Direct dialling of Haar random unitary matrices

Russell, Nicholas N.J.; Chakhmakhchyan, Levon; O'Brien, Jeremy J.L.; Laing, Anthony

doi:10.1088/1367-2630/aa60ed

Cited by 86 publications

(100 citation statements)

References 31 publications

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“…This result demonstrates superior robustness of the proposed composer architecture to any fabrication defects altering the mixing layer, as opposed to the previously reported implementations [17]. This is the main result of this work, since the proposed circuit overcomes the main problem of the architecture based on the Hurwitz decomposition -the inability to fully cover the unitary space when the elementary transformations suffer from imperfect fabrication [17,29].…”

Section: The Unitary Composermentioning

confidence: 66%

Robust Architecture for Programmable Universal Unitaries

et al. 2020

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Experimental implementation of a quantum computing algorithm strongly relies on the ability to construct required unitary transformations applied to the input quantum states. In particular, near-term linear optical computing requires universal programmable interferometers, capable of implementing an arbitrary transformation of input optical modes. So far these devices were composed as a circuit with well defined building blocks, such as balanced beamsplitters. This approach is vulnerable to manufacturing imperfections inevitable in any realistic experimental implementation, and the larger the circuit size grows, the more strict the tolerances become. In this work we demonstrate a new methodology for the design of the high-dimensional mode transformations, which overcomes this problem, and carefully investigate its features. The circuit in our architecture is composed of interchanging mode mixing layers, which may be almost arbitrary, and layers of variable phaseshifters, allowing to program the device to approximate any desired unitary transformation. arXiv:1906.06748v1 [quant-ph]

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Section: The Unitary Composermentioning

confidence: 66%

Robust Architecture for Programmable Universal Unitaries

et al. 2020

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“…Low-loss photon propagation is then required in circuitry of several thousands of modes, before arriving at several thousands of high-efficiency detectors. Programming different Haar random unitary matrices over those modes requires the precise setting of millions of phase shifters 36 .…”

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confidence: 99%

Classical boson sampling algorithms with superior performance to near-term experiments

et al. 2017

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It is predicted that quantum computers will dramatically outperform their conventional counterparts. However, largescale universal quantum computers are yet to be built. Boson sampling 1 is a rudimentary quantum algorithm tailored to the platform of linear optics, which has sparked interest as a rapid way to demonstrate such quantum supremacy 2-6 . Photon statistics are governed by intractable matrix functions, which suggests that sampling from the distribution obtained by injecting photons into a linear optical network could be solved more quickly by a photonic experiment than by a classical computer. The apparently low resource requirements for large boson sampling experiments have raised expectations of a near-term demonstration of quantum supremacy by boson sampling 7,8 . Here we present classical boson sampling algorithms and theoretical analyses of prospects for scaling boson sampling experiments, showing that near-term quantum supremacy via boson sampling is unlikely. Our classical algorithm, based on Metropolised independence sampling, allowed the boson sampling problem to be solved for 30 photons with standard computing hardware. Compared to current experiments, a demonstration of quantum supremacy over a successful implementation of these classical methods on a supercomputer would require the number of photons and experimental components to increase by orders of magnitude, while tackling exponentially scaling photon loss.It is believed that new types of computing machines will be constructed to exploit quantum mechanics for an exponential speed advantage in solving certain problems compared with classical computers 9 . Recent large state and private investments in developing quantum technologies have increased interest in this challenge. However, it is not yet experimentally proven that a large computationally useful quantum system can be assembled, and such a task is highly non-trivial given the challenge of overcoming the effects of errors in these systems.Boson sampling is a simple task which is native to linear optics and has captured the imagination of quantum scientists because it seems possible that the anticipated supremacy of quantum machines could be demonstrated by a near-term experiment. The advent of integrated quantum photonics 10 has enabled large, complex, stable and programmable optical circuitry 11,12 , while recent advances in photon generation [13][14][15] and detection 16,17 have also been impressive. The possibility to generate many photons, evolve them under a large linear optical unitary transformation, then detect them, seems feasible, so the role of a boson sampling machine as a rudimentary but legitimate computing device is particularly appealing. Compared to a universal digital quantum computer, the resources required for experimental boson sampling appear much less demanding. This approach of designing quantum algorithms to demonstrate computational supremacy with nearterm experimental capabilities has inspired a raft of proposals suited to different hardware platforms [18]...

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“…For instance, when one clip-to-clip connection 2 To fulfill all the above desiderata, a top-down approach could be to adopt well-established meshes of optical elements for programmable multifunctional nanophotonics hardware [32]. However, their versatility comes at the cost of higher computational resources (should one iteratively adjust their settings according to externally-processed unitary decompositions [31]) or of a less intuitive dependency of the output on the internal components [48,49]. 3 Non-ideal detection efficiency can be counteracted with a control feedback, by sending again a photon to the same decision tree if, in the previous time bin, no photon was collected.…”

Section: Decision Trees As Linear Optical Circuitsmentioning

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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Direct dialling of Haar random unitary matrices

Cited by 86 publications

References 31 publications

Robust Architecture for Programmable Universal Unitaries

Robust Architecture for Programmable Universal Unitaries

Classical boson sampling algorithms with superior performance to near-term experiments

Photonic architecture for reinforcement learning

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