The phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) method is a stochastic imaginary-time projection technique for computing ground-state properties of strongly correlated quantum systems, with accuracy that depends critically on the choice of trial wavefunction. Here, we investigate ph-AFQMC with trial states prepared using parameterized quantum circuits. In this work, we present a comprehensive benchmarking study of quantum trial wavefunctions spanning unitary coupled-cluster, Hamiltonian-informed, Jastrow-inspired, and adaptively constructed ansatze. The benchmarking evaluates accuracy, expressibility, and scalability of these ansatze within the QC-AFQMC framework. We test these ansatze on linear hydrogen chains under bond stretching and find that several ansatz families produce chemically accurate ph-AFQMC energies across the dissociation curve. We have performed simulations using the CUDA-Q quantum development platform on the GPU partition of the Perlmutter supercomputer. When comparing ansatze at similar numbers of variational parameters, we find that different ansatz families yield comparable ph-AFQMC results despite exhibiting substantially different variational energies, optimization costs, and circuit depths. Our results indicate that the variational energy of an ansatz is not always a reliable indicator of its quality for ph-AFQMC and reveal instances of over-parameterization. In the strongly correlated regime, trial wavefunctions obtained from adaptive ansatze, exemplified here by ADAPT-VQE with the UCCSD operator pool, can outperform their fixed-ansatz counterparts (UCCSD) in terms of projected energies while using substantially more compact circuits, providing a flexible route to optimize quantum resources within the ph-AFQMC framework.

Cryogenic fluid (CF) tanks in zero gravity are frequently pressurized with a non-condensable (NC) gas to enable rapid extraction. However, this pressurization increases the boil-off of the CF. It has been hypothesized that NC gas accumulates in the nonequilibrium liquid-vapor interface, acting as a kinetic barrier to condensation. Due to the nanoscale thickness and transient nature of this layer, experimental characterization is challenging. In this work, we use molecular dynamics simulations to study the phase change kinetics of evaporation and condensation in model CFs (liquid nitrogen and oxygen) in the presence of varying amounts of NC gas (neon). Our simulations demonstrate a significant accumulation of NC gas at the liquid-vapor interface, resulting in an interface density more than twice its bulk value. Furthermore, our results indicate that the reduction in CF vapor flux due to NC gas is approximated by the Schrage relation at low concentrations that are typical in CF tanks. We also show that, with increasing concentration of the NC gas, CF vapor density and vapor pressure increases, which can be attributed to the significant attractive interactions between the CF and the NC gas. Our findings suggest that reducing the strength of interaction between the CF and NC gas could help mitigate this undesirable phase equilibrium shift.

We evaluate the quantum resource requirements for ATP/metaphosphate hydrolysis, one of the most important reactions in all of biology with implications for metabolism, cellular signaling, and cancer therapeutics. In particular, we consider three algorithms for solving the ground state energy estimation problem: the variational quantum eigensolver, quantum Krylov, and quantum phase estimation. By utilizing exact classical simulation, numerical estimation, and analytical bounds, we provide a current and future outlook for using quantum computers to solve impactful biochemical and biological problems. Our results show that variational methods, while being the most heuristic, still require substantially fewer overall resources on quantum hardware, and could feasibly address such problems on current or near-future devices. We include our complete dataset of biomolecular Hamiltonians and code as benchmarks to improve upon with future techniques.

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for variational quantum eigensolver or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical central processing unit/graphical processing unit) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, which is passed as input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.

The ADAPT-VQE algorithm is a promising method for generating a compact ansatz based on derivatives of the underlying cost function, and it yields accurate predictions of electronic energies for molecules. In this work, we report the implementation and performance of ADAPT-VQE with our recently developed sparse wave function circuit solver (SWCS) in terms of accuracy and efficiency for molecular systems with up to 52 spin orbitals. The SWCS can be tuned to balance computational cost and accuracy, which extends the application of ADAPT-VQE for molecular electronic structure calculations to larger basis sets and a larger number of qubits. Using this tunable feature of the SWCS, we propose an alternative optimization procedure for ADAPT-VQE to reduce the computational cost of the optimization. By preoptimizing a quantum simulation with a parametrized ansatz generated with ADAPT-VQE/SWCS, we aim to utilize the power of classical high-performance computing in order to minimize the work required on noisy intermediate-scale quantum hardware, which offers a promising path toward demonstrating quantum advantage for chemical applications.

