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Niranjan Govind

Publications and source records attributed to Niranjan Govind.

At least 19 recordsLinked to original sources

How Accurately Can We Describe Spin Crossover?

The complicated physicochemical properties of metal complexes that exhibit thermal spin crossover make it difficult for routine electronic structure calculations to yield an accurate transition temperature prediction, $T_{1/2}$. The difficulty lies in the intricate connection between the spin-crossover energy, which is a molecular spectroscopic property, and $T_{1/2}$, a condensed phase property. Here we show how to obtain spin-crossover energies systematically by reverse engineering of experimental $T_{1/2}$ data. The protocol is based upon fitting the range separation parameter, $ω$, in the hybrid LC-$ω$PBE density functional to reproduce the experimental $T_{1/2}$ values for a series of metal complexes. We provide insights into the sources of variations of at least $\pm 15$ kJ mol$^{-1}$ found from common exchange and correlation functionals by comparing their performance against our reference data. By analysis of the sensitivity of transition temperatures to $\pm 1$ \% shifts in the range separation parameter, we determined a typical uncertainty of $\pm 50$ K for them, and a $\pm 2$ kJ mol$^{-1}$ uncertainty in the extracted spin-crossover energies due to $\pm 1$ \% variations of $T_{1/2}$. Lastly, we present results from the high-level, all-electron coupled cluster method for eight of the smaller molecules in the reference data set, and discuss the influence of the truncation of the excitation series upon the spin state energies.

cond-mat.mtrl-sci

Compile-once operation graphs for reusable continuous unitary transformations

Continuous unitary transformations repeatedly evaluate the same operator algebra as the coefficients of a Hamiltonian and its observables evolve. We compile these algebraic relationships once into a numerical operation graph that can be saved and reused for new coefficient sets, additional operators, derivatives, and forward and reverse calculations on CPUs and GPUs. Reuse is demonstrated by applying the same compiled graph to hydrogen and deuterium parameterizations of a reduced molecular model without regenerating the operator equations. The main computational challenge is construction: generated many-body algebra can contain hundreds of millions of contributions before numerical propagation begins. We address this by determining the required storage in advance and writing contributions directly into the final disk-backed representation. In a deliberately large diagnostic-enabled stress test containing more than 350 million generated contributions, peak host memory remains only about 7\% above the final stored size. Cross-checks show that CPU and GPU implementations, forward and reverse operations, and repeated loading of the compiled graph reproduce the tested operations to numerical precision. This compile-once representation separates expensive operator-algebra generation from the numerical transformations that reuse it.

physics.chem-ph

A Machine-Learning Framework for Efficient Ring-Polymer Instanton Rate Calculations

We develop an efficient machine-learning framework for ring-polymer instanton rate calculations that combines Gaussian process regression (GPR)-enhanced line integral string optimization with scalable surrogate modeling of the fluctuation prefactor. By exploiting uncertainty estimates from the surrogate modeling, we show that the number of force evaluations required to converge an instanton path becomes effectively independent of the number of beads used to discretize the pathway. To improve the efficiency of GPR model training, we introduce a strategy combining a physics-informed kernel prior, Hessian-free hyperparameter optimization, and GPU-accelerated Blackbox Matrix-Matrix Multiplication (BBMM), reducing model-training costs by more than an order of magnitude. For rate calculations, we develop adaptive regression, selective Hessian training, and cubic-spline interpolation strategies that substantially reduce the number of explicit Hessian evaluations while maintaining accurate tunneling rates. We apply and compare both cubic spline interpolation and GPR methods to approximate the instanton rate for representative proton transfer systems such as malonaldehyde, Z-3-aminopropenal, and 7,9-dinitro-10-hydroxybenzo[h]quinoline (dinitro-HBQ). Both approaches perform well for the smaller systems, whereas the dinitro-HBQ results expose limitations of the GPR model and demonstrate the greater robustness of the cubic spline interpolation method. These developments provide a practical workflow for reducing the computational cost of instanton rate calculations in complex molecular systems.

