A regularization method for quantum neural networks using data symmetry

• URL: http://arxiv.org/abs/2607.03805v1
• Date: Sat, 04 Jul 2026 10:25:07 GMT
• Title: A regularization method for quantum neural networks using data symmetry
• Abstract summary: Leveraging data symmetries has recently become a key strategy in quantum neural networks (QNNs) to improve generalization and training efficiency.<n>We propose a novel regularization method for QNNs based on input data symmetry.<n>We validate its effectiveness through numerical experiments on both classification tasks and quantum generative adversarial networks.
• Score: 1.7767466724342065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging data symmetries has recently become a key strategy in quantum neural networks (QNNs) to improve generalization and training efficiency. In this study, we propose a novel regularization method for QNNs based on input data symmetry. By introducing a penalty term that encourages the model to align with data symmetry, our method enables improved training speed and generalization. This symmetry-based regularization is simple to implement and does not require prior knowledge of the symmetry group. We validate its effectiveness through numerical experiments on both classification tasks and quantum generative adversarial networks. Empirical results demonstrate faster convergence and lower test errors. Furthermore, we provide a theoretical generalization bound using Rademacher complexity and conjecture a condition under which models with symmetry exhibit better generalization. Our findings highlight the potential of symmetry-aware regularization in enhancing the performance of QML models.

Related papers

• Reducing Symmetry Increase in Equivariant Neural Networks [21.62431626298374]
  We provide a detailed and in-depth characterization of symmetry increase together with a principled framework for its reduction.<n>We propose practical guidelines for feature design to prevent harmful symmetry increases.<n>We provide visualizations and experiments on both synthetic datasets and the real-world QM9 dataset.
  arXiv  Detail & Related papers  (2026-08-12T12:46:25Z)
• Aligning Network Equivariance with Data Symmetry: A Theoretical Framework and Adaptive Approach for Image Restoration [71.51826063983434]
  Image restoration is an inherently ill posed inverse problem.<n>We use non strict symmetry as a constraint to formulate the restoration inverse problem.<n>We show that the equivariance error of the optimal restoration operator is strictly bounded by the data symmetry error and the discretization mesh size.
  arXiv  Detail & Related papers  (2026-05-13T16:22:19Z)
• Geometric Quantum Physics Informed Neural Network [0.0]
  We introduce geometric quantum physics-informed neural networks (GQPINNs) in which the geometric structure of the underlying PDE is incorporated directly into the quantum-circuit ansatz.<n>GQPINNs achieve improved solution accuracy, as quantified by lower mean absolute error, while requiring substantially fewer trainable parameters.<n>These results identify symmetry-aware quantum-circuit design as an effective route toward improved efficiency and generalization in quantum PDE solvers.
  arXiv  Detail & Related papers  (2026-05-04T08:55:29Z)
• Reinforcement Learning Using known Invariances [54.91261509214309]
  This paper develops a theoretical framework for incorporating known group symmetries into kernel-based reinforcement learning.<n>We show that symmetry-aware RL achieves significantly better performance than their standard kernel counterparts.
  arXiv  Detail & Related papers  (2025-11-05T13:56:14Z)
• Learning Broken Symmetries with Approximate Invariance [1.0485739694839669]
  In many cases, the exact underlying symmetry is present only in an idealized dataset, and is broken in actual data.<n>Standard approaches, such as data augmentation or equivariant networks fail to represent the nature of the full, broken symmetry.<n>We propose a learning model which balances the generality and performance of unconstrained networks with the rapid learning of constrained networks.
  arXiv  Detail & Related papers  (2024-12-25T04:29:04Z)
• The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof [50.49582712378289]
  We investigate the impact of neural parameter symmetries by introducing new neural network architectures. We develop two methods, with some provable guarantees, of modifying standard neural networks to reduce parameter space symmetries. Our experiments reveal several interesting observations on the empirical impact of parameter symmetries.
  arXiv  Detail & Related papers  (2024-05-30T16:32:31Z)
• Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks [79.16635054977068]
  We present a new class of equivariant neural networks, dubbed Lattice-Equivariant Neural Networks (LENNs) Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators. Our work opens towards practical utilization of machine learning-augmented Lattice Boltzmann CFD in real-world simulations.
  arXiv  Detail & Related papers  (2024-05-22T17:23:15Z)
• Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks [2.1797442801107056]
  Undirected Weighted Network (UWN) is commonly found in big data-related applications. Existing models fail in either modeling its intrinsic symmetry or low-data density. Proximal Symmetric Nonnegative Latent-factor-analysis model is proposed.
  arXiv  Detail & Related papers  (2023-06-06T13:03:24Z)
• Theory for Equivariant Quantum Neural Networks [0.0]
  We present a theoretical framework to design equivariant quantum neural networks (EQNNs) for essentially any relevant symmetry group. Our framework can be readily applied to virtually all areas of quantum machine learning.
  arXiv  Detail & Related papers  (2022-10-16T15:42:21Z)
• Symmetric Pruning in Quantum Neural Networks [111.438286016951]
  Quantum neural networks (QNNs) exert the power of modern quantum machines. QNNs with handcraft symmetric ansatzes generally experience better trainability than those with asymmetric ansatzes. We propose the effective quantum neural tangent kernel (EQNTK) to quantify the convergence of QNNs towards the global optima.
  arXiv  Detail & Related papers  (2022-08-30T08:17:55Z)
• Sampling asymmetric open quantum systems for artificial neural networks [77.34726150561087]
  We present a hybrid sampling strategy which takes asymmetric properties explicitly into account, achieving fast convergence times and high scalability for asymmetric open systems. We highlight the universal applicability of artificial neural networks, underlining the universal applicability of neural networks.
  arXiv  Detail & Related papers  (2020-12-20T18:25:29Z)

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.