# Assessment of image generation by quantum annealer

arXiv: https://arxiv.org/abs/2103.08373 · HTML: https://mlsanity.com/paper/2103.08373/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Quantum Variational Autoencoder](https://mlsanity.com/paper/1802.05779/) (2018)
- [Application of Quantum Annealing to Training of Deep Neural Networks](https://mlsanity.com/paper/1510.06356/) (2015)
- [Traffic flow optimization using a quantum annealer](https://mlsanity.com/paper/1708.01625/) (2017)
- [Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models](https://mlsanity.com/paper/1609.02542/) (2016)
- [Reinforcement Learning Using Quantum Boltzmann Machines](https://mlsanity.com/paper/1612.05695/) (2016)
- [Control of automated guided vehicles without collision by quantum annealer and digital devices](https://mlsanity.com/paper/1812.01532/) (2018)
- [Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer](https://mlsanity.com/paper/1508.06182/) (2015)
- [Efficiency of quantum versus classical annealing in non-convex learning problems](https://mlsanity.com/paper/1706.08470/) (2017)
- [Item Listing Optimization for E-commerce Websites based on Diversity](https://mlsanity.com/paper/1903.12478/) (2019)
- [Quantum Annealing Implementation of Job-Shop Scheduling](https://mlsanity.com/paper/1506.08479/) (2015)
- [Deep Neural Network Detects Quantum Phase Transition](https://mlsanity.com/paper/1712.00371/) (2017)
- [Optimization of neural networks via finite-value quantum fluctuations](https://mlsanity.com/paper/1807.00414/) (2018)
- [L_1-regularized Boltzmann machine learning using majorizer minimization](https://mlsanity.com/paper/1503.03132/) (2015)
- [Leveraging Adiabatic Quantum Computation for Election Forecasting](https://mlsanity.com/paper/1802.00069/) (2018)

## Built on by (most cited first, 2 of 2)

- [Performance Evaluation of Ising and QUBO Variable Encodings in Boltzmann Machine Learning](https://mlsanity.com/paper/2510.13210/) (2025)
- [Training Energy-Based Models with Non-MCMC Samplers and Efficient Temperature Estimation](https://mlsanity.com/paper/2512.02323/) (2025)

## Abstract (arXiv)

Quantum annealing was originally proposed as an approach for solving combinatorial optimisation problems using quantum effects. D-Wave Systems has released a production model of quantum annealing hardware. However, the inherent noise and various environmental factors in the hardware hamper the determination of optimal solutions. In addition, the freezing effect in regions with weak quantum fluctuations generates outputs approximately following a Gibbs--Boltzmann distribution at an extremely low temperature. Thus, a quantum annealer may also serve as a fast sampler for the Ising spin-glass problem, and several studies have investigated Boltzmann machine learning using a quantum annealer. Previous developments have focused on comparing the performance in the standard distance of the resulting distributions between conventional methods in classical computers and sampling by a quantum annealer. In this study, we focused on the performance of a quantum annealer as a generative model. To evaluate its performance, we prepared a discriminator given by a neural network trained on an a priori dataset. The evaluation results show a higher performance of quantum annealing compared with the classical approach for Boltzmann machine learning.