2 papers
quant-ph2024
Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agents
Daniel Kent, Clement O'Rourke, Jake Southall +2
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…
quant-ph2021
Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer
Catherine F. Higham, Adrian Bedford
We demonstrate the feasibility of framing a classically learned deep neural network as an energy based model that can be processed on a one-step quantum annealer in order to exploi…