Search Results for author: Thomas D. Barrett

Found 10 papers, 9 papers with code

Universally Expressive Communication in Multi-Agent Reinforcement Learning

no code implementations14 Jun 2022 Matthew Morris, Thomas D. Barrett, Arnu Pretorius

Allowing agents to share information through communication is crucial for solving complex tasks in multi-agent reinforcement learning.

Multi-agent Reinforcement Learning reinforcement-learning

Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories

1 code implementation28 May 2022 Christopher W. F. Parsonson, Alexandre Laterre, Thomas D. Barrett

By retrospectively deconstructing the search tree into multiple paths each contained within a sub-tree, we enable the agent to learn from shorter trajectories with more predictable next states.

Imitation Learning reinforcement-learning

Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration

1 code implementation27 May 2022 Thomas D. Barrett, Christopher W. F. Parsonson, Alexandre Laterre

Compared to the nearest competitor, ECORD reduces the optimality gap by up to 73% on 500 vertex graphs with a decreased wall-clock time.

Efficient Exploration graph partitioning

Autoregressive neural-network wavefunctions for ab initio quantum chemistry

1 code implementation26 Sep 2021 Thomas D. Barrett, Aleksei Malyshev, A. I. Lvovsky

In recent years, neural network quantum states (NNQS) have emerged as powerful tools for the study of quantum many-body systems.

Variational Monte Carlo

Learning Group Structure and Disentangled Representations of Dynamical Environments

1 code implementation17 Feb 2020 Robin Quessard, Thomas D. Barrett, William R. Clements

Learning disentangled representations is a key step towards effectively discovering and modelling the underlying structure of environments.

Disentanglement

Backpropagation through nonlinear units for all-optical training of neural networks

1 code implementation23 Dec 2019 Xianxin Guo, Thomas D. Barrett, Zhiming M. Wang, A. I. Lvovsky

Backpropagation through nonlinear neurons is an outstanding challenge to the field of optical neural networks and the major conceptual barrier to all-optical training schemes.

Emerging Technologies Signal Processing Optics

Exploratory Combinatorial Optimization with Reinforcement Learning

2 code implementations9 Sep 2019 Thomas D. Barrett, William R. Clements, Jakob N. Foerster, A. I. Lvovsky

Our approach of exploratory combinatorial optimization (ECO-DQN) is, in principle, applicable to any combinatorial problem that can be defined on a graph.

Combinatorial Optimization reinforcement-learning

Pushing Purcell-enhancement beyond its limits

1 code implementation20 Mar 2019 Thomas D. Barrett, Thomas H. Doherty, Axel Kuhn

It is found that birefringence can mitigate the tradeoff between stronger emitter-cavity coupling and efficient photon extraction.

Quantum Physics

Polarisation oscillations in birefringent emitter-cavity systems

1 code implementation19 Jul 2018 Thomas D. Barrett, Oliver Barter, Dustin Stuart, Ben Yuen, Axel Kuhn

We present the effects of resonator birefringence on the cavity-enhanced interfacing of quantum states of light and matter, including the first observation of single photons with a time-dependent polarisation state that evolves within their coherence time.

Quantum Physics

Nonlinear Zeeman Effects in the Cavity-Enhanced Emission of Polarised Photons

1 code implementation27 Apr 2018 Thomas D. Barrett, Dustin Stuart, Oliver Barter, Axel Kuhn

We theoretically and experimentally investigate nonlinear Zeeman effects within a polarised single-photon source that uses a single 87Rb atom strongly coupled to a high finesse optical cavity.

Quantum Physics

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