Simulation of chemical reactions to compute reaction energies using variational algorithms remains challenging in achieving chemical accuracy relative to benchmark computational chemistry methods due to limitations such as qubit number, circuit depth, and noise. To address this issue, we propose the definition of different active spaces for studying chemical reactions, incorporating irreducible representations of both ground and excited states by defining the maximum contribution of excitation terms in the ansatz, implemented here within a second-order Trotterized UCCSD-VQE framework. Our results demonstrate that this approach can achieve chemical accuracy for several representative reactions and yields close agreement with benchmark methods across a broader range of reactions. For all reactions studied, the difference in reaction energies between VQE and CCSD remains within 1 kcal/mol. At the same time, comparison with FCI shows chemical accuracy for several cases and close agreement for the remaining systems. Furthermore, our analysis simplifies the selection of active spaces and electrons for each reaction, reducing it to a single optimal combination that supports chemically accurate or near-chemical-accuracy predictions.
Simulation of chemical reactions to compute reaction energies using variational algorithms remains challenging in achieving chemical accuracy relative to benchmark computational chemistry methods due to limitations such as qubit number, circuit depth, and noise. To address this issue, we propose the definition of different active spaces for studying chemical reactions, incorporating irreducible representations of both ground and excited states by defining the maximum contribution of excitation terms in the ansatz, implemented here within a second-order Trotterized UCCSD-VQE framework. Our results demonstrate that this approach can achieve chemical accuracy for several representative reactions and yields close agreement with benchmark methods across a broader range of reactions. For all reactions studied, the difference in reaction energies between VQE and CCSD remains within 1 kcal/mol. At the same time, comparison with FCI shows chemical accuracy for several cases and close agreement for the remaining systems. Furthermore, our analysis simplifies the selection of active spaces and electrons for each reaction, reducing it to a single optimal combination that supports chemically accurate or near-chemical-accuracy predictions.
Pericyclic reactions provide stringent tests for quantum simulations because their mechanisms are governed by orbital symmetry and involve correlated transition states. In this work, we employ the variational quantum eigensolver (VQE) combined with a previously established symmetry-guided active-space selection protocol based on symmetry-matched fractions (SMF-VQE) to simulate Diel-Alder and Alder-ene reactions in complex systems involving extended pi-conjugation and multiple bonding. Although absolute electronic energies obtained from the current protocol exhibit significant deviations from the values computed using CCSD method, the symmetry-guided active spaces yield substantial cancellation of deviations in the energy differences. As a result, reaction energies are predicted with error (relative to CCSD) less than one kcal per mol, while activation energies are reproduced within about five to six kcal per mol. The symmetry-guided protocol also reduces the large combinatorial space of active-space choices to a single symmetry-consistent selection for each reaction.
Simulation of chemical reactions on quantum computing platforms using quantum classical hybrid algorithms such as the Variational Quantum Eigensolver (VQE) is challenged by the need for a reaction consistent treatment of electron correlation in reaction energy evaluations. In this work, we employ a previously reported symmetry guided active space selection protocol to compute ring strain energies of cyclic hydrocarbons using homodesmotic reaction schemes. The protocol enforces symmetry consistency across all reactants and products by selecting active spaces that yield identical symmetry matched fraction (SMF) values, thereby ensuring balanced correlation treatment at the reaction level. When multiple active spaces satisfy this criterion for a given molecule, larger active spaces often provide improved correlation treatment; however, smaller symmetry consistent active spaces can also yield comparable agreement due to favorable error cancellation within the homodesmotic framework. Using this framework, ring strain energies were evaluated for a series of saturated and unsaturated cyclic hydrocarbons, ranging from cyclopropane to the structurally complex adamantane. The resulting energies achieve chemical accuracy relative to density functional theory (DFT) and remain in close agreement with coupled cluster singles and doubles (CCSD) benchmarks. The systematic performance across increasing molecular complexity highlights the effectiveness of combining homodesmotic reaction design with symmetry-consistent VQE calculations. This approach, which enforces physically grounded consistency across reaction species, demonstrates clear potential for extending reaction based quantum simulations to larger molecular systems and broader classes of chemical reactions.
