Quantum learning of coherent states
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In this paper, a quantum learning scheme for binary discrimination of coherent states of light was developed for the reading of information stored in a digital memory, where a coherent light source is used to illuminate a memory cell and retrieve its encoded bit by determining the quantum state of the reflected signal.Abstract:
We develop a quantum learning scheme for binary discrimination of coherent states of light. This is a problem of technological relevance for the reading of information stored in a digital memory. In our setting, a coherent light source is used to illuminate a memory cell and retrieve its encoded bit by determining the quantum state of the reflected signal. We consider a situation where the amplitude of the states produced by the source is not fully known, but instead this information is encoded in a large training set comprising many copies of the same coherent state. We show that an optimal global measurement, performed jointly over the signal and the training set, provides higher successful identification rates than any learning strategy based on first estimating the unknown amplitude by means of Gaussian measurements on the training set, followed by an adaptive discrimination procedure on the signal. By considering a simplified variant of the problem, we argue that this is the case even for non-Gaussian estimation measurements. Our results show that, even in absence of entanglement, collective quantum measurements yield an enhancement in the readout of classical information, which is particularly relevant in the operating regime of low-energy signals.read more
Citations
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Journal ArticleDOI
Quantum machine learning
Jacob Biamonte,Jacob Biamonte,Peter Wittek,Nicola Pancotti,Patrick Rebentrost,Nathan Wiebe,Seth Lloyd +6 more
TL;DR: The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers.
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Machine learning & artificial intelligence in the quantum domain: a review of recent progress.
TL;DR: In this article, the authors describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain, and discuss the fundamental issue of quantum generalizations of learning and AI concepts.
Posted Content
Machine learning \& artificial intelligence in the quantum domain
Vedran Dunjko,Hans J. Briegel +1 more
TL;DR: The main ideas, recent developments and progress are described in a broad spectrum of research investigating ML and AI in the quantum domain, investigating how results and techniques from one field can be used to solve the problems of the other.
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Quantum generalisation of feedforward neural networks
TL;DR: It is demonstrated numerically that the proposed quantum generalisation of a classical neural network can compress quantum states onto a minimal number of qubits, create a quantum autoencoder, and discover quantum communication protocols such as teleportation.
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Quantum generalisation of feedforward neural networks
TL;DR: In this article, the classical neurons are first rendered reversible by adding ancillary bits, and then they are generalised to being quantum reversible, i.e. unitary.
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