Using machine learning for autonomous selection of optimal qubit layouts on quantum devices

In recent years, the experimental performance of quantum algorithms on real hardware has increased substantially. This is due not just to advancements in quantum hardware, but also the development of a vast array of classical preprocessing techniques. Advancements in compilation, layout selection, and quantum control have been used to push the performance of quantum devices right up to the hardware limit. Layout selection is the process by which virtual qubits in a quantum circuit representation are mapped to a subset of physical qubits on a quantum computer. Choosing a suboptimal qubit layout can be very detrimental for the performance of a quantum algorithm. On NISQ-era quantum devices, each qubit often has a unique coherence time, readout error, gate fidelity, and so on. Before executing a circuit, experts need to carefully balance these traits to select an optimal qubit layout, thus hopefully maximising the performance of the device. In this work, we explore the effects of layout selection on quantum algorithm performance. We propose a set of heuristics for performing layout selection on quantum devices. We observe that an optimal layout can increase algorithm success probability up to 3x when compared to a suboptimal layout. Using our methods, we are able to achieve substantial improvements on real hardware across a wide range of quantum algorithms such as BV, QAOA, and QFT. We also demonstrate that our tools vastly outperform existing solutions.