physics.chem-ph

Fermionic mean-field dynamics for spin systems beyond free fermions

We introduce the fermionized time-dependent Hartree-Fock (fTDHF), a real-time quantum dynamics method for spin-1/2 Hamiltonians following their mapping to fermions via the Jordan-Wigner transformation. fTDHF is formally equivalent to exact dynamics in the case of free fermions and can efficiently handle non-local string operators arising from long-range interactions via transition matrix elements between non-orthogonal Slater determinants. We show that the fTDHF method can be implemented on a classical computer with a cost that scales polynomially with system size, and linearly with the time steps. We benchmark fTDHF against exact dynamics on three separate spin-1/2 models, representing adiabatic preparation of states with long-range correlations, disorder-driven observation of many-body localization, and particle production in the Schwinger model. For each of these systems, fTDHF is shown to reproduce the qualitative dynamics generated by the exact evolutions, while maintaining a simple physical picture due to its mean-field nature.

cond-mat.str-el

Tracking Electron, Proton, and Solvent Motion in Proton-Coupled Electron Transfer with Ultrafast X-rays

Proton-coupled electron transfer (PCET) is foundational to catalysis, bioenergetics, and energy conversion, yet capturing and disentangling the coupled motions of electrons, protons, and solvent has remained a major experimental challenge. We combine femtosecond optical spectroscopy, site-specific ultrafast soft X-ray absorption spectroscopy, and time-resolved X-ray scattering with advanced calculations to disentangle the elementary steps of PCET in solution. Using a ruthenium polypyridyl model complex, we directly resolve photoinduced electron redistribution, ligand-site protonation within 100 ps, and the accompanying solvent reorganization. This unified multi-modal approach provides an orbital-level, atomistic picture of PCET, showing how electronic, nuclear, and solvation degrees of freedom can be separated experimentally. Our results establish a general X-ray framework for understanding and ultimately controlling PCET in catalysis, artificial photosynthesis, and biological energy flow.

physics.chem-ph

A Perspective on Quantum Computing Applications in Quantum Chemistry using 25--100 Logical Qubits

The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25--100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, for instance, multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.

quant-ph

Tracing long-lived atomic coherences generated via molecular conical intersections

Accessing coherences is key to fully understand and control ultrafast dynamics of complex quantum systems like molecules. Most photochemical processes are mediated by conical intersections (CIs), which generate coherences between electronic states in molecules. We show with accurate calculations performed on gas-phase methyl iodide that CI-induced electronic coherences of spin-orbit-split states persist in atomic iodine after dissociation. Our simulation predicts a maximum magnitude of vibronic coherence in the molecular regime of 0.75% of the initially photoexcited state population. Upon dissociation, one third of this coherence magnitude is transferred to a long-lived atomic coherence where vibrational decoherence can no longer occur. To trace these dynamics, we propose a table-top experimental approach--heterodyned attosecond four-wave-mixing spectroscopy (Hd-FWM). This technique can temporally resolve small electronic coherence magnitudes and reconstruct the full complex coherence function via phase cycling. Hence, Hd-FWM leads the way to a complete understanding and optimal control of spin-orbit-coupled electronic states in photochemistry.

physics.chem-ph

Fast simulation of soft x-ray near-edge spectra using a relativistic state-interaction approach: Application to closed-shell transition metal complexes

Spectroscopic techniques based on core-level excitations provide powerful tools for probing molecular and electronic structures with high spatial resolution. However, accurately calculating spectral features at the L or M edges is challenging due to the significant influence of spin-orbit and multiplet effects. While scalar-relativistic effects can be incorporated at minimal computational cost, accounting for spin-orbit interactions requires more complex computational frameworks. In this work, we develop and apply the state-interaction approach, incorporating relativistic effects using the ZORA-Kohn-Sham Hamiltonian, to simulate near-edge soft X-ray absorption spectra for closed-shell transition metal complexes. The computed spin-orbit splittings closely match those obtained from more rigorous methods. This approach provides a practical and cost-effective alternative to more rigorous two-component methods, making it particularly valuable for large-scale calculations and applications such as resonant inelastic X-ray scattering simulations, where capturing a large number of excited states is essential.

physics.chem-ph

Quantum time dynamics mediated by the Yang-Baxter equation and artificial neural networks

Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANN) and the Yang-Baxter equation (YBE). Unlike traditional error mitigation methods, which are computationally intensive, we investigate artificial error mitigation. We developed a novel method that combines ANN for noise mitigation combined with the YBE to generate noisy data. This approach effectively reduces noise in quantum simulations, enhancing the accuracy of the results. The YBE rigorously preserves quantum correlations and symmetries in spin chain simulations in certain classes of integrable lattice models, enabling effective compression of quantum circuits while retaining linear scalability with the number of qubits. This compression facilitates both full and partial implementations, allowing the generation of noisy quantum data on hardware alongside noiseless simulations using classical platforms. By introducing controlled noise through the YBE, we enhance the dataset for error mitigation. We train an ANN model on partial data from quantum simulations, demonstrating its effectiveness in mitigating errors in time-evolving quantum states, providing a scalable framework to enhance quantum computation fidelity, particularly in noisy intermediate-scale quantum (NISQ) systems. We demonstrate the efficacy of this approach by performing quantum time dynamics simulations using the Heisenberg XY Hamiltonian on real quantum devices.

quant-ph

Polaritonic Chemistry using the Density Matrix Renormalization Group Method

The emerging field of polaritonic chemistry explores the behavior of molecules under strong coupling with cavity modes. Despite recent developments in ab initio polaritonic methods for simulating polaritonic chemistry under electronic strong coupling, their capabilities are limited, especially in cases where the molecule also features strong electronic correlation. To bridge this gap, we have developed a novel method for cavity QED calculations utilizing the Density Matrix Renormalization Group (DMRG) algorithm in conjunction with the Pauli-Fierz Hamiltonian. Our approach is applied to investigate the effect of the cavity on the S0 -S1 transition of n-oligoacenes, with n ranging from 2 to 5, encompassing 22 fully correlated π orbitals in the largest pentacene molecule. Our findings indicate that the influence of the cavity intensifies with larger acenes. Additionally, we demonstrate that, unlike the full determinantal representation, DMRG efficiently optimizes and eliminates excess photonic degrees of freedom, resulting in an asymptotically constant computational cost as the photonic basis increases.

physics.chem-ph

$G_0W_0$ Ionization Potentials of First-Row Transition Metal Aqua Ions

We report computations of the vertical ionization potentials within the $GW$ approximation of the near-complete series of first-row transition metal (V-Cu) aqua ions in their most common oxidation states, i.e. V$^{3+}$, Cr$^{3+}$, Cr$^{2+}$, Mn$^{2+}$, Fe$^{3+}$, Fe$^{2+}$, Co$^{2+}$, Ni$^{2+}$, and Cu$^{2+}$. The $d$-orbital occupancy of these systems spans a broad range from $d^2$ to $d^9$. All the structures were first optimized at the density functional theory level using a large cluster of explicit water molecules that are embedded in a continuum solvation model. Vertical ionization potentials were computed with the one-shot $G_0W_0$ approach on a range of transition metal ion clusters (6, 18, 40, and 60 explicit water molecules) wherein the convergence with respect to the basis set size was evaluated using the systems with 40 water molecules. We assess the results using three different density functional approximations as starting points for the vertical ionization potential calculations, namely $G_0W_0$@PBE, $G_0W_0$@PBE0, and $G_0W_0$@r$^2$SCAN. While the predicted ground-state structures are similar with all three exchange-correlation functionals, the vertical ionization potentials were in closer agreement with the experiment when using the $G_0W_0$@PBE0 and $G_0W_0$@r$^2$SCAN approaches, with the r2SCAN based calculations being significantly less expensive. Computed bond distances and vertical ionization potentials for all structures were compared with available experimental data and are in good agreement.

physics.chem-ph

Hybrid algorithm for the time-dependent Hartree-Fock method using the Yang-Baxter equation on quantum computers

The time-dependent Hartree-Fock (TDHF) method is an approach to simulate the mean field dynamics of electrons within the assumption that the electrons move independently in their self-consistent average field and within the space of single Slater determinants. One of the major advantages of performing time dynamics within Hartree-Fock theory is the free fermionic nature of the problem, which makes TDHF classically simulatable in polynomial time. Here, we present a hybrid TDHF implementation for quantum computers. This quantum circuit grows with time; but with our recent work on circuit compression via the Yang-Baxter equation (YBE), the resulting circuit is constant depth. This study provides a new way to simulate TDHF with the aid of a quantum device as well as provides a new direction for the application of YBE symmetry in quantum chemistry simulations.

