Enhancing Quantum Machine Learning with Anyons

• URL: http://arxiv.org/abs/2606.16090v1
• Date: Mon, 15 Jun 2026 01:13:04 GMT
• Title: Enhancing Quantum Machine Learning with Anyons
• Abstract summary: We introduce a quantum kernel framework that unifies bosonic, fermionic, and anyonic exchange statistics.<n>Our work identifies particle exchange statistics as an overlooked computational ingredient for quantum machine learning.
• Score: 5.881710760997403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The power of quantum computing and quantum machine learning relies on harnessing uniquely quantum phenomena as computational resources. While superposition, coherence and entanglement have been central to this effort, the role of particle exchange statistics remains largely unexplored. Here, we introduce a quantum kernel framework that unifies bosonic, fermionic, and anyonic (fractional) exchange statistics within a single learning paradigm. We study this family of kernels from three perspectives. At the representation level, Haar-averaged effective-dimension analysis shows that fractional exchange phases access feature-space directions inaccessible to the purely symmetric or antisymmetric limits. At the level of kernel geometry, the corresponding Gram matrices show greater separation from the distinguishable-particle baseline and reduced label-dependent model complexity. Finally, on learning benchmarks, anyonic kernels consistently outperform their bosonic and fermionic counterparts, with stronger target alignment and more favorable class geometry. Together, these findings show that exchange statistics reshape the structure and geometry of quantum feature space, leading to enhanced learning performance. Our work identifies particle exchange statistics as an overlooked computational ingredient for quantum machine learning and provides the first systematic comparison of quantum learning models across exchange phases.

Related papers

• Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study [22.101976874889147]
  We present the first comprehensive empirical study of machine unlearning (MU) in hybrid quantum-classical neural networks.<n>We adapt a broad suite of unlearning methods to quantum settings, including gradient-based, distillation-based, regularization-based and certified techniques.<n>We find that quantum models can support effective unlearning, but outcomes depend strongly on circuit depth, entanglement structure, and task complexity.
  arXiv  Detail & Related papers  (2025-12-22T10:40:03Z)
• Digitized Counterdiabatic Quantum Feature Extraction [35.670314643295036]
  We introduce a Hamiltonian-based quantum feature extraction method that generates complex features via the dynamics of $k$-local many-body spins Hamiltonians.<n>We assess the approach on high-dimensional, real-world datasets, including molecular toxicity classification and image recognition.<n>The results suggest that combining quantum and classical feature extraction can provide consistent improvements across diverse machine learning tasks.
  arXiv  Detail & Related papers  (2025-10-15T17:59:35Z)
• Benign Overfitting with Quantum Kernels [5.499796332553708]
  Quantum kernels quantify similarity between data points by measuring the inner product between quantum states.<n>We propose a novel strategy for constructing quantum kernels that achieve good generalization performance.
  arXiv  Detail & Related papers  (2025-03-21T10:30:42Z)
• Large-scale quantum reservoir learning with an analog quantum computer [45.21335836399935]
  We develop a quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning.
  arXiv  Detail & Related papers  (2024-07-02T18:00:00Z)
• Enhanced Entanglement in the Measurement-Altered Quantum Ising Chain [43.80709028066351]
  Local quantum measurements do not simply disentangle degrees of freedom, but may actually strengthen the entanglement in the system.<n>This paper explores how a finite density of local measurement modifies a given state's entanglement structure.
  arXiv  Detail & Related papers  (2023-10-04T09:51:00Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning [52.77024349608834]
  Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. Key issue is how to address the inherent non-linearity of classical deep learning. We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
  arXiv  Detail & Related papers  (2022-12-22T16:06:24Z)
• Complete characterization of quantum correlations by randomized measurements [0.832184180529969]
  We provide a method to measure any locally invariant property of quantum states using locally randomized measurements. We implement these methods experimentally using pairs of entangled photons, characterizing their usefulness for quantum teleportation. Our results can be applied to various quantum computing platforms, allowing simple analysis of correlations between arbitrary distant qubits.
  arXiv  Detail & Related papers  (2022-12-15T15:22:28Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• The theory of the quantum kernel-based binary classifier [0.8057006406834467]
  kernel method that exploit quantum interference and feature quantum Hilbert space opened up tremendous opportunities for quantum-enhanced machine learning. This work extends the general theory of quantum kernel-based classifiers. Focusing on the squared overlap between quantum states as a similarity measure, the essential and minimal ingredients for the quantum binary classification are examined. The validity of the Hilbert-Schmidt inner product, which becomes the squared overlap for pure states, as a positive definite and symmetric kernel is explicitly shown.
  arXiv  Detail & Related papers  (2020-04-07T15:39:36Z)

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.