Quantum Approximate Optimization Algorithm applied to the binary perceptron
This work addresses a supervised learning task in neural networks, but it is incremental as it applies existing quantum methods to a new classical problem with non-local interactions.
The researchers tackled the problem of optimizing synaptic weights for the binary perceptron using quantum algorithms, specifically digitized Quantum Annealing (QA) and Quantum Approximate Optimization Algorithm (QAOA), and found that QAOA outperformed traditional QA with evidence of optimal, transferable parameter solutions.
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.