Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem.

The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances. We perform noiseless simulations with up to 40 qubits and observe that the runtime of QAOA with fixed parameters scales better than branch-and-bound solvers, which are the state-of-the-art exact solvers for LABS. The combination of QAOA with quantum minimum finding gives the best empirical scaling of any algorithm for the LABS problem. We demonstrate experimental progress in executing QAOA for the LABS problem using an algorithm-specific error detection scheme on Quantinuum trapped-ion processors. Our results provide evidence for the utility of QAOA as an algorithmic component that enables quantum speedups.

Towards provably efficient quantum algorithms for large-scale machine-learning models.

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as [Formula: see text], where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.

Analytical nonadiabatic coupling and state-specific energy gradient for the crystal field Hamiltonian describing lanthanide single-ion magnets.

Paramagnetic molecules with a metal ion as an electron spin center are promising building blocks for molecular qubits and high-density memory arrays. However, fast spin relaxation and decoherence in these molecules lead to a rapid loss of magnetization and quantum information. Nonadiabatic coupling (NAC), closely related to spin-vibrational coupling, is the main source of spin relaxation and decoherence in paramagnetic molecules at higher temperatures. Predicting these couplings using numerical differentiation requires a large number of computationally intensive ab initio or crystal field electronic structure calculations. To reduce computational cost and improve accuracy, we derive and implement analytical NAC and state-specific energy gradient for the ab initio parametrized crystal field Hamiltonian describing single-ion molecular magnets. Our implementation requires only a single crystal field calculation. In addition, the accurate NACs and state-specific energy gradients can be used to model spin relaxation using sophisticated nonadiabatic molecular dynamics, which avoids the harmonic approximation for molecular vibrations. To test our implementation, we calculate the NAC values for three lanthanide complexes. The predicted values support the relaxation mechanisms reported in previous studies.

Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansätze for Hybrid Variational Quantum Computing.

One of the commonly used chemically inspired approaches in variational quantum computing is the unitary coupled-cluster (UCC) ansätze. Despite being a systematic way of approaching the exact limit, the number of parameters in the standard UCC ansätze exhibits unfavorable scaling with respect to the system size, hindering its practical use on near-term quantum devices. Efforts have been taken to propose some variants of the UCC ansätze with better scaling. In this paper, we explore the parameter redundancy in the preparation of unitary coupled-cluster singles and doubles (UCCSD) ansätze employing spin-adapted formulation, small amplitude filtration, and entropy-based orbital selection approaches. Numerical results of using our approach on some small molecules have exhibited a significant cost reduction in the number of parameters to be optimized and in the time to convergence compared with conventional UCCSD-VQE simulations. We also discuss the potential application of some machine learning techniques in further exploring the parameter redundancy, providing a possible direction for future studies.

Localized Quantum Chemistry on Quantum Computers.

Quantum chemistry calculations of large, strongly correlated systems are typically limited by the computation cost that scales exponentially with the size of the system. Quantum algorithms, designed specifically for quantum computers, can alleviate this, but the resources required are still too large for today's quantum devices. Here, we present a quantum algorithm that combines a localization of multireference wave functions of chemical systems with quantum phase estimation (QPE) and variational unitary coupled cluster singles and doubles (UCCSD) to compute their ground-state energy. Our algorithm, termed "local active space unitary coupled cluster" (LAS-UCC), scales linearly with the system size for certain geometries, providing a polynomial reduction in the total number of gates compared with QPE, while providing accuracy above that of the variational quantum eigensolver using the UCCSD ansatz and also above that of the classical local active space self-consistent field. The accuracy of LAS-UCC is demonstrated by dissociating (H2)2 into two H2 molecules and by breaking the two double bonds in trans-butadiene, and resource estimates are provided for linear chains of up to 20 H2 molecules.

Dissecting protein tyrosine phosphatase signaling by engineered chemogenetic control of its activity.

Protein tyrosine phosphatases (PTPases) are critical mediators of dynamic cell signaling. A tool capable of identifying transient signaling events downstream of PTPases is essential to understand phosphatase function on a physiological time scale. We report a broadly applicable protein engineering method for allosteric regulation of PTPases. This method enables dissection of transient events and reconstruction of individual signaling pathways. Implementation of this approach for Shp2 phosphatase revealed parallel MAPK and ROCK II dependent pathways downstream of Shp2, mediating transient cell spreading and migration. Furthermore, we show that the N-SH2 domain of Shp2 regulates MAPK-independent, ROCK II-dependent cell migration. Engineered targeting of Shp2 activity to different protein complexes revealed that Shp2-FAK signaling induces cell spreading whereas Shp2-Gab1 or Shp2-Gab2 mediates cell migration. We identified specific transient morphodynamic processes induced by Shp2 and determined the role of individual signaling pathways downstream of Shp2 in regulating these events. Broad application of this approach is demonstrated by regulating PTP1B and PTP-PEST phosphatases.

