Molecular docking with Gaussian Boson Sampling

Abstract

Gaussian Boson Samplers are photonic quantum devices with the potential to perform intractable tasks for classical systems. As with other near-term quantum technologies, an outstanding challenge is to identify specific problems of practical interest where these devices can prove useful. Here, we show that Gaussian Boson Samplers can be used to predict molecular docking configurations, a central problem for pharmaceutical drug design. We develop an approach where the problem is reduced to finding the maximum weighted clique in a graph, and show that Gaussian Boson Samplers can be programmed to sample large-weight cliques, i.e., stable docking configurations, with high probability, even with photon losses. We also describe how outputs from the device can be used to enhance the performance of classical algorithms. To benchmark our approach, we predict the binding mode of a ligand to the tumor necrosis factor-α converting enzyme, a target linked to immune system diseases and cancer.

Broadband quadrature-squeezed vacuum and nonclassical photon number correlations from a nanophotonic device

“…If, however, the state contains the product of many such two-mode squeezed states, each of which populates a different temporal mode, and the measurement of the state cannot distinguish between photons that were detected from different temporal modes, then the second-order correlation will exhibit g (2) < 2; in the limit of very many equally populated modes, the photon number statistics become Poissonian and g (2) → 1. In quantum sampling applications using squeezed light, all relevant information is extracted directly from the photon statistics of the output ( 4 – 6 , 8 , 9 ); thus, using a squeezed light source with a highly multimode temporal structure would destroy the utility of such a machine. It is therefore important to assess the temporal mode structure of the generated squeezed states from our source ( 22 ).…”

Quantum computational chemistry

“…Extended discussion is reserved for methods which are key to understanding how quantum computers can be used to solve general chemistry problems, or articles which have made important observations on ways to make these simulations more tractable. It is beyond the scope of this review to summarise work in directions complementary to these, such as: quantum machine learning based approaches to the electronic structure problem , using quantum computers as part of a problem decomposition approach to simulation (Bauer et al, 2016;Dallaire-Demers and Wilhelm, 2016a,b;Keen et al, 2019;Kreula et al, 2016;Rubin, 2016;Rungger et al, 2019), hybrid quantum algorithms for density functional theory (Hatcher et al, 2019;Whitfield et al, 2014), relativistic quantum chemistry (Senjean, 2019;, gate based methods for simulating molecular vibrations (McArdle et al, 2018b;Sawaya and Huh, 2018;Sawaya et al, 2019), analog simulators of molecular vibrations (Chin and Huh, 2018;Clements et al, 2017;Hu et al, 2018a;Huh et al, 2015;Huh and Yung, 2017;Joshi et al, 2014;Shen et al, 2018;Sparrow et al, 2018;Wang et al, 2019), fermionic quantum computation for chemistry simulation (O'Brien et al, 2018b), quantum methods for electron-phonon systems (Macridin et al, 2018a,b;Wu et al, 2002), protein folding and molecular docking (Babbush et al, 2012;Babej et al, 2018;Banchi et al, 2019;Fingerhuth et al, 2018;Lu and Li, 2019;Perdomo et al, 2008;Robert et al, 2019), solving problems in chemistry using a quantum annealer (Babbush et al, 2014;…”

The Walrus: a library for the calculation of hafnians, Hermite polynomials and Gaussian boson sampling

“…Our package has already been used in several research efforts to understand how to generate resource states for universal quantum computing (N. Tzitrin, Bourassa, Menicucci, & Sabapathy, 2019), study the dynamics of vibrational quanta in molecules (N Quesada, 2019;Valson Jacob, Kaur, Roga, & Takeoka, 2019), and develop the applications of GBS (Bromley et al, 2019) to molecular docking (Banchi, Fingerhuth, Babej, & Arrazola, 2019), graph theory (Schuld, Brádler, Israel, Su, & Gupt, 2019), and point processes (Jahangiri, Arrazola, Quesada, & Killoran, 2019). More importantly, it has been useful in delineating when quantum computation can be simulated by classical computing resources and when it cannot (Gupt, Arrazola, Quesada, & Bromley, 2018;Killoran et al, 2019;Nicolas Quesada & Arrazola, 2019;Wu, Cheng, Zhang, Yung, & Sun, 2019).…”