Quantum approximate optimization algorithm applied to the binary perceptron
Copyright policy )Quantum approximate optimization algorithm applied to the binary perceptron
We apply digitized Quantum Annealing (QA) and Quantum Approximate Optimization Algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks: the optimization of synaptic weights for the binary perceptron. At variance with the usual QAOA applications to MaxCut, or to quantum spin-chains ground state preparation, the classical Hamiltonian is characterized by highly non-local multi-spin interactions. Yet, we provide evidence for the existence of optimal smooth solutions for the QAOA parameters, which are transferable among typical instances of the same problem, and we prove numerically an enhanced performance of QAOA over traditional QA. We also investigate on the role of the QAOA optimization landscape geometry in this problem, showing that the detrimental effect of a gap-closing transition encountered in QA is also negatively affecting the performance of our implementation of QAOA.
14 pages, 9 figures
FOS: Computer and information sciences, Quantum Physics, Computer Science - Machine Learning, MACHINE LEARNING, QUANTUM COMPUTING, OPTIMIZATION, FOS: Physical sciences, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Quantum Physics (quant-ph), Machine Learning (cs.LG)
FOS: Computer and information sciences, Quantum Physics, Computer Science - Machine Learning, MACHINE LEARNING, QUANTUM COMPUTING, OPTIMIZATION, FOS: Physical sciences, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Quantum Physics (quant-ph), Machine Learning (cs.LG)
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