Channel-Constrained Markovian Quantum Diffusion Model from Open System Perspective
- URL: http://arxiv.org/abs/2511.12221v1
- Date: Sat, 15 Nov 2025 13:52:15 GMT
- Title: Channel-Constrained Markovian Quantum Diffusion Model from Open System Perspective
- Authors: Qin-Sheng Zhu, Geng Chen, Lian-Hui Yu, Xiaodong Xing, Xiao-Yu Li,
- Abstract summary: We present a channel-constrained Markovian quantum diffusion model that prepares quantum states.<n>Our model interprets the forward diffusion process as natural decoherence using quantum master equations, whereas the reverse denoising is achieved by learning inverse quantum channels.<n>This work confirms that quantum diffusion can be characterized as a controlled Markov evolution, demonstrating that environmental interactions are not limited to being a source of decoherence but can also be utilized to achieve high-fidelity quantum state synthesis.
- Score: 6.550541885221236
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a channel-constrained Markovian quantum diffusion (CCMQD) model that prepares quantum states by rigorously framing the generative process within the dynamics of open quantum systems. Our model interprets the forward diffusion process as natural decoherence using quantum master equations, whereas the reverse denoising is achieved by learning inverse quantum channels. Our core innovation is a comprehensive channel-constrained framework: we model the diffusion and denoising steps as quantum channels defined by Kraus operators, ensure their physical validity through optimization on the Stiefel manifold, and introduce tailored training strategies and loss functions that leverage this constrained structure for high-fidelity state reconstruction. Experimental validation on systems ranging from single qubits to entangled states $7$ -qubits demonstrates high-fidelity state generation, achieving fidelities exceeding $0.998$ under both random and depolarizing noise conditions. This work confirms that quantum diffusion can be characterized as a controlled Markov evolution, demonstrating that environmental interactions are not limited to being a source of decoherence but can also be utilized to achieve high-fidelity quantum state synthesis.
Related papers
- Universal classical and quantum fluctuations in the large deviations of current of noisy quantum systems: The case of QSSEP and QSSIP [2.035631599424874]
We study the fluctuation statistics of integrated currents in noisy quantum diffusive systems.<n>We show that the cumulant generating function of the integrated current, at large scales, obeys a large deviation principle.<n>We identify the leading finite-size corrections to the current statistics.
arXiv Detail & Related papers (2026-01-23T16:45:31Z) - 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) - Crosstalk-Resilient Quantum MIMO for Scalable Quantum Communications [40.44880302154388]
Crosstalk arises when physically coupled quantum modes interfere, degrading signal fidelity.<n>We propose a mitigation strategy based on encoding discrete-variable quantum information into continuous-variable modes.<n>We prove the existence of a gauge-fixing decoder enabling recovery of the logical information.
arXiv Detail & Related papers (2025-06-26T18:40:26Z) - Continuous-variable Quantum Diffusion Model for State Generation and Restoration [3.3864018929063477]
This paper introduces a novel framework based on continuous-variable quantum diffusion principles, synergizing them with CV quantum neural networks (CVQNNs)<n>For the task of state generation, our Continuous-Variable Quantum Diffusion Generative model (CVQD-G) employs a physically driven forward diffusion process using a thermal loss channel.<n>For state recovery, our specialized variant designed to restore quantum states, particularly coherent states with unknown parameters, from thermal degradation.
arXiv Detail & Related papers (2025-06-24T03:04:21Z) - A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
arXiv Detail & Related papers (2025-06-12T11:00:21Z) - 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) - 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 multimode conditional quantum Entropy Power Inequality and the squashed entanglement of the multimode extreme bosonic Gaussian channels [53.253900735220796]
Inequality determines the minimum conditional von Neumann entropy of the output of the most general linear mixing of bosonic quantum modes.<n>Bosonic quantum systems constitute the mathematical model for the electromagnetic radiation in the quantum regime.
arXiv Detail & Related papers (2024-10-18T13:59:50Z) - Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble [40.06696963935616]
We introduce Quantum Generative Diffusion Model (QGDM) as their simple and elegant quantum counterpart.
QGDM exhibits faster convergence than Quantum Generative Adversarial Network (QGAN)
It can achieve 53.02% higher fidelity in mixed-state generation than QGAN.
arXiv Detail & Related papers (2024-01-13T10:56:34Z) - Variational method for learning Quantum Channels via Stinespring Dilation on neutral atom systems [0.0]
We propose a method to approximate an arbitrary target quantum channel by variationally constructing equivalent unitary operations on an extended system.<n>We also present an experimentally feasible approach to extrapolate the quantum channel in discrete time steps beyond the period covered by the training data.
arXiv Detail & Related papers (2023-09-19T13:06:44Z) - Neural-network quantum states for ultra-cold Fermi gases [49.725105678823915]
This work introduces a novel Pfaffian-Jastrow neural-network quantum state that includes backflow transformation based on message-passing architecture.
We observe the emergence of strong pairing correlations through the opposite-spin pair distribution functions.
Our findings suggest that neural-network quantum states provide a promising strategy for studying ultra-cold Fermi gases.
arXiv Detail & Related papers (2023-05-15T17:46:09Z) - Entangling Quantum Generative Adversarial Networks [53.25397072813582]
We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN)
We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
arXiv Detail & Related papers (2021-04-30T20:38:41Z) - Experimental Realization of Nonadiabatic Holonomic Single-Qubit Quantum
Gates with Two Dark Paths in a Trapped Ion [41.36300605844117]
We show nonadiabatic holonomic single-qubit quantum gates on two dark paths in a trapped $171mathrmYb+$ ion based on four-level systems with resonant drives.
We find that nontrivial holonomic two-qubit quantum gates can also be realized within current experimental technologies.
arXiv Detail & Related papers (2021-01-19T06:57:50Z)
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.