Structure- and Interaction-Based Design of Anti-SARS-CoV-2 Aptamers.

Aptamer selection against novel infections is a complicated and time-consuming approach. Synergy can be achieved by using computational methods together with experimental procedures. This study aims to develop a reliable methodology for a rational aptamer in silico et vitro design. The new approach combines multiple steps: (1) Molecular design, based on screening in a DNA aptamer library and directed mutagenesis to fit the protein tertiary structure; (2) 3D molecular modeling of the target; (3) Molecular docking of an aptamer with the protein; (4) Molecular dynamics (MD) simulations of the complexes; (5) Quantum-mechanical (QM) evaluation of the interactions between aptamer and target with further analysis; (6) Experimental verification at each cycle for structure and binding affinity by using small-angle X-ray scattering, cytometry, and fluorescence polarization. By using a new iterative design procedure, structure- and interaction-based drug design (SIBDD), a highly specific aptamer to the receptor-binding domain of the SARS-CoV-2 spike protein, was developed and validated. The SIBDD approach enhances speed of the high-affinity aptamers development from scratch, using a target protein structure. The method could be used to improve existing aptamers for stronger binding. This approach brings to an advanced level the development of novel affinity probes, functional nucleic acids. It offers a blueprint for the straightforward design of targeting molecules for new pathogen agents and emerging variants.

The role of SAXS and molecular simulations in 3D structure elucidation of a DNA aptamer against lung cancer.

Aptamers are short, single-stranded DNA or RNA oligonucleotide molecules that function as synthetic analogs of antibodies and bind to a target molecule with high specificity. Aptamer affinity entirely depends on its tertiary structure and charge distribution. Therefore, length and structure optimization are essential for increasing aptamer specificity and affinity. Here, we present a general optimization procedure for finding the most populated atomistic structures of DNA aptamers. Based on the existed aptamer LC-18 for lung adenocarcinoma, a new truncated LC-18 (LC-18t) aptamer LC-18t was developed. A three-dimensional (3D) shape of LC-18t was reported based on small-angle X-ray scattering (SAXS) experiments and molecular modeling by fragment molecular orbital or molecular dynamic methods. Molecular simulations revealed an ensemble of possible aptamer conformations in solution that were in close agreement with measured SAXS data. The aptamer LC-18t had stronger binding to cancerous cells in lung tumor tissues and shared the binding site with the original larger aptamer. The suggested approach reveals 3D shapes of aptamers and helps in designing better affinity probes.

Ab initio molecular dynamics on quantum computers.

Ab initio molecular dynamics (AIMD) is a valuable technique for studying molecules and materials at finite temperatures where the nuclei evolve on potential energy surfaces obtained from accurate electronic structure calculations. In this work, we present an approach to running AIMD simulations on noisy intermediate-scale quantum (NISQ)-era quantum computers. The electronic energies are calculated on a quantum computer using the variational quantum eigensolver (VQE) method. Algorithms for computation of analytical gradients entirely on a quantum computer require quantum fault-tolerant hardware, which is beyond NISQ-era. Therefore, we compute the energy gradients numerically using finite differences, the Hellmann-Feynman theorem, and a correlated sampling technique. This method only requires additional classical calculations of electron integrals for each degree of freedom without any additional computations on a quantum computer beyond the initial VQE run. As a proof of concept, AIMD simulations are demonstrated for the H2 molecule on IBM quantum devices. In addition, we demonstrate the validity of the method for larger molecules using full configuration interaction wave functions. As quantum hardware and noise mitigation techniques continue to improve, the method can be utilized for studying larger molecular systems.

Computational Methods for Biochemical Simulations Implemented in GAMESS.

Computational methods for modeling biochemical processes implemented in GAMESS package are reviewed; in particular, quantum mechanics combined with molecular mechanics (QM/MM), semi-empirical, and fragmentation approaches. A detailed summary of capabilities is provided for the QM/MM implementation in QuanPol program and the fragment molecular orbital (FMO) method. Molecular modeling and visualization packages useful for biochemical simulations with GAMESS are described. GAMESS capabilities with corresponding references are tabulated for reader's convenience.

Locating Minimum Energy Crossings of Different Spin States Using the Fragment Molecular Orbital Method.

Spin-dependent processes involving nonadiabatic transitions between electronic states with different spin multiplicities play important roles in the chemistry of complex systems. The rates of these processes can be predicted based on the molecular properties at the minimum energy crossing point (MECP) between electronic states. We present the development of the MECP search technique within the fragment molecular orbital (FMO) method applicable to large complex systems. The accuracy and scalability of the new method is demonstrated on several models of the metal-sulfur protein rubredoxin. The effect of the model size on the MECP geometry and relative energy is discussed. The fragment energy decomposition and spin density delocalization analyses reveal how different protein residues and solvent molecules contribute to stabilization of the spin states. The developed FMO-MECP method can help to clarify the role of nonadiabatic spin-dependent processes in complex systems and can be used for designing mutations aimed at controlling these processes in metalloproteins, including spin-dependent catalysis and electron transfer.