The standard paradigm for state preparation on quantum computers for the simulation of physical systems in the near term has been widely explored with different algorithmic methods. One such approach is the optimization of parameterized circuits, but this becomes increasingly challenging with circuit size. As a consequence, the utility of large-scale circuit optimization is relatively unknown. In this work we demonstrate that purely classical resources can be used to optimize quantum circuits in an approximate but robust manner such that we can bridge the resources that we have from high performance computing and see a direct transition to quantum advantage. We show this through the sparse wave function circuit solvers, which we detail here, and demonstrate a region of efficient classic simulation. With such tools, we can avoid the many problems that plague circuit optimization for circuits with hundreds of qubits using only practical and reasonable classical computing resources. These tools allow us to probe the true benefit of variational optimization approaches on quantum computers, thus opening the window to what can be expected with near term hardware for physical systems. We demonstrate this with a unitary coupled cluster ansatz on various molecules up to 64 qubits with tens of thousands of variational parameters.

Recent research has shown that wavefunction evolution in real and imaginary time can generate quantum subspaces with significant utility for obtaining accurate ground state energies. Inspired by these methods, we propose combining quantum subspace techniques with the variational quantum eigensolver (VQE). In our approach, the parameterized quantum circuit is divided into a series of smaller subcircuits. The sequential application of these subcircuits to an initial state generates a set of wavefunctions that we use as a quantum subspace to obtain high-accuracy groundstate energies. We call this technique the circuit subspace variational quantum eigensolver (CSVQE) algorithm. By benchmarking CSVQE on a range of quantum chemistry problems, we show that it can achieve significant error reduction in the best case compared to conventional VQE, particularly for poorly optimized circuits, greatly improving convergence rates. Furthermore, we demonstrate that when applied to circuits trapped at local minima, CSVQE can produce energies close to the global minimum of the energy landscape, making it a potentially powerful tool for diagnosing local minima.

The unitary coupled cluster (UCC) ansatz is a promising tool for achieving high-precision results using the variational quantum eigensolver (VQE) algorithm in the NISQ era. However, results on quantum hardware are thus far very limited and simulations have only accessed small system sizes. We advance the state of the art of UCC simulations by utilizing an efficient sparse wavefunction circuit solver and studying systems up to 64 qubits. Here we report results obtained using this solver that demonstrate the power of the UCC ansatz and address pressing questions about optimal initial parameterizations and circuit construction, among others. Our approach enables meaningful benchmarking of the UCC ansatz, a crucial step in assessing the utility of VQE for achieving quantum advantage.

The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of catalysis and biophysics. However, noisy hardware, the costs of computing gradients, and the number of qubits required to simulate large systems present major challenges to realizing the potential of dynamical simulations using quantum hardware. Here, we demonstrate that some of these issues can be mitigated by recent advances in machine learning. By combining transfer learning with techniques for building machine-learned potential energy surfaces, we propose a new path forward for molecular dynamics simulations on quantum hardware. We use transfer learning to reduce the number of energy evaluations that use quantum hardware by first training models on larger, less accurate classical datasets and then refining them on smaller, more accurate quantum datasets. We demonstrate this approach by training machine learning models to predict a molecule's potential energy using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamics simulations that cannot be readily obtained directly from quantum hardware. To reduce the quantum resources needed, the model is initially trained with data derived from low-cost techniques, such as Density Functional Theory, and subsequently refined with a smaller dataset obtained from the optimization of the Unitary Coupled Cluster ansatz. We show that this approach significantly reduces the size of the quantum training dataset while capturing the high accuracies needed for quantum chemistry simulations.

