Extreme susceptibility of quantum computation to noise is one of the crucial factors that hinder the development of large-scale quantum computers. By the means of optimizing gate count in a quantum circuit, it is possible to significantly reduce hardware errors and increase the accuracy of quantum computation.
Efficient compilation and circuit optimisation (finding an optimal sequence of gates for the desired quantum computation) is of immense importance for practical applications and is necessary for further progress towards scalable quantum computing.
Optimal (or near-optimal) circuit compilation is an extremely challenging and still open problem due to additional constraints imposed by hardware configuration, such as restricted qubit connectivity and hardware-native gate set.
Arline project has been launched to optimise quantum algorithms with machine learning techniques. We believe that quantum-applied machine learning will make quantum algorithms run on NISQ computers and solve state-of-the-art computational problems.
Arline Benchmarks platform allows to benchmark various algorithms for quantum circuit mapping/compression against each other on a list of predefined hardware types and target circuit classes.
Arline Quantum is an open-source library providing basic functionality for creating and manipulating quantum circuits. It also contains a list of mock quantum hardware.