Quantum computing in pharma: A multilayer embedding approach for near future applications
Robert Izsak,Christoph Riplinger,Nick S. Blunt,Bernardo de Souza,Nicole Holzmann,Ophelia Crawford,Joan Camps,Frank Neese,Patrick Schopf +8 more
TL;DR: In this article , a scheme for selecting an active space automatically is described and simulated results obtained using both the quantum phase estimation (QPE) and variational quantum eigensolver (VQE) algorithms are presented and combined with a subtractive method to enable accurate description of the environment.
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Abstract: Quantum computers are special purpose machines that are expected to be particularly useful in simulating strongly correlated chemical systems. The quantum computer excels at treating a moderate number of orbitals within an active space in a fully quantum mechanical manner. We present a quantum phase estimation calculation on F2 in a (2,2) active space on Rigetti's Aspen‐11 QPU. While this is a promising start, it also underlines the need for carefully selecting the orbital spaces treated by the quantum computer. In this work, a scheme for selecting such an active space automatically is described and simulated results obtained using both the quantum phase estimation (QPE) and variational quantum eigensolver (VQE) algorithms are presented and combined with a subtractive method to enable accurate description of the environment. The active occupied space is selected from orbitals localized on the chemically relevant fragment of the molecule, while the corresponding virtual space is chosen based on the magnitude of interactions with the occupied space calculated from perturbation theory. This protocol is then applied to two chemical systems of pharmaceutical relevance: the enzyme [Fe] hydrogenase and the photosenzitizer temoporfin. While the sizes of the active spaces currently amenable to a quantum computational treatment are not enough to demonstrate quantum advantage, the procedure outlined here is applicable to any active space size, including those that are outside the reach of classical computation.
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Citations
Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications
Nick S. Blunt,Joan Camps,Ophelia Crawford,R'obert Izs'ak,S. Leontica,Arjun Mirani,A Moylett,Sam A. Scivier,Christoph Sunderhauf,Patrick Schopf,Jacob M. Taylor,Nicole Holzmann +11 more
TL;DR: In this paper , the authors compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib.
The state of quantum computing applications in health and medicine
Frederik F. Flöther
TL;DR: Quantum computing has made significant strides in healthcare and medicine, with a focus on genomics, clinical research and discovery, diagnostics, and treatments and interventions.
A Vision for the Future of Multiscale Modeling
Matteo Capone,Marco Romanelli,Davide Castaldo,Giovanni Parolin,Alessandro Bello,Gabriel Gil,Mirko Vanzan +6 more
TL;DR: Multiscale modeling is a powerful tool for simulating complex systems across various length and time scales. It involves coupling theories and algorithms at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics. Multiscale modeling has achieved remarkable results and holds great potential for future advancements in physical chemistry.
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Statistical Phase Estimation and Error Mitigation on a Superconducting Quantum Processor
Nick S. Blunt,Laura Caune,Róbert Izsák,Earl T. Campbell,Nicole Holzmann +4 more
TL;DR: Statistical phase estimation is a quantum algorithm that can estimate energies with high accuracy on fault-tolerant quantum computers. It has benefits on early fault-tolerant devices, including shorter circuits and better suitability for error-mitigation techniques.
Accelerating reliable multiscale quantum refinement of protein–drug systems enabled by machine learning
Zeyin Yan,Dacong Wei,Xin Li,Lung Wa Chung +3 more
TL;DR: Researchers developed a machine learning-accelerated quantum refinement method for protein-drug systems, achieving QM-level accuracy with higher efficiency, and demonstrated its application in refining the structure of a FDA-approved drug, nirmatrelvir, in a SARS-CoV-2 main protease.
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