Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
Performance of hybrid quantum/classical variational heuristics for combinatorial optimization
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abstract
The recent literature on near-term applications for quantum computers contains several examples of the applications of hybrid quantum/classical variational approaches. This methodology can be applied to a variety of optimization problems, but its practical performance is not well studied yet. This paper moves some steps in the direction of characterizing the practical performance of the methodology, in the context of finding solutions to classical combinatorial optimization problems. Our study is based on numerical results obtained applying several classical nonlinear optimization algorithms to Hamiltonians for six combinatorial optimization problems; the experiments are conducted via noise-free classical simulation of the quantum circuits implemented in Qiskit. We empirically verify that: (1) finding the ground state is harder for Hamiltonians with many Pauli terms; (2) classical global optimization methods are more successful than local methods due to their ability of avoiding the numerous local optima; (3) there does not seem to be a clear advantage in introducing entanglement in the variational form.
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UNVERDICTED 2representative citing papers
Numerical benchmarks identify a minimum problem size where variational quantum circuits for Max-Cut outperform sampling on average, with quantified separation from greedy methods and instance-level performance correlations.
citing papers explorer
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Learning to learn with quantum neural networks via classical neural networks
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
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Benchmarking Variational Quantum Algorithms for Combinatorial Optimization in Practice
Numerical benchmarks identify a minimum problem size where variational quantum circuits for Max-Cut outperform sampling on average, with quantified separation from greedy methods and instance-level performance correlations.