Photonic quantum information processing: a review

Flamini, Fulvio; Spagnolo, Nicolò; Sciarrino, Fabio

doi:10.1088/1361-6633/aad5b2

Cited by 573 publications

(446 citation statements)

References 701 publications

(1,271 reference statements)

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“…Also, its simple and scalable design warrants a near-term implementation in optical experiments and is apt for embedding in miniaturized devices compatible with quantum technologies. Indeed, all required optical components have already been experimentally demonstrated on integrated circuits [27][28][29][30][31][32].…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

“…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.…”

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confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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Photonic architecture for reinforcement learning

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“…Recovery with prior knowledge If some part of the dynamics is known to high accuracy, equation (4) can be turned into a non-homogenous equation. For instance, if the Hamiltonian is known but the dissipators are not, we obtain equation (11). Using a set of constraint operators { } = A n n N 1 , we obtain the system of equations…”

Section: Discussionmentioning

confidence: 99%

“…The development of quantum simulators and computation devices has rapidly progressed over the last few years [1]. These developments span a multitude of physical platforms, including ultracold atoms [2][3][4][5], trapped ions [6][7][8], photonic circuits [9][10][11][12], Josephson junction arrays [13][14][15][16][17] and more, reaching ever larger complexity. The growth in the complexity of these systems calls for efficient methods to characterize and verify their dynamics.…”

Section: Introductionmentioning

confidence: 99%

Learning the dynamics of open quantum systems from their steady states

et al. 2020

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Recent works have shown that generic local Hamiltonians can be efficiently inferred from local measurements performed on their eigenstates or thermal states. Realistic quantum systems are often affected by dissipation and decoherence due to coupling to an external environment. This raises the question whether the steady states of such open quantum systems contain sufficient information allowing for full and efficient reconstruction of the system's dynamics. We find that such a reconstruction is possible for generic local Markovian dynamics. We propose a recovery method that uses only local measurements; for systems with finite-range interactions, the method recovers the Lindbladian acting on each spatial domain using only observables within that domain. We numerically study the accuracy of the reconstruction as a function of the number of measurements, type of opensystem dynamics and system size. Interestingly, we show that couplings to external environments can in fact facilitate the reconstruction of Hamiltonians composed of commuting terms.

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Progress of Gate‐Defined Semiconductor Spin Qubit: Host Materials and Device Geometries

Liu,

Guan,

Luo

et al. 2024

Adv Funct Materials

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Quantum computing offers the potential to revolutionize information processing by exploiting the principles of quantum mechanics. Among the diverse quantum bit (qubit) technologies, silicon‐based semiconductor spin qubits have emerged as a promising contender due to their potential scalability and compatibility with existing semiconductor technologies. In this paper, the latest developments of spin qubits in gate‐defined semiconducting nanostructures made of silicon and germanium, starting from the basic properties of electron and hole states in group‐IV semiconductors, are reviewed. Specifically, various nanostructures that exploit their unique microscopic properties for qubit implementations, elaborating on the advances and challenges in experiments, are discussed. Strategies for enhancing qubit performance, such as designing new nanostructures and identifying suitable operating points, particularly those involving the valleys of electron qubits and the heavy‐hole–light‐hole mixing of hole qubits, are also highlighted. This comprehensive review thus provides valuable insights into the current state‐of‐the‐art in semiconductor quantum computing and suggests avenues for future research.

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Photonic quantum information processing: a review

Cited by 573 publications

References 701 publications

Photonic architecture for reinforcement learning

Photonic architecture for reinforcement learning

Learning the dynamics of open quantum systems from their steady states

Progress of Gate‐Defined Semiconductor Spin Qubit: Host Materials and Device Geometries

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