Molecular Electrostatic Potential and Electron Density of Large Systems in Solution Computed with the Fragment Molecular Orbital Method.

A solvent screening model for the molecular electrostatic potential is developed for the fragment molecular orbital method combined with the polarizable continuum model at the Hartree-Fock and density functional theory levels. The accuracy of the generated potentials is established in comparison to calculations without fragmentation. Solvent effects upon the molecular electrostatic potential and electron density of solute are discussed. The method is applied to two proteins: chignolin (PDB: 1UAO ) and ovine prostaglandin H(2) synthase-1 ( 1EQG ).

A systematic study of minima in alanine dipeptide.

The alanine dipeptide is a standard system to model dihedral angles in proteins. It is shown that obtaining the Ramachandran plot accurately is a hard problem because of many local minima; depending on the details of geometry optimizations, different Ramachandran plots can be obtained. To locate all energy minima, starting from geometries from MD simulations, 250,000 geometry optimizations were performed at the level of RHF/6-31G*, followed by re-optimizations of the located 827 minima at the level of MP2/6-311++G**, yielding 30 unique minima, most of which were not previously reported in literature. Both in vacuo and solvated structures are discussed. The minima are systematically categorized based on four backbone dihedral angles. The Gibbs energies are evaluated and the structural factors determining the relative stabilities of conformers are discussed. © 2018 Wiley Periodicals, Inc.

Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks.

BACKGROUND: Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures. In this work, we implement a low-latency Multi-Layer Perceptron (MLP) processor using Field Programmable Gate Arrays (FPGAs). Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. Moreover, the compute pipelines themselves are tailored specifically to the application, improving resource utilization and reducing idle cycles. We demonstrate the effectiveness of our approach using mass-spectrometry data sets for real-time cancer detection. RESULTS: We demonstrate that correct parameter sizing, based on the application, can reduce latency by 20% on average. Furthermore, we show that in an application with tightly coupled data-path and latency constraints, having a large amount of computing resources can actually reduce performance. Using mass-spectrometry benchmarks, we show that our proposed FPGA design outperforms both CPU and GPU implementations, with an average speedup of 144x and 21x, respectively. CONCLUSION: In our work, we demonstrate the importance of application-specific optimizations in order to minimize latency and maximize resource utilization for MLP inference. By directly interfacing and processing sensor data with ultra-low latency, FPGAs can perform real-time analysis during procedures and provide diagnostic feedback that can be critical to achieving higher percentages of successful patient outcomes.

Ligand binding to an Allergenic Lipid Transfer Protein Enhances Conformational Flexibility resulting in an Increase in Susceptibility to Gastroduodenal Proteolysis.

Non-specific lipid transfer proteins (LTPs) are a family of lipid-binding molecules that are widely distributed across flowering plant species, many of which have been identified as allergens. They are highly resistant to simulated gastroduodenal proteolysis, a property that may play a role in determining their allergenicity and it has been suggested that lipid binding may further increase stability to proteolysis. It is demonstrated that LTPs from wheat and peach bind a range of lipids in a variety of conditions, including those found in the gastroduodenal tract. Both LTPs are initially cleaved during gastroduodenal proteolysis at three major sites between residues 39-40, 56-57 and 79-80, with wheat LTP being more resistant to cleavage than its peach ortholog. The susceptibility of wheat LTP to proteolyic cleavage increases significantly upon lipid binding. This enhanced digestibility is likely to be due to the displacement of Tyr79 and surrounding residues from the internal hydrophobic cavity upon ligand binding to the solvent exposed exterior of the LTP, facilitating proteolysis. Such knowledge contributes to our understanding as to how resistance to digestion can be used in allergenicity risk assessment of novel food proteins, including GMOs.

Importance of Three-Body Interactions in Molecular Dynamics Simulations of Water Demonstrated with the Fragment Molecular Orbital Method.

The analytic first derivative with respect to nuclear coordinates is formulated and implemented in the framework of the three-body fragment molecular orbital (FMO) method. The gradient has been derived and implemented for restricted second-order Møller-Plesset perturbation theory, as well as for both restricted and unrestricted Hartree-Fock and density functional theory. The importance of the three-body fully analytic gradient is illustrated through the failure of the two-body FMO method during molecular dynamics simulations of a small water cluster. The parallel implementation of the fragment molecular orbital method, its parallel efficiency, and its scalability on the Blue Gene/Q architecture up to 262,144 CPU cores are also discussed.

