Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model

• URL: http://arxiv.org/abs/2602.22061v2
• Date: Sun, 01 Mar 2026 07:27:23 GMT
• Title: Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
• Authors: Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo, Hirotaka Oshima,
• Abstract summary: We propose a framework that generates projected ensembles via chaotic Hamiltonian time evolution.<n>This method improves trainability and robustness, broadening the applicability of quantum generative modeling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum physics. Quantum denoising diffusion probabilistic models (QuDDPMs) enable efficient learning of quantum data distributions by progressively scrambling and denoising quantum states; however, existing implementations typically rely on circuit-based random unitary dynamics that can be costly to realize and sensitive to control imperfections, particularly on analog quantum hardware. We propose the chaotic quantum diffusion model, a framework that generates projected ensembles via chaotic Hamiltonian time evolution, providing a flexible and hardware-compatible diffusion mechanism. Requiring only global, time-independent control, our approach substantially reduces implementation overhead across diverse analog quantum platforms while achieving accuracy comparable to QuDDPMs. This method improves trainability and robustness, broadening the applicability of quantum generative modeling.

Related papers

• Mitigating Barren plateaus in quantum denoising diffusion probabilistic models [49.90716699848553]
  Quantum generative models leverage quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data.<n>QuDDPM has been proposed as a promising framework for quantum generative learning.<n>We show that barren plateaus emerge in QuDDPMs due to the use of 2-design states as the input for the denoising process.<n>We introduce an improved QuDDPM that utilizes a distribution maintaining a certain distance from the Haar distribution, ensuring better trainability.
  arXiv  Detail & Related papers  (2025-12-07T07:01:44Z)
• Quantum Latent Diffusion Models [65.16624577812436]
  We propose a potential version of a quantum diffusion model that leverages the established idea of classical latent diffusion models.<n>This involves using a traditional autoencoder to reduce images, followed by operations with variational circuits in the latent space.<n>The results demonstrate an advantage in using a quantum version, as evidenced by obtaining better metrics for the images generated by the quantum version.
  arXiv  Detail & Related papers  (2025-01-19T21:24:02Z)
• Learning agent-based approach to the characterization of open quantum systems [0.08496348835248901]
  We introduce the open Quantum Model Learning Agent (oQMLA) framework to account for Markovian noise through the Liouvillian formalism.<n>By simultaneously learning the Hamiltonian and jump operators, oQMLA independently captures both the coherent and incoherent dynamics of a system.<n>We validate our implementation in simulated scenarios of increasing complexity, demonstrating its robustness to hardware-induced measurement errors.
  arXiv  Detail & Related papers  (2025-01-09T16:25:17Z)
• Dissipation-induced Quantum Homogenization for Temporal Information Processing [44.99833362998488]
  Quantum reservoirs have great potential as they utilize the complex real-time dissipative dynamics of quantum systems for information processing and target time-series generation without precise control or fine-tuning of the Hamiltonian parameters.<n>We propose the disordered quantum homogenizer as an alternative platform, and prove it satisfies the necessary and sufficient conditions - stability and contractivity - of the reservoir dynamics.<n>The results indicate that the quantum homogenization protocol, physically implementable as either nuclear magnetic resonance ensemble or a photonic system, can potentially function as a reservoir computer.
  arXiv  Detail & Related papers  (2024-12-13T09:05:41Z)
• Mixed-State Quantum Denoising Diffusion Probabilistic Model [0.40964539027092906]
  We propose a mixed-state quantum denoising diffusion probabilistic model (MSQuDDPM) to eliminate the need for scrambling unitaries.<n>MSQuDDPM integrates depolarizing noise channels in the forward diffusion process and parameterized quantum circuits with projective measurements in the backward denoising steps.<n>We evaluate MSQuDDPM on quantum ensemble generation tasks, demonstrating its successful performance.
  arXiv  Detail & Related papers  (2024-11-26T17:20:58Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Generative quantum machine learning via denoising diffusion probabilistic models [17.439525936236166]
  We propose the quantum denoising diffusion probabilistic model (QuDDPM) to enable efficiently trainable generative learning of quantum data. We provide bounds on the learning error and demonstrate QuDDPM's capability in learning correlated quantum noise model, quantum many-body phases, and topological structure of quantum data.
  arXiv  Detail & Related papers  (2023-10-09T17:03:08Z)
• 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)
• 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)
• Entanglement transfer, accumulation and retrieval via quantum-walk-based qubit-qudit dynamics [50.591267188664666]
  Generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies. We propose a protocol that is able to attain entangled states of $d$-dimensional systems through a quantum-walk-based it transfer & accumulate mechanism. In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons.
  arXiv  Detail & Related papers  (2020-10-14T14:33:34Z)

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.