Quantum-Applied Machine Learning
compression and optimisation
Quantum compilation is a problem of translating a quantum algorithm to a set of low-level hardware instructions to be executed on a quantum processor.
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 optimising gate count in a quantum circuit, it is possible to significantly reduce hardware errors and increase the accuracy of quantum computation.
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.
Automated benchmarking platform for quantum compilers
We are delighted to announce the release of Arline Benchmarks, which is the first open-source automated benchmarking platform for quantum compilers!
Efficient compilation of quantum algorithms and circuit optimisation is vital in the era of noisy intermediate scaly noisy devices. While there are multiple quantum circuit compilation and optimisation frameworks available, such as Qiskit (IBM), Pytket (CQC), Cirq (Google), there is no good way to compare their performance.
Arline Benchmarks solves this problem by providing the solution for cross-benchmarking of quantum compilers. The comparison of different quantum compilation frameworks is based on a set of relevant metrics, such as final gate count, circuit depth, compiler runtime etc. Moreover, Arline Benchmarks allows user to combine circuit compilation and optimisation routines from different providers in a custom compilation pipeline to achieve the best performance. The cherry on top is a pdf report with results of benchmarking and relevant analytics that is generated in a fully automated way!
We are welcoming all quantum computer researchers and enthusiasts to explore Arline Benchmarks capabilities and perhaps design more efficient compilation strategies!