quant-ph

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

One of the commonly used chemical-inspired approaches in variational quantum computing is the unitary coupled-cluster (UCC) ansatze. Despite being a systematic way of approaching the exact limit, the number of parameters in the standard UCC ansatze 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 UCC ansatze with better scaling. In this paper we explore the parameter redundancy in the preparation of unitary coupled-cluster singles and doubles (UCCSD) ansatze 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.

quant-ph

QuYBE -- An Algebraic Compiler for Quantum Circuit Compression

QuYBE is an open-source algebraic compiler for the compression of quantum circuits. It has been applied for the efficient simulation of the Heisenberg Hamiltonian on quantum computers. Currently, it can simulate the time dynamics of one-dimensional chains. It includes modules to generate the quantum circuits for the above as well as produce the compressed circuits, which are independent of the time step. It utilizes the Yang-Baxter equation (YBE) to perform the compression. QuYBE enables users to seamlessly design, execute, and analyze the time dynamics of the Heisenberg Hamiltonian on quantum computers. QuYBE is the first step toward making the YBE technique available to a broader community of scientists from multiple domains. The QuYBE compiler is available at https://github.com/ZichangHe/QuYBE

quant-ph

On the basis set selection for molecular core-level $GW$ calculations

The $GW$ approximation has been recently gaining popularity among the method for simulating molecular core-level X-ray photoemission spectra. Traditionally, $GW$ core-level binding energies have been computed using either the cc-pV$n$Z or def2-$n$ZVP basis set families, extrapolating the obtained results to the complete basis set limit, followed by a an element-specific relativistic correction. Despite of achieving good accuracy, these binding energies are chronically underestimated. By using first-row elements and standard techniques known to offer good cost-accuracy ratio in other theories, we show that the cc-pV$n$Z and def2-$n$ZVP families show large contraction errors and lead to unreliable complete basis set extrapolations. On the other hand, we demonstrate that uncontracted versions of these basis sets offer vastly improved convergence. Even faster convergence can be obtained using core-rich, property-optimized, basis sets families like pcSseg-$n$, pcJ-$n$ and ccX-$n$Z. Finally, we also show that the improvement over the core properties does not degrade the calculation of the valence excitations, and thus offer a balanced description of both core and valence regions.

physics.chem-ph

Quantum time dynamics of 1D-Heisenberg models employing the Yang-Baxter equation for circuit compression

Quantum time dynamics (QTD) is considered a promising problem for quantum supremacy on near-term quantum computers. However, QTD quantum circuits grow with increasing time simulations. This study focuses on simulating the time dynamics of 1-D integrable spin chains with nearest neighbor interactions. We show how the quantum Yang-Baxter equation can be exploited to compress and produce a shallow quantum circuit. With this compression scheme, the depth of the quantum circuit becomes independent of step size and only depends on the number of spins. We show that the compressed circuit scales quadratically with system size, which allows for the simulations of time dynamics of very large 1-D spin chains. We derive the compressed circuit representations for different special cases of the Heisenberg Hamiltonian. We compare and demonstrate the effectiveness of this approach by performing simulations on quantum computers.

quant-ph

VQE Method: A Short Survey and Recent Developments

The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues and eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum algorithms such as quantum phase estimation because fully quantum algorithms require quantum hardware that will not be accessible in the near future. VQE has been successfully applied to solve the electronic Schrödinger equation for a variety of small molecules. However, the scalability of this method is limited by two factors: the complexity of the quantum circuits and the complexity of the classical optimization problem. Both of these factors are affected by choice of the variational ansatz used to represent the trial wave function. Hence, the construction of efficacious ansatz is an active area of research. Put another way, modern quantum computers are not capable of executing deep quantum circuits produced by using currently available ansatze for problems that map onto more than several qubits. In this review, we present recent developments in the field of designing effective ansatzes that fall into two categories -- chemistry inspired and hardware efficient -- that produce quantum circuits that are easier to run on modern hardware. We discuss the shortfalls of ansatzes originally formulated for VQE simulations, how they are addressed in more sophisticated methods, and the potential ways for further improvements.

quant-ph

Scalable Molecular GW Calculations: Valence and Core Spectra

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 are 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 one order of magnitude. A complex heuristic in the quasiparticle equation solver further allows a speed-up of the computation of core and semi-core ionization energies. Benchmark tests using the GW100 and CORE65 datasets and the carbon 1{\it s} 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.

physics.chem-ph