Coherent transport of quantum states by deep reinforcement learning

Porotti, Riccardo; Tamascelli, Dario; Restelli, Marcello; Prati, Enrico

doi:10.1038/s42005-019-0169-x

Cited by 112 publications

(100 citation statements)

References 60 publications

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“…Another fundamental aspect is related to the realization of devices able to interconnect remote sites composing the quantum circuit to transfer information [67,68]. In order to overcome the problem of interaction between distant qubits, different routes have been pursued: the SWAP chain protocol [4,5] and the coherent tunneling by adiabatic passage (CTAP) scheme [69,70,71]. The SWAP method is based on the sequential repetition of SWAP gates between adjacent qubits.…”

Section: Theorymentioning

confidence: 99%

Is all-electrical silicon quantum computing feasible in the long term?

Ferraro¹,

Prati²

2020

Physics Letters A

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The development of the first generation of commercial quantum computers is based on superconductive qubits and trapped ions respectively. Other technologies such as semiconductor quantum dots, neutral ions and photons could in principle provide an alternative to achieve comparable results in the medium term. It is relevant to evaluate if one or more of them is potentially more effective to address scalability to millions of qubits in the long term, in view of creating a universal quantum computer. We review an all-electrical silicon spin qubit, that is the double quantum dot hybrid qubit, a quantum technology which relies on both solid theoretical grounding on one side, and massive fabrication technology of nanometric scale devices by the existing silicon supply chain on the other.

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Section: Theorymentioning

confidence: 99%

Is all-electrical silicon quantum computing feasible in the long term?

Ferraro¹,

Prati²

2020

Physics Letters A

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“…Interestingly, [16] describes a one-to-one mapping between RBM-based DNNs and the variational RG [3], while in [4] they conjecture even possible similarities between the principles of QFT and RBM-based DNN. The question becomes even more intriguing in a recent publication [17] where deep reinforcement learning has been demonstrated to show its effectiveness in discovering control sequences for a tunable quantum system whose time evolution starts far from its final equilibrium, without any a priori knowledge. These simple considerations, and other analyses described in literature, are suggesting the possibility that DNN principles are deeply rooted in quantum physics.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Complex Deep Learning with Quantum Optics

Manzalini¹

2019

Quantum Reports

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The rapid evolution towards future telecommunications infrastructures (e.g., 5G, the fifth generation of mobile networks) and the internet is renewing a strong interest for artificial intelligence (AI) methods, systems, and networks. Processing big data to infer patterns at high speeds and with low power consumption is becoming an increasing central technological challenge. Electronics are facing physically fundamental bottlenecks, whilst nanophotonics technologies are considered promising candidates to overcome the limitations of electronics. Today, there are evidences of an emerging research field, rooted in quantum optics, where the technological trajectories of deep neural networks (DNNs) and nanophotonics are crossing each other. This paper elaborates on these topics and proposes a theoretical architecture for a Complex DNN made from programmable metasurfaces; an example is also provided showing a striking correspondence between the equivariance of convolutional neural networks (CNNs) and the invariance principle of gauge transformations.

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“…Modern computing devices are rapidly evolving from handy resources to autonomous machines [1]. On the brink of this new technological revolution [2], reinforcement learning (RL) has emerged as a powerful and flexible tool to enable problem solving at an unprecedented scale, both in computer science [3-63-6] and in physics research [7][8][9][10][11][12][13]. This breakthrough development was in part spurred by the technological achievements of the last decades, which unlocked vast amounts of data and computational power.…”

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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Coherent transport of quantum states by deep reinforcement learning

Cited by 112 publications

References 60 publications

Is all-electrical silicon quantum computing feasible in the long term?

Is all-electrical silicon quantum computing feasible in the long term?

Complex Deep Learning with Quantum Optics

Photonic architecture for reinforcement learning

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