29 research outputs found
Quantum autoencoders via quantum adders with genetic algorithms
The quantum autoencoder is a recent paradigm in the field of quantum machine
learning, which may enable an enhanced use of resources in quantum
technologies. To this end, quantum neural networks with less nodes in the inner
than in the outer layers were considered. Here, we propose a useful connection
between approximate quantum adders and quantum autoencoders. Specifically, this
link allows us to employ optimized approximate quantum adders, obtained with
genetic algorithms, for the implementation of quantum autoencoders for a
variety of initial states. Furthermore, we can also directly optimize the
quantum autoencoders via genetic algorithms. Our approach opens a different
path for the design of quantum autoencoders in controllable quantum platforms
Quantum autoencoders via quantum adders with genetic algorithms
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms. (c) 2018 IOP Publishing Ltd.The authors acknowledge support from Spanish MINECO FIS2015-69983-P, Ram贸n y Cajal Grant RYC-2012-11391, UPV/EHU Postdoctoral Grant, and Basque Government Postdoctoral Grant POS_2017_1_0022 and IT986-16
Quantum Machine Learning Implementations: Proposals and Experiments
This article gives an overview and a perspective of recent theoretical
proposals and their experimental implementations in the field of quantum
machine learning. Without an aim to being exhaustive, the article reviews
specific high-impact topics such as quantum reinforcement learning, quantum
autoencoders, and quantum memristors, and their experimental realizations in
the platforms of quantum photonics and superconducting circuits. The field of
quantum machine learning could be among the first quantum technologies
producing results that are beneficial for industry and, in turn, to society.
Therefore, it is necessary to push forward initial quantum implementations of
this technology, in Noisy Intermediate-Scale Quantum Computers, aiming for
achieving fruitful calculations in machine learning that are better than with
any other current or future computing paradigm.Comment: Invited Perspective for Advanced Quantum Technologie
Quantum machine learning and quantum biomimetics: A perspective
Quantum machine learning has emerged as an exciting and promising paradigm
inside quantum technologies. It may permit, on the one hand, to carry out more
efficient machine learning calculations by means of quantum devices, while, on
the other hand, to employ machine learning techniques to better control quantum
systems. Inside quantum machine learning, quantum reinforcement learning aims
at developing "intelligent" quantum agents that may interact with the outer
world and adapt to it, with the strategy of achieving some final goal. Another
paradigm inside quantum machine learning is that of quantum autoencoders, which
may allow one for employing fewer resources in a quantum device via a training
process. Moreover, the field of quantum biomimetics aims at establishing
analogies between biological and quantum systems, to look for previously
inadvertent connections that may enable useful applications. Two recent
examples are the concepts of quantum artificial life, as well as of quantum
memristors. In this Perspective, we give an overview of these topics,
describing the related research carried out by the scientific community.Comment: Invited Perspective article for Machine Learning: Science and
Technology, 17 pages, 6 figures, 110 reference
On compression rate of quantum autoencoders: Control design, numerical and experimental realization
Quantum autoencoders which aim at compressing quantum information in a
low-dimensional latent space lie in the heart of automatic data compression in
the field of quantum information. In this paper, we establish an upper bound of
the compression rate for a given quantum autoencoder and present a learning
control approach for training the autoencoder to achieve the maximal
compression rate. The upper bound of the compression rate is theoretically
proven using eigen-decomposition and matrix differentiation, which is
determined by the eigenvalues of the density matrix representation of the input
states. Numerical results on 2-qubit and 3-qubit systems are presented to
demonstrate how to train the quantum autoencoder to achieve the theoretically
maximal compression, and the training performance using different machine
learning algorithms is compared. Experimental results of a quantum autoencoder
using quantum optical systems are illustrated for compressing two 2-qubit
states into two 1-qubit states
Quantum Machine Learning: A tutorial
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.Ministerio de Ciencia, Innovaci贸n y Universidades GC2018-095113-B-I00,PID2019-104002GB-C21, and PID2019-104002GB-C22 (MCIU/AEI/FEDER, UE
A generative modeling approach for benchmarking and training shallow quantum circuits
Hybrid quantum-classical algorithms provide ways to use noisy
intermediate-scale quantum computers for practical applications. Expanding the
portfolio of such techniques, we propose a quantum circuit learning algorithm
that can be used to assist the characterization of quantum devices and to train
shallow circuits for generative tasks. The procedure leverages quantum hardware
capabilities to its fullest extent by using native gates and their qubit
connectivity. We demonstrate that our approach can learn an optimal preparation
of the Greenberger-Horne-Zeilinger states, also known as "cat states". We
further demonstrate that our approach can efficiently prepare approximate
representations of coherent thermal states, wave functions that encode
Boltzmann probabilities in their amplitudes. Finally, complementing proposals
to characterize the power or usefulness of near-term quantum devices, such as
IBM's quantum volume, we provide a new hardware-independent metric called the
qBAS score. It is based on the performance yield in a specific sampling task on
one of the canonical machine learning data sets known as Bars and Stripes. We
show how entanglement is a key ingredient in encoding the patterns of this data
set; an ideal benchmark for testing hardware starting at four qubits and up. We
provide experimental results and evaluation of this metric to probe the trade
off between several architectural circuit designs and circuit depths on an
ion-trap quantum computer.Comment: 16 pages, 9 figures. Minor revisions. As published in npj Quantum
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