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Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices.
Comparison of qaoa with quantum and simulated annealing
Streif, M. and Leib, M. (2019) · 1901
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Optimizing qaoa: Success probability and runtime dependence on circuit depth
Niu, M.Y., Lu, S., and Chuang, I.L. (2019b) · 1905
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Robust quantum control in games: an adversarial learning approach
Ge, X., Ding, H., Rabitz, H., and Wu, R. (2019) · 1909
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Almost Any Quantum Logic Gate is Universal
Lloyd, S. (1995) · 1995
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A rapid monotonically convergent iteration algorithm for quantum optimal control over the expectation value of a positive definite operator
Zhu, W. and Rabitz, H. (1998) · 1998
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New formulations of monotonically convergent quantum control algorithms
Maday, Y. and Turinici, G. (2003) · 2003
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Optimal control of coupled spin dynamics: design of nmr pulse sequences by gradient ascent algorithms
Khaneja, N., Reiss, T., Kehlet, C., Schulte-Herbrüggen, T., and Glaser, S.J. (2005) · 2005
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Robust control of quantum gates via sequential convex programming
Kosut, R.L., Grace, M.D., and Brif, C. (2013) · 2013
Cited alongside, same era.
A quantum approximate optimization algorithm
Farhi, E., Goldstone, J., and Gutmann, S. (2014) · 2014
Cited alongside, same era.
The theory of variational hybrid quantum-classical algorithms
McClean, J.R., Romero, J., Babbush, R., and Aspuru-Guzik, A. (2016) · 2016
Cited alongside, same era.
Optimal control of two qubits via a single cavity drive in circuit quantum electrodynamics
Allen, J.L., Kosut, R., Joo, J., Leek, P., and Ginossar, E. (2017) · 2017
Cited alongside, same era.
Optimizing variational quantum algorithms using pontryagin’s minimum principle
Yang, Z.C., Rahmani, A., Shabani, A., Neven, H., and Chamon, C. (2017) · 2017
Cited alongside, same era.
Quantum algorithms for scientific computing and approximate optimization
Hadfield, S. (2018) · 2018
Later among the works it cites.
Quantum variational autoencoder
Khoshaman, A., Vinci, W., Denis, B., Andriyash, E., and Amin, M.H. (2018) · 2018
Later among the works it cites.
Quantum approximate optimization is computationally universal
Lloyd, S. (2018) · 2018
Later among the works it cites.
EE364b, Sequential Convex Programming, Stanford University, https://web.stanford.edu/class/ee364b/lectures/seq_slides.pdf
Boyd, S. (2019) · 2019
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Gurobi optimizer reference manual
Gurobi Optimization, L. (2019) · 2019
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A Quantum Approximate Optimization Algorithm for continuous problems
Verdon, G., Arrazola, J.M., Brádler, K., and Killoran, N. (2019) · 2019
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Optimal control of superconducting gmon qubits using pontryagin's minimum principle: Preparing a maximally entangled state with singular bang-bang protocols
Bao, S., Kleer, S., Wang, R., and Rahmani, A. (2018) · 2018
Cited alongside, same era.
Reinforcement learning in different phases of quantum control
Bukov, M., Day, A.G., Sels, D., Weinberg, P., Polkovnikov, A., and Mehta, P. (2018) · 2018
Cited alongside, same era.
Universal quantum control through deep reinforcement learning
Niu, M.Y., Boixo, S., Smelyanskiy, V.N., and Neven, H. (2019a)
Cited in the paper.
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Learning robust and high-precision quantum controls
Wu, R.B., Ding, H., Dong, D., and Wang, X. (2019) · 2019
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