Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision

• URL: http://arxiv.org/abs/2601.18058v1
• Date: Mon, 26 Jan 2026 01:21:44 GMT
• Title: Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision
• Authors: Mohamed Afane, Quanjiang Long, Haoting Shen, Ying Mao, Junaid Farooq, Ying Wang, Juntao Chen,
• Abstract summary: Current quantum neural networks suffer from extreme sensitivity to both adversarial perturbations and hardware noise.<n>We propose a hybrid quantum-classical Differentiable Quantum Architecture Search (DQAS) framework that addresses these limitations.
• Score: 6.837371200793778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current quantum neural networks suffer from extreme sensitivity to both adversarial perturbations and hardware noise, creating a significant barrier to real-world deployment. Existing robustness techniques typically sacrifice clean accuracy or require prohibitive computational resources. We propose a hybrid quantum-classical Differentiable Quantum Architecture Search (DQAS) framework that addresses these limitations by jointly optimizing circuit structure and robustness through gradient-based methods. Our approach enhances traditional DQAS with a lightweight Classical Noise Layer applied before quantum processing, enabling simultaneous optimization of gate selection and noise parameters. This design preserves the quantum circuit's integrity while introducing trainable perturbations that enhance robustness without compromising standard performance. Experimental validation on MNIST, FashionMNIST, and CIFAR datasets shows consistent improvements in both clean and adversarial accuracy compared to existing quantum architecture search methods. Under various attack scenarios, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Basic Iterative Method (BIM), and Momentum Iterative Method (MIM), and under realistic quantum noise conditions, our hybrid framework maintains superior performance. Testing on actual quantum hardware confirms the practical viability of discovered architectures. These results demonstrate that strategic classical preprocessing combined with differentiable quantum architecture optimization can significantly enhance quantum neural network robustness while maintaining computational efficiency.

Related papers

• Noise-Aware Quantum Architecture Search Based on NSGA-II Algorithm [6.42663482584159]
  We propose a noise-aware quantum architecture search (NA-QAS) framework based on variational quantum circuit design.<n>We introduce a hybrid Hamiltonian $varepsilon$ -greedy strategy to optimize evaluation costs.<n>An enhanced variable-depth NSGA-II algorithm is employed to navigate the vast search space.
  arXiv  Detail & Related papers  (2026-01-16T03:11:34Z)
• 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)
• QuProFS: An Evolutionary Training-free Approach to Efficient Quantum Feature Map Search [19.355532507267075]
  We propose an evolutionary training-free quantum architecture search (QAS) framework that employs circuit-baseds focused on trainability, hardware robustness, generalisation ability, expressivity, complexity, and kernel-target alignment.<n>By ranking circuit architectures with various proxies, we reduce evaluation costs and incorporate hardware-aware circuits to enhance robustness against noise.<n>Our approach demonstrates competitive accuracy on both simulators and real quantum hardware, surpassing state-of-the-art QAS methods in terms of sampling efficiency and achieving up to a 2x speedup in architecture search runtime.
  arXiv  Detail & Related papers  (2025-08-09T21:17:24Z)
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
  We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• Resource-Efficient Hadamard Test Circuits for Nonlinear Dynamics on a Trapped-Ion Quantum Computer [1.2063443893298391]
  We propose a low-depth implementation of a class of Hadamard test circuits.<n>We develop a parameterized quantum ansatz specifically tailored for variational algorithms.<n>Our findings demonstrate a significant reduction in single- and two-qubit gate counts.
  arXiv  Detail & Related papers  (2025-07-25T13:16:54Z)
• 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)
• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
  Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• 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)
• Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation [4.358861563008207]
  Quantum neural networks (QNNs) have shown promise both empirically and theoretically.<n> Hardware imperfections and limited access to quantum devices pose practical challenges.<n>We propose an automated solution using differentiable optimization.
  arXiv  Detail & Related papers  (2025-05-13T19:01:08Z)
• Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search [14.288836269941207]
  Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. We decompose the search process into dynamic alternating phases of coarse and fine-grained knowledge learning. QUEST-A develops an evolutionary mechanism with knowledge accumulation and reuse to enhance multi-level knowledge transfer.
  arXiv  Detail & Related papers  (2024-10-31T01:57:14Z)
• Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures [1.8781124875646162]
  This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based approach for efficient quantum circuit compilation and mapping. In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn theses for a specific multi-core architecture.
  arXiv  Detail & Related papers  (2024-06-17T12:09:11Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• 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.