mlsanityData to 14 Oct 2026

Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agents

arXiv 2408.17240 ↗First posted 30 Aug 2024quant-ph cs.LGcited by 1

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

Deep Learning algorithms, such as those used in Reinforcement Learning, often require large quantities of data to train effectively. In most cases, the availability of data is not a significant issue. However, for some contexts, such as in autonomous cyber defence, we require data efficient methods. Recently, Quantum Machine Learning and Boltzmann Machines have been proposed as solutions to this challenge. In this work we build upon the pre-existing work to extend the use of Deep Boltzmann Machines to the cutting edge algorithm Proximal Policy Optimisation in a Reinforcement Learning cyber defence environment. We show that this approach, when solved using a D-WAVE quantum annealer, can lead to a two-fold increase in data efficiency. We therefore expect it to be used by the machine learning and quantum communities who are hoping to capitalise on data-efficient Reinforcement Learning methods.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 19 references that are in the map. First posted on arXiv 30 Aug 2024.

Builds on

8 of 19 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2024Reinforcement learningcited by 247
  4. 2021Quantum advantage in learning from experimentscited by 188
  5. 2022Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulationcited by 37
  6. 2016Reinforcement Learning Using Quantum Boltzmann Machinescited by 20
  7. 2021Prospective Artificial Intelligence Approaches for Active Cyber Defencecited by 11
  8. 2021On the challenges of using D-Wave computers to sample Boltzmann Random Variablescited by 4

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learningcited by 0

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  3. 2024An Introduction to Quantum Reinforcement Learning (QRL)
  4. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
  5. 2025From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware
  6. 2024Reinforcement learning-based architecture search for quantum machine learning
  7. 2025A Primer on Quantum Machine Learning
  8. 2024Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization