Boosting the Performance of Quantum Annealers using Machine Learning
This addresses a critical bottleneck for users of quantum annealers by significantly enhancing their performance, though it is incremental as it builds on existing error correction concepts.
The paper tackled the problem of intrinsic imperfections limiting quantum annealers by introducing a machine learning-based error correction method that adjusts the input Hamiltonian, resulting in up to three orders of magnitude performance improvement and enabling the solution of a previously intractable, maximally complex problem.
Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution. Of these, quantum annealers are the only ones currently offering real world, commercial applications on as many as 5000 qubits. The size of problems that can be solved by quantum annealers is limited mainly by errors caused by environmental noise and intrinsic imperfections of the processor. We address the issue of intrinsic imperfections with a novel error correction approach, based on machine learning methods. Our approach adjusts the input Hamiltonian to maximize the probability of finding the solution. In our experiments, the proposed error correction method improved the performance of annealing by up to three orders of magnitude and enabled the solving of a previously intractable, maximally complex problem.