| Tensorflow quantum: A software framework for quantum machine learning M Broughton, G Verdon, T McCourt, AJ Martinez, JH Yoo, SV Isakov, ... arXiv preprint arXiv:2003.02989, 2020 | 381 | 2020 |
| Reinforcement learning with quantum variational circuit O Lockwood, M Si Proceedings of the AAAI conference on artificial intelligence and …, 2020 | 113 | 2020 |
| A review of uncertainty for deep reinforcement learning O Lockwood, M Si Proceedings of the AAAI Conference on Artificial Intelligence and …, 2022 | 35 | 2022 |
| Playing atari with hybrid quantum-classical reinforcement learning O Lockwood, M Si NeurIPS 2020 workshop on pre-registration in machine learning, 285-301, 2021 | 22 | 2021 |
| An empirical review of optimization techniques for quantum variational circuits O Lockwood arXiv preprint arXiv:2202.01389, 2022 | 10 | 2022 |
| QuantumCircuitOpt: An open-source framework for provably optimal quantum circuit design H Nagarajan, O Lockwood, C Coffrin 2021 IEEE/ACM Second International Workshop on Quantum Computing Software …, 2021 | 10 | 2021 |
| Optimizing quantum variational circuits with deep reinforcement learning O Lockwood arXiv preprint arXiv:2109.03188, 2021 | 3 | 2021 |
| Quantum Dynamical Hamiltonian Monte Carlo O Lockwood, P Weiss, F Aronshtein, G Verdon arXiv preprint arXiv:2403.01775, 2024 | | 2024 |
| In Defense of the Paper O Lockwood arXiv preprint arXiv:2104.08359, 2021 | | 2021 |
| Replicating Softmax Deep Double Deterministic Policy Gradients O Lockwood, M Qian | | 2021 |
