Deep reinforcement learning for quantum gate control

An, Zheng; Zhou, D. L.

doi:10.1209/0295-5075/126/60002

Cited by 114 publications

(76 citation statements)

References 29 publications

(35 reference statements)

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“…The successful deployment of DL is primarily attributed to the improvement of new computer architectures associated with powerful computing capabilities in the past decades. Many researchers also utilized DL-based data analysis methods on fundamental physics researches such as quantum many-body physics [8]- [10], phase-transitions [11], quantum control [12], [13], and quantum error correction [14], [15]. In the meantime, great efforts from both the physics and machine learning community have dedicated to and empowered quantum computation.…”

Section: Introductionmentioning

confidence: 99%

Variational Quantum Circuits for Deep Reinforcement Learning

et al. 2020

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The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. However, the existing quantum computing platforms are hard to simulate classical deep learning models or problems because of the intractability of deep quantum circuits. Thus, it is necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.

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Section: Introductionmentioning

confidence: 99%

Variational Quantum Circuits for Deep Reinforcement Learning

et al. 2020

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“…A shallow depth may broaden exploration, a strategy typically found in Reinforcement Learning (RL) [30]. This has been powerfully combined with Deep Neural Networks (DNN) [31][32][33][34][35] and applied recently to quantum systems [36][37][38][39][40][41][42][43]. Unfortunately, single-step lookaheads are inherently local and thus require a slower learning rate, with no performance gain found over full-depth, domain-specialized (Hessian approximation) methods in QOCT.…”

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confidence: 99%

Global optimization of quantum dynamics with AlphaZero deep exploration

et al. 2020

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While a large number of algorithms for optimizing quantum dynamics for different objectives have been developed, a common limitation is the reliance on good initial guesses, being either random or based on heuristics and intuitions. Here we implement a tabula rasa deep quantum exploration version of the Deepmind AlphaZero algorithm for systematically averting this limitation. AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden variable approximation of the quantum parameter landscape. To emphasize transferability, we apply and benchmark the algorithm on three classes of control problems using only a single common set of algorithmic hyperparameters. AlphaZero achieves substantial improvements in both the quality and quantity of good solution clusters compared to earlier methods. It is able to spontaneously learn unexpected hidden structure and global symmetry in the solutions, going beyond even human heuristics. arXiv:1907.05672v1 [quant-ph]

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“…Similarly, Ref. [225] constructs a deep Q-learning framework to find the optimal time dependence of controllable parameters to implement a local Hadamard gate and a two-qubit CNOT gate. Importantly, Ref.…”

Section: Quantum Controlmentioning

confidence: 99%

Machine learning for quantum matter

Carrasquilla

2020

Advances in Physics: X

172

111

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Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information, quantum gravity, and large-scale numerical simulations. Recently, researchers interested in quantum matter and strongly correlated quantum systems have turned their attention to the algorithms underlying modern machine learning with an eye on making progress in their fields. Here we provide a short review on the recent development and adaptation of machine learning ideas for the purpose advancing research in quantum matter, including ideas ranging from algorithms that recognize conventional and topological states of matter in synthetic experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems. We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics.

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Deep reinforcement learning for quantum gate control

Cited by 114 publications

References 29 publications

Variational Quantum Circuits for Deep Reinforcement Learning

Variational Quantum Circuits for Deep Reinforcement Learning

Global optimization of quantum dynamics with AlphaZero deep exploration

Machine learning for quantum matter

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