20 research outputs found
Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an
important in-silico tool for discovering new drugs in a faster and
cost-effective manner, especially for emerging diseases such as COVID-19. In
this paper, we propose a general-purpose framework combining a classical
Support Vector Classifier (SVC) algorithm with quantum kernel estimation for
LB-VS on real-world databases, and we argue in favor of its prospective quantum
advantage. Indeed, we heuristically prove that our quantum integrated workflow
can, at least in some relevant instances, provide a tangible advantage compared
to state-of-art classical algorithms operating on the same datasets, showing
strong dependence on target and features selection method. Finally, we test our
algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing
that hardware simulations provide results in line with the predicted
performances and can surpass classical equivalents.Comment: 16 pages, 7 figure
Algorithmic Error Mitigation Scheme for Current Quantum Processors
We present a hardware agnostic error mitigation algorithm for near term
quantum processors inspired by the classical Lanczos method. This technique can
reduce the impact of different sources of noise at the sole cost of an increase
in the number of measurements to be performed on the target quantum circuit,
without additional experimental overhead. We demonstrate through numerical
simulations and experiments on IBM Quantum hardware that the proposed scheme
significantly increases the accuracy of cost functions evaluations within the
framework of variational quantum algorithms, thus leading to improved
ground-state calculations for quantum chemistry and physics problems beyond
state-of-the-art results