Potential decomposition mechanisms are investigated using density functional theory (DFT) for a set of amide and urea solvents. Reaction energies and barriers are reported for proton and hydrogen abstraction, and preferred abstraction sites are identified. The N-H bond of secondary amides and the a-hydrogen atoms are more susceptible to proton abstraction than other sites in the solvent molecules. Additionally, hydrogen abstraction is more favorable at the N-alkyl substituents as well as a-hydrogen atoms that result in the formation of secondary and tertiary radicals. All proton abstraction energies are sensitive to the presence of a coordinating Li+, but the hydrogen abstraction energies do not depend on the presence of a Li+. The stabilization due to the presence of Li+ is most pronounced for sites near the carbonyl group where the Li+ interacts with the lone pair of electrons formed by proton abstraction and the carbonyl O atom. The initial steps of a Baeyer-Villiger oxidation type mechanism are also examined. Barriers for these initial steps are significantly lower if HO2- is the oxidant compared to LiO2-. A comparison to our previously reported experimental results indicates that no single reaction could be identified as the rate-limiting step that would predict the performance of this set of solvents in Li-O-2 batteries.

Nonaqueous Li-O2 batteries have the potential to aid in the electrification of our society due to their relatively high theoretical energy density. Unfortunately, the technology suffers from large degrees of irreversibility due to the aggressive chemical environment associated with the oxidation of the discharge product lithium peroxide. Herein, we present a study of a range of linear and cyclic amides and ureas as aprotic electrolyte solvents for the Li-O2 battery, some of which show slight increases in reversibility relative to the well-established pseudo-stable glymes, although we find that none provide reversibility necessary to enable a rechargeable system. Using quantitative differential electrochemical mass spectrometry, acid titrations, and isotopic labeling of O2 and carbon in the positive electrode, we provide insight into the degradation pathways for these solvents. In the companion article, we compare our experimental results presented here to solvent decomposition pathways including a Baeyer-Villiger oxidation mechanism.

The material behavior of the API-60 epoxy resin was investigated using atomistic molecular dynamics simulations. In this study, a hybrid, reactive force field (M-GAFF2), which combined Morse and harmonic bond potentials from GAFF2, was developed to understand the elastic/plastic response of the cured epoxy polymer. We found that elastic deformation was caused by epoxy network rearrangement at low strain while plastic deformation was observed by stretching and breaking covalent bonds after the yield point. Ab initio calculations at the CASPT2(2,2)/6-311+G* level of theory were performed to compute parameters for M-GAFF2. These are necessary for breaking covalent bonds in the epoxy network backbone when they are deformed. The M-GAFF2 was effective in revealing the ductile-like deformation behavior of the crosslinked epoxy polymer at the microscopic level, including elastic/plastic deformations and progressive failure. The effect of testing temperature at 300 and 400 K and degree of cure (DoC) on tensile properties such as Young's modulus, ultimate strength, and failure strain was evaluated to verify the feasibility of the M-GAFF2 in different thermodynamic constraints. This new approach is easy to use for nonequilibrium MD simulations and computationally efficient for studying the microscopic deformation and failure behavior of thermosetting polymers at the atomistic scale.

We employ density functional theory (DFT) to examine reaction mechanisms involving singlet oxygen 1Δg (1O2) and 1,2-dimethoxyethane (DME) to probe potential parasitic reactions occurring in Li-O2 batteries. First, we investigate the attack of 1O2 on the ethylene group (-CH2-CH2-) to form H2O2 and a C-C double bond in a single step. Second, we look at hydroperoxide formation that occurs via a two-step mechanism. We employ an implicit solvent model, Li+ coordination, and external electric fields to model the complex electrolyte environment near the cathode of a Li-O2 battery. The initial barriers for these reactions are decreasing functions of the dielectric constant of the implicit solvent model as well as the strength of the electric field. These initial barriers range between 17 and 26 kcal mol-1 for large dielectric constants and in the presence of electric fields. We discuss the implications of these results on ether-based electrolytes for Li-O2 batteries.

