Adaptive Learning for Quantum Linear Regression
<|reference_start|>Adaptive Learning for Quantum Linear Regression: The recent availability of quantum annealers as cloud-based services has enabled new ways to handle machine learning problems, and several relevant algorithms have been adapted to run on these devices. In a recent work, linear regression was formulated as a quadratic binary optimization problem that can be solved via quantum annealing. Although this approach promises a computational time advantage for large datasets, the quality of the solution is limited by the necessary use of a precision vector, used to approximate the real-numbered regression coefficients in the quantum formulation. In this work, we focus on the practical challenge of improving the precision vector encoding: instead of setting an array of generic values equal for all coefficients, we allow each one to be expressed by its specific precision, which is tuned with a simple adaptive algorithm. This approach is evaluated on synthetic datasets of increasing size, and linear regression is solved using the D-Wave Advantage quantum annealer, as well as classical solvers. To the best of our knowledge, this is the largest dataset ever evaluated for linear regression on a quantum annealer. The results show that our formulation is able to deliver improved solution quality in all instances, and could better exploit the potential of current quantum devices.<|reference_end|>
@article{carugno2024adaptive,
    title={Adaptive Learning for Quantum Linear Regression},
    author={Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi},
    journal={arXiv preprint arXiv:2408.02833},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02833},
    primaryClass={quant-ph cs.LG}
}