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Quantum computing is a computational paradigm with the potential tooutperform classical methods for a variety of problems. Proposed recently, theQuantum Approximate Optimization Algorithm (QAOA) is considered as one of theleading candidates for demonstrating quantum advantage in the near term. QAOAis a variational hybrid quantum-classical algorithm for approximately solvingcombinatorial optimization problems. The quality of the solution obtained byQAOA for a given problem instance depends on the performance of the classicaloptimizer used to optimize the variational parameters. In this paper, weformulate the problem of finding optimal QAOA parameters as a learning task inwhich the knowledge gained from solving training instances can be leveraged tofind high-quality solutions for unseen test instances. To this end, we developtwo machine-learning-based approaches. Our first approach adopts areinforcement learning (RL) framework to learn a policy network to optimizeQAOA circuits. Our second approach adopts a kernel density estimation (KDE)technique to learn a generative model of optimal QAOA parameters. In bothapproaches, the training procedure is performed on small-sized probleminstances that can be simulated on a classical computer; yet the learned RLpolicy and the generative model can be used to efficiently solve largerproblems. Extensive simulations using the IBM Qiskit Aer quantum circuitsimulator demonstrate that our proposed RL- and KDE-based approaches reduce theoptimality gap by factors up to 30.15 when compared with other commonly usedoff-the-shelf optimizers.

Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems