Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors

TL;DR

This paper introduces a quantum-annealing-based framework for generative models with Boltzmann priors, enabling efficient training and high-quality data generation using a D-Wave quantum processor.

Contribution

It develops a novel quantum annealing approach for variational autoencoders with general Boltzmann priors, improving training stability and generation quality.

Findings

Achieved faster convergence and lower reconstruction loss than Gaussian-prior VAEs.

Demonstrated high-quality generation on MNIST, Fashion-MNIST, and CelebA datasets.

Provided out-of-distribution detection signals surpassing reconstruction loss.

Abstract

Energy-based models provide a natural bridge between statistical physics and machine learning by representing data through structured energy landscapes. Boltzmann machines are a particularly compelling class of such models for capturing complex interactions among latent variables, but their use in modern generative learning has been limited by the classical intractability of sampling from general (non-restricted) Boltzmann distributions. Here we develop a quantum-annealing-based framework that enables variational autoencoders with general Boltzmann priors. The framework employs three complementary annealing modes tailored to different stages of learning and deployment: diabatic quantum annealing provides unbiased Boltzmann samples for efficient training, slower annealing concentrates samples near low-energy configurations of the learned prior for unconditional generation, and conditional annealing with external fields steers the learned energy landscape toward attribute-specific regions for conditional generation and semantic editing. Using up to 2000 qubits on a D-Wave Advantage2 processor, we demonstrate stable training and high-quality generation on MNIST, Fashion-MNIST, and CelebA, achieving faster convergence and lower reconstruction loss than a Gaussian-prior VAE with the same encoder-decoder architecture. Beyond generation, the learned energy function provides out-of-distribution detection signals that add discriminative power beyond reconstruction loss. We demonstrate that these scores separate in-distribution samples from held-out digit classes in one-class MNIST experiments and improve the detection of market regime shifts in financial data. These results establish quantum annealing as a practical and controllable physical mechanism for energy-based representation learning and generative modeling beyond the reach of tractable classical approaches.