Quantum optical neural networks

Steinbrecher, Gregory R.; Olson, Jonathan P.; Englund, Dirk; Carolan, Jacques

doi:10.1038/s41534-019-0174-7

Cited by 164 publications

(153 citation statements)

References 95 publications

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“…[36][37][38][39][40] , where the authors exploit a qubit circuit setup, though the gate choices and geometry are somewhat more specific than ours. Another interesting approach is to use continuous-variable quantum systems (e.g., light) to define quantum perceptrons [41][42][43] .…”

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confidence: 99%

Training deep quantum neural networks

et al. 2020

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Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data.

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confidence: 99%

Training deep quantum neural networks

et al. 2020

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“…We think it is the reason that our scheme has the compatibility. Our training method for efficient computation of MSE can avoid the considerations about the knowledge of internal quantum state of the system [44] and thus provide a new way to finish optimizing quantum version of feedforward neural network. Compared with the quantum generalized scheme [45], our scheme has an efficient economy in the number of qubits and can relax the demands for experimental realization.…”

Section: Discussionmentioning

confidence: 99%

Entanglement-based quantum deep learning

Yang

Zhang

2020

New J. Phys.

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Classical deep learning algorithms have aroused great interest in both academia and industry for their utility in image recognition, language translation, decision-making problems and more. In this work, we have provided a quantum deep learning scheme based on multi-qubit entanglement states, including computation and training of neural network in full quantum process. In the course of training, efficient calculation of the distance between unknown unit vector and known unit vector has been realized by proper measurement based on the Greenberger-Horne-Zeilinger entanglement states.An exponential speedup over classical algorithms has been demonstrated. In the process of computation, quantum scheme corresponding to multi-layer feedforward neural network has been provided. We have shown the utility of our scheme using Iris dataset. The extensibility of the present scheme to different types of model has also been analyzed

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“…Among them, photonic devices represent a promising technological platform due to their fast switching time, high bandwidth and low crosstalks [18]. For neural networks, for instance, first proof-of-principle demonstrations on optical platforms have already been studied [19,20] and experimentally tested [21,22]. Inspired by the outstanding success of both RL and ASICs, here we present a novel photonic architecture for the implementation of active learning agents.…”

Section: Introductionmentioning

confidence: 99%

Photonic architecture for reinforcement learning

et al. 2020

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The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.OPEN ACCESS RECEIVED promising features as compared to electronic processors [27][28][29][30]. For instance, nanosecond-scale routing and reconfigurability have already been demonstrated [31][32][33], while encoding information in photons enables decision-making at the speed of light, only limited by the generation and detection rates. Moreover, the use of phase-change materials for in-memory information processing [34] promises to enhance the energy efficiency, since their properties can be modified without continuous external intervention [35,36]. Importantly, since the architecture uses single photons, decision-making is fueled by genuine quantum randomness. This feature marks a fundamental departure from pseudorandom number generation in conventional devices. (ii) The second contribution is the development of a specific variant of PS based on binary decision trees (tree-PS, or t-PS for short), which is closely connected to the standard PS and suitable for the implementation on a photonic circuit. Furthermore, we discuss how this variant enables key features of artificial intelligence, namely abstraction and generalization [37,38].The Article is structured as follows. In section 2 we summarize the theoretical framework of RL, exemplified by three common approaches: SARSA, Q-learning, and PS. In section 3 we describe the blueprint for a fully integrated, photonic RL agent. We then numerically investigate its performance within two standard RL tasks and under realistic experimental imperfections in section 4. Finally, in section 5 we discuss promising features of this architecture within the context of t-PS.

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Quantum optical neural networks

Cited by 164 publications

References 95 publications

Training deep quantum neural networks

Training deep quantum neural networks

Entanglement-based quantum deep learning

Photonic architecture for reinforcement learning

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