Potent inhibition of VEGFR-2 activation by tight binding of green tea epigallocatechin gallate and apple procyanidins to VEGF: relevance to angiogenesis.

SCOPE: Excessive concentrations of vascular endothelial growth factor (VEGF) drive angiogenesis and cause complications such as increased growth of tumours and atherosclerotic plaques. The aim of this study was to determine the molecular mechanism underlying the potent inhibition of VEGF signalling by polyphenols. METHODS AND RESULTS: We show that the polyphenols epigallocatechin gallate from green tea and procyanidin oligomers from apples potently inhibit VEGF-induced VEGF receptor-2 (VEGFR-2) signalling in human umbilical vein endothelial cells by directly interacting with VEGF. The polyphenol-induced inhibition of VEGF-induced VEGFR-2 activation occurred at nanomolar polyphenol concentrations and followed bi-phasic inhibition kinetics. VEGF activity could not be recovered by dialysing VEGF-polyphenol complexes. Exposure of VEGF to epigallocatechin gallate or procyanidin oligomers strongly inhibited subsequent binding of VEGF to human umbilical vein endothelial cells expressing VEGFR-2. Remarkably, even though VEGFR-2 signalling was completely inhibited at 1 µM concentrations of polyphenols, endothelial nitric oxide synthase was shown to still be activated via the PI3K/Akt signalling pathway which is downstream of VEGFR-2. CONCLUSION: These data demonstrate for the first time that VEGF is a key molecular target for specific polyphenols found in tea, apples and cocoa which potently inhibit VEGF signalling and angiogenesis at physiological concentrations. These data provide a plausible mechanism which links bioactive compounds in food with their beneficial effects.

Effective ion mobility calculations for macromolecules by scattering on electron clouds.

Broad commercialization and increasing resolving power of ion mobility spectrometry/mass spectrometry (IMS/MS) platforms have engendered an explosion of IMS applications to structural characterization of gas-phase biomolecules. That has renewed interest in more accurate and rapid ion mobility calculations that are needed to elicit ion geometries from the measurements. An approach based on scattering on electron density isosurfaces (SEDI) that mirrors the physics of molecular collisions was proven superior to the common methods involving atomic coordinates a decade ago but has remained impractical for large ions because of extreme computational demands. Here, we accelerate SEDI by up to ∼500 times using the fragment molecular orbital approach for surface generation and the multiplexed scattering algorithm in conjunction with the new grid extrapolation procedure for cross section evaluations. Parallelization of the code on a supercomputer has produced major further speed gains, allowing SEDI calculations for proteins (defined by over a million surface points) with a precision of <0.1% in 1 min. Initial tests reveal the anticipated dependence of mobility on the ion charge state and lower cross sections in view of reduced surface roughness. Present developments are expected to lead to broad application of SEDI in IMS studies of macromolecules, enabling more accurate and reliable structural assignments.

GAMESS as a free quantum-mechanical platform for drug research.

Driven by a steady improvement of computational hardware and significant progress in ab initio method development, quantum-mechanical approaches can now be applied to large biochemical systems and drug design. We review the methods implemented in GAMESS, which are suitable to calculate large biochemical systems. An emphasis is put on the fragment molecular orbital method (FMO) and quantum mechanics interfaced with molecular mechanics (QM/MM). The use of FMO in the protein-ligand binding, structure-activity relationship (SAR) studies, fragment- and structure-based drug design (FBDD/SBDD) is discussed in detail.

Responsiveness of the major birch allergen Bet v 1 scaffold to the gastric environment: impact on structure and allergenic activity.

SCOPE: Four Bet v 1 homologous food allergens from celeriac (rApi g 1), apple (rMal d 1), peach (rPru p 1) and hazelnut (rCor a 1), were used to probe the structural responsiveness of the Bet v 1 scaffold to gastric digestion conditions and its impact on allergenicity. METHODS AND RESULTS: Low pH induced conformational changes of all homologues, which was reduced at physiological ionic strength for all except rPru p 1 as observed by circular dichroism (CD)-spectroscopy. The homologues were rapidly digested by pepsin, losing their IgE binding activity, although the kinetics and patterns of digestion varied subtly between homologues, rApi g 1 being the most stable. We have demonstrated for the first time that gastric phosphatidyl-choline (PC) induced conformational changes in all homologues but only rMal d 1 penetrated the PC vesicles as detected by fluorescence polarization, slowing its digestion and retaining more of its allergenic activity. PC enhanced basophil activation of all digested allergens except rApi g 1. CONCLUSION: The Bet v 1 scaffold is generally susceptible to low pH and pepsinolysis and interacts with PC vesicles, properties which can explain effects of the gastric environment on their allergenicity. These data show the importance of including surfactants in model digestion systems.