mlsanityData to 14 Oct 2026

On the challenges of using D-Wave computers to sample Boltzmann Random Variables

arXiv 2111.15295 ↗First posted 30 Nov 2021quant-ph stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of textitBoltzmann machines, a specific kind of neural network. Several attempts have been made to use a D-Wave quantum computer to sample such a distribution, as this could lead to significant speedup in these applications. Yet, at present, several challenges remain to efficiently perform such sampling. We detail the various obstacles and explain the remaining difficulties in solving the sampling problem on a D-wave machine.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 30 Nov 2021.

Builds on

5 of 13 references.

References · 5

  1. 2015Application of Quantum Annealing to Training of Deep Neural Networkscited by 37
  2. 2016Reinforcement Learning Using Quantum Boltzmann Machinescited by 20
  3. 2020Training and Classification using a Restricted Boltzmann Machine on the D-Wave 2000Qcited by 18
  4. 2021High-quality Thermal Gibbs Sampling with Quantum Annealing Hardwarecited by 9
  5. 2021Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machinescited by 4

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023A hybrid quantum-classical approach for inference on restricted Boltzmann machinescited by 3
  2. 2023Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computingcited by 3
  3. 2024Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agentscited by 1
  4. 2025Quantum Adiabatic Generation of Human-Like Passwordscited by 0