This study found that a scaled-down defect epoxy model with combined force fields with GAFF2 and Morse bond potential captured nonlinear brittle epoxy behavior including failure events by allowing covalent bond-breaking under deformation. We performed uniaxial tensile and shear testing, and epoxy brittle behavior became apparent by increasing amounts of defects. This computational methodology provides more physically realistic thermosetting models that can facilitate better description of epoxy behavior, especially brittle fracture, and improved quantitative prediction of mechanical properties. The computational efficiency is greater for large deformation simulations compared to the ReaxFF force field, which generally requires a lower time step of similar to 0.1 fs.

Recent experimental and computational evidence indicates that singlet oxygen (1O2) attacks the ethylene group (-CH2-CH2-) in ethylene carbonate (EC) leading to degradation in Li-ion batteries employing EC as the electrolyte solvent [J. Phys. Chem. A 2018, 122, 8828-8839]. Here, we employ computational quantum chemistry to explore this mechanism in detail for a large set of organic molecules. Benchmark calculations comparing density functional theory to the complete active space second-order perturbation theory and internally contracted multireference configuration interaction indicate that the M11 functional adequately captures trends in the transition-state energies for this mechanism. Based on our results, we recommend that solvents which include the ethylene group should be avoided in Li-ion and Li-O2 batteries where 1O2 is generated unless neighboring functional groups raise the reaction barrier to avoid this decomposition pathway.

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

In the lithium-O-2 battery, redox mediators lower the charging overpotential while facilitating the oxidation of the discharge product (lithium peroxide, Li2O2) to molecular oxygen. Previous studies have shown that compounds such as 9,10-dimethylphenazine and 10-ethylphenothiazine are effective as redox mediators. Herein, we investigate the radical cation chemistry of thianthrene toward Li2O2 and lithium oxide (Li2O). Given the high oxidation potential of thianthrene (4.15 V vs. Li-0/Li+) several electron-rich analogs (including 2,3,6,7-tetramethox-yselenanthrene and a phenoxytellurine) were synthesized to decrease the oxidation potential toward the oxidation potential of Li2O2. Control experiments showed, in the absence of any candidate molecules, similar to 15% of the electrons were diverted to parasitic chemistries (2.3 electrons per oxygen). Surprisingly, experiments with several of the candidate molecules demonstrated that the charging of a battery (with a LiFePO4 anode) yields oxygen in a 2-electron process for most of the charging process. These 2-electron yields occurred at potentials where the thianthrene analogs could not be oxidized and thus act as redox mediators for Li2O2 oxidation. Under conditions where the mediator is oxidized to the radical cation, both the charging overpotentials and yields of oxygen were diminished. These data suggest that the thianthrene scaffold serves as a poor candidate for redox mediation but acts in a beneficial manner to suppress parasitic chemistries (e.g., parasitic reactions involving the likely formation of singlet oxygen).

We present a heterogeneous CPU+GPU algorithm for the direct variational optimization of the two-electron reduced-density matrix (2RDM) under two-particle N-representability conditions. This variational 2RDM (v2RDM) approach is the driver for a polynomially-scaling approximation to configuration-interaction-driven complete active space self-consistent field (CASSCF) theory. For v2RDM-based CASSCF com- putations involving an active space consisting of 50 electrons in 50 orbitals [denoted (50e,50o)], we observe a speedup of a factor of 3.7 when the code is executed on a combination of an NVIDIA TITAN V GPU and an Intel Core i7-6850k CPU, relative to the case when the code is executed on the CPU alone. We use this GPU-accelerated v2RDM-CASSCF algorithm to explore the electronic structure of the 3,k-circumacene and 3,k-periacene series (k=2–7) and compare indicators of polyradical character in the lowest-energy singlet states to those observed for oligoacene molecules. The singlet states in larger circumacene and periacene molecules display the same polyradical characteristics observed in oligoacenes, with the onset of this behavior occuring at smallest k for periacenes, followed by the circumacenes and then the oligoacenes. However, the unpaired electron density that accumulates along the zig-zag edge of the circumacenes is slightly less than that which accumulates in the oligoacenes, while periacenes clearly exhibit the greatest build-up of unpaired electron density in this region.

