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.

We present a scalable implementation of the GW approximation using Gaussian atomic orbitals to study the valence and core ionization spectroscopies of molecules. The implementation of the standard spectral decomposition approach to the screened-Coulomb interaction, as well as a contour-deformation method, is described. We have implemented both of these approaches using the robust variational fitting approximation to the four-center electron repulsion integrals. We have utilized the MINRES solver with the contour-deformation approach to reduce the computational scaling by 1 order of magnitude. A complex heuristic in the quasiparticle equation solver further allows a speed-up of the computation of core and semicore ionization energies. Benchmark tests using the GW100 and CORE65 data sets and the carbon 1s binding energy of the well-studied ethyl trifluoroacetate, or ESCA molecule, were performed to validate the accuracy of our implementation. We also demonstrate and discuss the parallel performance and computational scaling of our implementation using a range of water clusters of increasing size.

Metastable electronic states decaying via autoionization or autodetachment are common gateway states for chemical processes initiated by electron-molecule interactions or photo-excitation and are ubiquitous in highly energetic environments. We present a robust theoretical approach for calculating positions and widths of electronic resonances. The method is based on the extended multiconfigurational quasidegenerate perturbation theory combined with complex absorbing potential technique (CAP-XMCQDPT2). The theory is capable of describing the resonance position and width for shape and Feshbach resonances with high accuracy and low computational cost. Importantly, the resonance parameters are extracted at a cost of a single electronic structure calculation. Resonances positions and widths computed for shape and Feshbach molecular resonances are in a good agreement with the experimental data and with the previous theoretical estimates.

Quinones are versatile biological electron acceptors and mobile electron carriers in redox processes. We present the first ab initio calculations of the width of the (2)A(u) shape resonance in the para-benzoquinone anion, the simplest member of the quinone family. This resonance state located at 2.5 eV above the ground state of the anion is believed to be a gateway state for electron attachment in redox processes involving quinones. We employ the equation-of-motion coupled-cluster method for electron affinity augmented by a complex-absorbing potential (CAP-EOM-EA-CCSD) to calculate the resonance position and width. The calculated width, 0.013 eV, is in excellent agreement with the width of the resonant peak in the photodetachment spectrum, thus supporting the assignment of the band to resonance excitation to the autodetaching (2)A(u) state. The methodological aspects of CAP-EOM-EA-CCSD calculations of resonances positions and widths in medium-sized molecules, such as basis set and CAP box size effects, are also discussed.

Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the Dynamic Sparse Reparameterization scheme which was originally proposed for training deep convolutional neural networks. We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k-UpCCGSD, as well as the Low-Depth Circuit Ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).