An Evolutionary Deep Learning Approach for Efficient Quantum Algorithms Transpilation

Gate-based quantum computation describes algorithms as quantum circuits. These can be seen as a set of quantum gates acting on a set of qubits. To be executable, the circuit requires complex transformations to comply with the physical constraints of the machines. This process is known as transpilation, where qubits’ layout initialisation is one of its first and most challenging steps, usually done by considering the device error properties. As the size of the quantum algorithm increases, the transpilation becomes increasingly complex and time-consuming. This constitutes a bottleneck towards agile, fast, and error-robust quantum computation. This work proposes an evolutionary deep neural network that learns the qubits’ layout initialisation of the most advanced and complex IBM heuristic used in today’s quantum machines. The aim is to progressively replace weakly scalable transpilation heuristics with machine learning models. Previous work using machine learning models for qubits’ layout initialisation suffers from some shortcomings in the proposal’s correctness and generalisation as well as benchmarks diversity, utility, and availability. The present work solves those flaws by (I) devising a complete Machine Learning pipeline including the ETL component and the evolutionary deep neural model using the linkage learning algorithm P3, (II) a modelling applicable to any quantum algorithm with a special interest to both optimisation and machine learning ones, (III) diverse and fresh benchmarks using calibration data of four real IBM quantum computers collected over 10 months (Dec. 2022 and Oct. 2023) and training dataset built using four types of quantum optimisation and machine learning algorithms, as well as random ones. The proposal has been proven to be more efficient and simple than state-of-the-art deep neural models in the literature.