Quantum learning by measurement and feedback

Abstract

We propose an approach to quantum computing in which quantum gate strengths are parametrized by quantum degrees of freedom, and the capability of the quantum computer to perform desired tasks is monitored and gradually improved by successive feedback modifications of the coupling strength parameters. Our proposal aims at experimental implementation, scalable to computational problems too large to be simulated theoretically, and we demonstrate feasibility of our proposal with simulations on search and factoring algorithms.

6 pages, 5 figures

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• A random walk approach to quantum algorithms
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• Trapped ion chain as a neural network
• Quantum Learning Machine

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