Trainable Quantum Channels as Computational Primitives for Quantum Learning

• URL: http://arxiv.org/abs/2606.15808v1
• Date: Sun, 14 Jun 2026 13:19:49 GMT
• Title: Trainable Quantum Channels as Computational Primitives for Quantum Learning
• Abstract summary: Variational quantum learning is traditionally constrained to unitary dynamics.<n>We establish a non-unitary quantum machine learning framework grounded in open-system dynamics.
• Score: 2.047424180164312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Variational quantum learning is traditionally constrained to unitary dynamics, often treating quantum channels as detrimental noise. In this work, we reformulate the quantum channels as trainable computational primitives and establish a non-unitary quantum machine learning framework grounded in open-system dynamics. We demonstrate that the outputs of channel-enhanced quantum models form a structured superposition of multiple functional components. Each component is governed by an effective observable whose spectrum can be adaptively modulated during training, a significant departure from the spectral invariance in unitary transformations. Moreover, the proposed framework generalizes conventional unitary quantum models by retaining them as a special case while introducing additional non-unitary degrees of freedom. Furthermore, we reveal that trainable quantum channels enrich the optimization geometry through ensemble-averaged gradient and additional optimization directions induced by the Kraus operators. Empirical evaluations on classification tasks using trainable amplitude-damping and phase-damping channels confirm enhanced optimization dynamics and predictive performance. Our work provides a principled approach for leveraging quantum channels as trainable resources and advances the design of high-performance quantum learning architectures.

Related papers

• Learnable yet not simulable: a quantum resource theory of learning models [57.695822250391906]
  It remains unclear which quantum resource governs the classical learnability of quantum circuits.<n>We study the expectation-value functions of families of tunable quantum circuits.<n>These results establish a quantitative resource-theoretic framework for delineating the boundary between classical simulation and learning.
  arXiv  Detail & Related papers  (2026-08-03T14:48:03Z)
• Compositional Quantum Heuristics for Max-Clique Detection [0.0]
  In general, circuits that are sufficiently expressive to model complex behaviour often exhibit barren plateaus.<n>In this work we investigate a compositional approach to mitigate this trade-off by assembling larger quantum models from smaller subcomponents.<n>We use this framework to design permutation-equivariant quantum graph neural networks for identifying maximal cliques in graphs.
  arXiv  Detail & Related papers  (2026-05-08T11:38:57Z)
• Efficient training of photonic quantum generative models [0.0]
  This paper exploits the properties of intermediate-complexity circuits whose training can be simulated classically efficiently.<n>We propose an efficient training procedure for photon-native quantum generative models based on the maximum mean discrepancy.
  arXiv  Detail & Related papers  (2026-03-09T18:00:11Z)
• Quantum LEGO Learning: A Modular Design Principle for Hybrid Artificial Intelligence [63.39968536637762]
  We introduce Quantum LEGO Learning, a learning framework that treats classical and quantum components as reusable, composable learning blocks.<n>Within this framework, a pre-trained classical neural network serves as a frozen feature block, while a VQC acts as a trainable adaptive module.<n>We develop a block-wise generalization theory that decomposes learning error into approximation and estimation components.
  arXiv  Detail & Related papers  (2026-01-29T14:29:21Z)
• Multi-channel convolutional neural quantum embedding [10.620759775107787]
  We introduce a classical-quantum hybrid approach for optimizing quantum embedding beyond the limitations of the standard circuit model of quantum computation.<n>We benchmark the performance of various models in our framework using the CIFAR-10 and Tiny ImageNet datasets.
  arXiv  Detail & Related papers  (2025-09-26T13:51:40Z)
• Quantum Adaptive Excitation Network with Variational Quantum Circuits for Channel Attention [0.2812395851874055]
  We introduce the Quantum Adaptive Excitation Network (QAE-Net)<n>QAE-Net is a hybrid quantum-classical framework designed to enhance channel attention mechanisms in Convolutional Neural Networks (CNNs)
  arXiv  Detail & Related papers  (2025-07-15T11:40:37Z)
• 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)
• Flowing Through Hilbert Space: Quantum-Enhanced Generative Models for Lattice Field Theory [0.9208007322096533]
  We develop a hybrid quantum-classical normalizing flow model to explore quantum-enhanced sampling in such regimes.<n>Our approach embeds parameterized quantum circuits within a classical normalizing flow architecture, leveraging amplitude encoding and quantum entanglement to enhance expressivity in the generative process.
  arXiv  Detail & Related papers  (2025-05-15T17:58:16Z)
• 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)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)

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.