The Quantum Lottery Ticket Hypothesis

Variational Quantum Algorithms (VQAs) for problems in chemistry, optimization, and machine learning are successful for small systems. However, training these algorithms with limited quantum resources is a barrier to scaling them to larger systems. Recent works have shown that overparameterization of VQAs can alleviate these difficulties at the cost of deeper circuits. Pruning quantum circuits - a method derived from pruning classical neural networks - has been shown to be effective at reducing these costs by finding sparse approximations of overparameterized quantum circuits. The Lottery Ticket Hypothesis in classical machine learning occurs when within an untrained, overparameterized neural network, there exist sparse subnetworks that can be trained to produce similar or better accuracy than the original overparameterized network. In this work, we ask: within an untrained, overparameterized quantum circuit, does there exist a sparse subcircuit that can be trained to produce similar or better accuracy? Answering this question could help determine how pruning and overparameterization can be used to scale VQAs beyond proof-of-concept models.