In recent years, the Variational Quantum Eigensolver (VQE) has emerged as one of the most popular algorithms for solving the electronic structure problem on near-term quantum computers. The utility of VQE is often hindered by the limitations of current quantum hardware, including, but not limited to, short qubit coherence times and low gate fidelities. These limitations become particularly pronounced when VQE is used along with deep quantum circuits, such as those required by the ”Unitary Coupled Cluster Singles and Doubles” (UCCSD) ansatz, often resulting in significant errors. To address these issues, we propose a low-depth ansatz based on parallelized Givens rotations, which can recover substantial correlation energy while drastically reducing circuit depth and two-qubit gate counts for an arbitrary active space (AS). Also, considering the current hardware architectures with low qubit counts, we introduce a systematic way to select molecular orbitals to define active spaces (ASs) that retain significant electron correlation. We validate our approach by computing bond dissociation profiles of water and strongly correlated systems, such as molecular nitrogen and oxygen, across various ASs. Noiseless simulations using the new ansatz yield ground-state energies comparable to those from the UCCSD ansatz while reducing circuit depth by 50–70%. Moreover, in noisy simulations, our approach achieves energy error rates an order of magnitude lower than that of UCCSD. Considering the efficiency and practical usage of our ansatz, we hope that it becomes a potential choice for performing quantum chemistry calculations on near-term quantum devices.
Quantum computers promise scalable treatments of electronic structure, yet applying variational quantum eigensolvers (VQE) on realistic drug-like molecules remains constrained by the performance limitations of near-term quantum hardwares. A key strategy for addressing this challenge which effectively leverages current Noisy Intermediate-Scale Quantum (NISQ) hardwares yet remains under-benchmarked is active space selection. We introduce a benchmark that heuristically proposes criteria based on chemically grounded metrics to classify the suitability of a molecule for using quantum computing and then quantifies the impact of active space choices across the VQE pipeline for quantum drug discovery. The suite covers several representative drug-like molecules (e.g., lovastatin, oseltamivir, morphine) and uses chemically motivated active spaces. Our VQE evaluations employ both simulation and quantum processing unit (QPU) execution using unitary coupled-cluster with singles and doubles (UCCSD) and hardware-efficient ansatz (HEA). We adopt a more comprehensive evaluation, including chemistry metrics and architecture-centric metrics. For accuracy, we compare them with classical quantum chemistry methods. This work establishes the first systematic benchmark for active space driven VQE and lays the groundwork for future hardware-algorithm co-design studies in quantum drug discovery.
One of the most important application areas of molecular quantum chemistry is the study and prediction of chemical reactivity. Large-scale, fully error-tolerant quantum computers could provide exact or near-exact solutions to the underlying electronic structure problem with exponentially less effort than a classical computer thus enabling highly accurate predictions for comparably large molecular systems. In the nearer future, however, only “noisy” devices with a limited number of qubits that are subject to decoherence will be available. For such near-term quantum computers the hybrid quantum-classical variational quantum eigensolver algorithm in combination with the unitary coupled-cluster ansatz (UCCSD-VQE) [Peruzzo et al. Nat. Commun. 2014, 5, 4213 and McClean et al. New J. Phys. 2016, 18, 023023] has become an intensively discussed approach that could provide accurate results before the dawn of error-tolerant quantum computing. In this work we present an implementation of UCCSD-VQE that allows for the first time to treat both open- and closed-shell molecules. We study the accuracy of the obtained energies for nine small molecular systems as well as for four exemplary chemical reactions by comparing to well-established electronic structure methods like (nonunitary) coupled-cluster and density functional theory. Finally, we roughly estimate the required quantum hardware resources to obtain “useful” results for practical purposes.
Diazirines and diazo compounds are widely employed as photoreactive precursors for generating carbenes, key intermediates in chemical biology and materials science. However, computationally modeling their reaction pathways remains challenging due to the need for large active spaces and the requirement to accurately capture excited-state surfaces along with transition states and conical intersections. In this work, we utilize a hybrid quantum-classical workflow for investigating carbene formation in representative diazirine-diazomethane systems. Our approach leverages Sample-based Quantum Diagonalization (SQD) and its extended variant, Extended Sample-based Quantum Diagonalization (Ext-SQD), for ground- and excited-state analysis, combined with classical tools for geometry optimization, active-space selection, and diagnostic evaluation. Quantum computations were carried out on superconducting quantum processors, and results for both aliphatic and aryl-substituted diazirine-diazomethane pairs were benchmarked against established classical methods, including density functional theory (DFT), coupled cluster with singles and doubles (CCSD), complete active space configuration interaction (CASCI), and selected configuration interaction (SCI). SQD achieves accuracy surpassing the chemical accuracy threshold for nearly all stationary points on the potential energy surface of parent diazirine relative to the CASCI(12,10) reference, and remains close to chemical accuracy for phenyl-substituted diazirine in a (30,30) active space, with an average deviation of 1.1 kcal/mol relative to the SCI benchmark. SQD closely follows CASCI and SCI trends, showing consistent agreement. The findings demonstrate the promise of quantum computing frameworks in modeling photochemical transformations of electronically complex and pharmacologically relevant molecules.