Amplitude Amplification for Quadratic Unconstrained Binary Optimization with Regression Based Neural Network Bootstrapping

Cody Kearse, Daniel Koch

Keywords: Quantum Computing, Artificial Intelligence, Quantum Hybrid Algorithms, Quantum Artificial Intelligence, Quantum Machine Learning, QML, QAI, Quantum Optimization, QUBO, Quadratic Unconstrainted Binary Optimization

TL;DR: In this paper we demonstrate a neural-networks capacity to learn a critical free parameter used in Quantum Amplitude Amplification

Abstract:

A series of recent studies has demonstrated that Quantum Amplitude Amplification (QAA), the generalization of Grover’s search algorithm, is capable of solving combinatorial optimization problems using oracle operations which apply phases proportional to all possible solutions. However, the algorithm’s success is highly sensitive to a free parameter choice which must be determined before running the quantum algorithm. In this study we demonstrate the feasibility of using regression neural network architectures to predict this parameter using
only the weights and connections of a discrete objective function. We show that for both fixed length and varying length linear QUBO (quadratic unconstrained binary optimization) problems the neural network architectures can be trained to accurately predict the free parameter with sufficient error rates necessary for performing successful QAA.

Submission Number: 5