Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning

Pepper, Alex; Tischler, Nora; Pryde, Geoff J.

doi:10.1103/physrevlett.122.060501

Cited by 94 publications

(60 citation statements)

References 25 publications

(40 reference statements)

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“…Our platform, with the incorporation of non-classical light sources (e.g., single photon Fock states) and photon number resolving detectors, thus provides a promising avenue for their realization. Our platform can also be used to demonstrate some specific algorithms, such as quantum variational autoencoder 63 and quantum generative adversarial networks 64 .…”

Section: Discussionmentioning

confidence: 99%

An optical neural chip for implementing complex-valued neural network

et al. 2021

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Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.

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Section: Discussionmentioning

confidence: 99%

An optical neural chip for implementing complex-valued neural network

et al. 2021

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“…Numerical simulations. As an important example to illustrate the cost-function-dependent barren plateau phenomenon, we consider quantum autoencoders 11,[41][42][43][44] . In particular, the pioneering VQA proposed in ref.…”

Section: Corollarymentioning

confidence: 99%

Cost function dependent barren plateaus in shallow parametrized quantum circuits

et al. 2021

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Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining C in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when V(θ) is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining C with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of V(θ) is $${\mathcal{O}}(\mathrm{log}\,n)$$ O ( log n ) . Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation.

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“…Depending on the problem at hand the speedup can be associated with various features of quantum physics. A number of proposals and experiments focused on QML have been reported, such works include for instance quantum support vector machines [ mann machines [3], quantum autoencoders [4], kernel methods [5], and quantum reinforcement learning [6,7]. In reinforcement learning a learning agent receives feedback in order to learn an optimal strategy for handling a nontrivial task.…”

Section: Introductionmentioning

confidence: 99%

Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

et al. 2019

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We report on experimental implementation of a machine-learned quantum gate driven by a classical control. The gate learns optimal phase-covariant cloning in a reinforcement learning scenario having fidelity of the clones as reward. In our experiment, the gate learns to achieve nearly optimal cloning fidelity allowed for this particular class of states. This makes it a proof of present-day feasibility and practical applicability of the hybrid machine learning approach combining quantum information processing with classical control. Moreover, our experiment can be directly generalized to larger interferometers where the computational cost of classical computer is much lower than the cost of boson sampling.

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Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning

Cited by 94 publications

References 25 publications

An optical neural chip for implementing complex-valued neural network

An optical neural chip for implementing complex-valued neural network

Cost function dependent barren plateaus in shallow parametrized quantum circuits

Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

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