activity
20202022
most citedReinforcement Learning with Quantum Variational Circuits

16 citations · 43 across the 7 of their papers we have counts for

collaborators
Showing quant-phShow all

5 papers · 1 filter

quant-ph2022★ 7 cited

An Empirical Review of Optimization Techniques for Quantum Variational Circuits

Owen Lockwood

Quantum Variational Circuits (QVCs) are often claimed as one of the most potent uses of both near term and long term quantum hardware. The standard approaches to optimizing these c…

quant-ph2021

QuantumCircuitOpt: An Open-source Framework for Provably Optimal Quantum Circuit Design

Harsha Nagarajan, Owen Lockwood, Carleton Coffrin

In recent years, the quantum computing community has seen an explosion of novel methods to implement non-trivial quantum computations on near-term hardware. An important direction…

quant-ph2021★ 15 cited

Playing Atari with Hybrid Quantum-Classical Reinforcement Learning

Owen Lockwood, Mei Si

Despite the successes of recent works in quantum reinforcement learning, there are still severe limitations on its applications due to the challenge of encoding large observation s…

quant-ph2020★ 16 cited

Reinforcement Learning with Quantum Variational Circuits

Owen Lockwood, Mei Si

The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work expl…

quant-ph2020

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

Michael Broughton, Guillaume Verdon, Trevor McCourt +26

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high…