quantum state pairs, quantum algorithms, classical machine learning
Developing quantum algorithms has proven to be very difficult. In this paper, the concept of using classical machine learning techniques to derive quantum operators from examples is presented. A gradient descent algorithm for learning unitary operators from quantum state pairs is developed as a starting point to aid in developing quantum algorithms. The algorithm is used to learn the quantum Fourier transform, an underconstrained two-bit function, and Grover’s iterate.
Original Publication Citation
Neil Toronto and Dan Ventura, "Learning Quantum Operators from Quantum State Pairs", Proceedings of the IEEE Congress on Evolutionary Computation, pp. 9157-9162, July 26.
BYU ScholarsArchive Citation
Toronto, Neil and Ventura, Dan A., "Learning Quantum Operators From Quantum State Pairs" (2006). All Faculty Publications. 307.
Physical and Mathematical Sciences
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