Generative modeling with Gaussian Boson Sampling: classically trainable Bosonic Born Machines

• URL: http://arxiv.org/abs/2603.11195v1
• Date: Wed, 11 Mar 2026 18:04:11 GMT
• Title: Generative modeling with Gaussian Boson Sampling: classically trainable Bosonic Born Machines
• Abstract summary: We introduce a photonic quantum generative model built on parametrized Gaussian Boson Sampling circuits.<n>The training is based on the efficient classical evaluation of expectation values enabled by the Gaussian structure of the state.<n>We demonstrate the effectiveness of the approach through numerical experiments on photonic systems with up to 805 modes and over a million trainable parameters.
• Score: 0.1449880056570423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum generative modeling has emerged as a promising application of quantum computers, aiming to model complex probability distributions beyond the reach of classical methods. In practice, however, training such models often requires costly gradient estimation performed directly on the quantum hardware. Crucially, for certain structured quantum circuits, expectation values of local observables can be efficiently evaluated on a classical computer, enabling classical training without calls to the quantum hardware in the optimization loop. In these models, sampling from the resulting circuits can still be classically hard, so inference must be performed on a quantum device, yielding a potential computational advantage. In this work, we introduce a photonic quantum generative model built on parametrized Gaussian Boson Sampling circuits. The training is based on the efficient classical evaluation of expectation values enabled by the Gaussian structure of the state, allowing scalable optimization of the model parameters through the maximum mean discrepancy loss function. We demonstrate the effectiveness of the approach through numerical experiments on photonic systems with up to 805 modes and over a million trainable parameters, highlighting its scalability and suitability for near-term photonic quantum devices.

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)
• 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)
• Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits [0.0]
  Quantum Machine Learning models typically require expensive on-chip training procedures and often lack efficient gradient estimation methods.<n>By employing Pauli propagation, it is possible to derive a symbolic representation of observables as analytic functions of a circuit's parameters.<n>The proposed approach is demonstrated on the Variational Quantum Eigensolver for obtaining the ground state of a spin model.
  arXiv  Detail & Related papers  (2025-12-18T15:44:07Z)
• Fermionic Born Machines: Classical training of quantum generative models based on Fermion Sampling [0.0]
  We introduce Fermionic Born Machines as an example of classically trainable quantum generative models.<n>The model employs parameterized magic states and fermionic linear optical (FLO) transformations with learnable parameters.<n>The specific structure of the ansatz induces a loss landscape that exhibits favorable characteristics for optimization.
  arXiv  Detail & Related papers  (2025-11-17T19:03:03Z)
• Sample-based training of quantum generative models [1.3521721488318912]
  We introduce a training framework that extends the principle of contrastive divergence to quantum models.<n>By deriving the circuit structure and providing a general recipe for constructing it, we obtain quantum circuits that generate the samples required for parameter updates.
  arXiv  Detail & Related papers  (2025-11-14T19:00:02Z)
• 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)
• Train on classical, deploy on quantum: scaling generative quantum machine learning to a thousand qubits [0.27309692684728604]
  We show that instantaneous generative models based on quantum circuits can be trained efficiently on classical hardware.<n>By combining our approach with a data-dependent parameter initialisation strategy, we do not encounter issues of barren plateaus.<n>We find that the quantum models can successfully learn from high dimensional data, and perform surprisingly well compared to simple energy-based classical generative models.
  arXiv  Detail & Related papers  (2025-03-04T19:00:02Z)
• 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)
• 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)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Bosonic field digitization for quantum computers [62.997667081978825]
  We address the representation of lattice bosonic fields in a discretized field amplitude basis. We develop methods to predict error scaling and present efficient qubit implementation strategies.
  arXiv  Detail & Related papers  (2021-08-24T15:30:04Z)
• 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.