Analytic energy gradients are presented for a variational two-electron reduced-density-matrix-driven complete active space self-consistent field (v2RDM-CASSCF) procedure that employs the density-fitting (DF) approximation to the two-electron repulsion integrals. The DF approximation significantly reduces the computational cost of v2RDM-CASSCF gradient evaluation, in terms of both the number of floating-point operations and memory requirements, enabling geometry optimizations on much larger chemical systems than could previously be considered at the this level of theory [E. Maradzike et al., J. Chem. Theory Comput., 2017, 13, 4113-4122]. The efficacy of v2RDM-CASSCF for computing equilibrium geometries and harmonic vibrational frequencies is assessed using a set of 25 small closed- and open-shell molecules. Equilibrium bond lengths from v2RDM-CASSCF differ from those obtained from configuration-interaction-driven CASSCF (CI-CASSCF) by 0.62 pm and 0.05 pm, depending on whether the optimal reduced-density matrices from v2RDM-CASSCF satisfy two-particle N-representability conditions (PQG) or PQG plus partial three-particle conditions (PQG+T2), respectively. Harmonic vibrational frequencies, which are obtained by finite differences of v2RDM-CASSCF analytic energy gradients, similarly demonstrate that quantitative agreement between v2RDM- and CI-CASSCF requires the consideration of partial three-particle N-representability conditions. Lastly, optimized geometries are obtained for the lowest-energy singlet and triplet states of the linear polyacene series up to dodecacene (C50H28), in which case the active space is comprised of 50 electrons in 50 orbitals. The v2RDM-CASSCF singlet-triplet energy gap extrapolated to an infinitely-long linear acene molecule is found to be 7.8 kcal/mol

Vinyl alcohol and acetaldehyde are isoelectronic products of incomplete butanol combustion. Along with the radicals resulting from the removal of atomic hydrogen or the hydroxyl radical, these species are studied here using ab initio methods as complete as coupled cluster theory with single, double, triple, and perturbative quadruple excitations [CCSDT(Q)], with basis sets as large as cc-pV5Z. The relative energies provided herein are further refined by including corrections for relativistic effects, the frozen core approximation, and the Born-Oppenheimer approximation. The effects of anharmonic zero-point vibrational energies are also treated. The syn conformer of vinyl alcohol is predicted to be lower in energy than the anti conformer by 1.1 kcal mol-1. The alcoholic hydrogen of syn-vinyl alcohol is found to be the easiest to remove, requiring 84.4 kcal mol-1. Five other radicals are also carefully considered, with four conformers investigated for the 1-hydroxyvinyl radical. Beyond energetics, we have conducted an overhaul of the spectroscopic literature for these species. Our results also provide predictions for fundamental modes yet to be reported experimentally. To our knowledge, the ν3 (3076 cm-1) and ν4 (2999 cm-1) C-H stretches for syn-vinyl alcohol and all but one of the vibrational modes for anti-vinyl alcohol (ν1-ν14) are yet to be observed experimentally. For the acetyl radical, ν6 (1035 cm-1), ν11 (944 cm-1), ν12 (97 cm-1), and accounting for our changes to the assignment of the 1419.9 cm-1 experimental mode, ν10 (1441 cm-1), are yet to be observed. We have predicted these unobserved fundamentals and reassigned the experimental 1419.9 cm-1 frequency in the acetyl radical to ν4 rather than to ν10. Our work also strongly supports reassignment of the ν10 and ν11 fundamentals of the vinoxy radical. We suggest that the bands assigned to the overtones of these fundamentals were in fact combination bands. Our findings may be useful in constructing improved combustion models of butanol and in spectroscopically characterizing these molecules further.