safety verification before deployment, QVerifier addresses a critical need: because access to quantum hardware is expensive, pre-deployment verification is essential for any safety-critical use of QRL. QVerifier targets a potential classical-quantum sweet spot: trained QRL policies that execute efficiently on quantum hardware, yet remain tractable for classical probabilistic model checking despite being too slow for real-time classical deployment.

Related papers

• Noise-Resistant Feature-Aware Attack Detection Using Quantum Machine Learning [43.509065918669314]
  Continuous-variable quantum key distribution (CV-QKD) is a quantum communication technology that offers an unconditional security guarantee.<n>We propose a quantum machine learning (QML)-based attack detection framework (QML-ADF) that safeguards the security of high-rate CV-QKD systems.
  arXiv  Detail & Related papers  (2026-01-11T03:40:45Z)
• 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)
• Adversarial Limits of Quantum Certification: When Eve Defeats Detection [0.0]
  Security of quantum key distribution relies on certifying that observed correlations arise from genuine quantum entanglement.<n>Eve GAN is a generative adversarial network trained to produce classical correlations indistinguishable from quantum.<n>We find that same distribution calibration a common practice in prior certification studies inflates detection performance by 44 percentage points.
  arXiv  Detail & Related papers  (2025-12-04T02:24:04Z)
• Differentially Private Federated Quantum Learning via Quantum Noise [9.540961602976965]
  Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange.<n>QFL remains vulnerable to adversarial attacks, where shared QML model updates can be exploited to undermine information privacy.<n>This paper explores a novel DP mechanism that harnesses quantum noise to safeguard quantum models throughout the QFL process.
  arXiv  Detail & Related papers  (2025-08-27T22:56:16Z)
• First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies [22.395468970799993]
  This paper investigates the integration of quantum computing into offline reinforcement learning.<n>The goal is to evaluate the potential of deploying quantum architectures in real-world industrial control problems.
  arXiv  Detail & Related papers  (2025-08-03T09:50:40Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• Bayesian Quantum Amplitude Estimation [46.03321798937855]
  We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation.<n>In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt.<n>We propose a benchmark for amplitude estimation algorithms and use it to test BAE against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Instantaneous Quantum Polynomial-Time Sampling and Verifiable Quantum Advantage: Stabilizer Scheme and Classical Security [1.5647673631415648]
  We introduce a family of IQP sampling protocols called the stabilizer scheme.<n>We also introduce the Hidden Structured Code (HSC) problem as a well-defined mathematical challenge.
  arXiv  Detail & Related papers  (2023-08-14T14:03:33Z)
• Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning [47.991114317813555]
  Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots. This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
  arXiv  Detail & Related papers  (2023-04-06T22:05:21Z)
• 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.