Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agents
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Quantum Machine Learningreinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (…
Fraud DetectionGenerative training of quantum Boltzmann machines with hidden units
In this article we provide a method for fully quantum generative training of quantum Boltzmann machines with both visible and hidden units while using quantum relative entropy as an objective. This is significant because…
Generative modeling using evolved quantum Boltzmann machines
Born-rule generative modeling, a central task in quantum machine learning, seeks to learn probability distributions that can be efficiently sampled by measuring complex quantum states. One hope is for quantum models to e…
Quantum Machine LearningFundamentals of quantum Boltzmann machine learning with visible and hidden units
One of the primary applications of classical Boltzmann machines is generative modeling, wherein the goal is to tune the parameters of a model distribution so that it closely approximates a target distribution. Training r…
Structured quantum learning via em algorithm for Boltzmann machines
Quantum Boltzmann machines (QBMs) are generative models with potential advantages in quantum machine learning, yet their training is fundamentally limited by the barren plateau problem, where gradients vanish exponential…
Quantum Machine Learning