Quantum computing offers a compelling route to ab initio simulations of chemical reactions, while noisy intermediate-scale quantum (NISQ) hardware demands resource-aware protocols. In this study, we introduce an efficient workflow using a noise-resilient hardware-adaptable ansatz (HAA) in an active space combined with driven similarity renormalization group (DSRG) downfolding. The active space is obtained via a new selection algorithm, many-body-expanded correlation-energy active space (MBECAS), based on orbital ranking by first-/second-order correlation-energy increments. We validate the MBECAS-DSRG-HAA workflow on reactions with up to tens of atoms, reproducing Diels-Alder barriers within a few millihartrees after error mitigation. By combining an efficient active space selector, effective-Hamiltonian downfolding, and a low-depth variational circuit, our approach delivers highly accurate reaction energetics with modest quantum resources and motivates extensions to larger systems and excited-state dynamics.
Quantum computing offers a compelling route to ab initio simulations of chemical reactions, while noisy intermediate-scale quantum (NISQ) hardware demands resource-aware protocols. In this study, we introduce an efficient workflow using a noise-resilient hardware-adaptable ansatz (HAA) in an active space combined with driven similarity renormalization group (DSRG) downfolding. The active space is obtained via a new selection algorithm, many-body-expanded correlation-energy active space (MBECAS), based on orbital ranking by first-/second-order correlation-energy increments. We validate the MBECAS-DSRG-HAA workflow on reactions with up to tens of atoms, reproducing Diels-Alder barriers within a few millihartrees after error mitigation. By combining an efficient active space selector, effective-Hamiltonian downfolding, and a low-depth variational circuit, our approach delivers highly accurate reaction energetics with modest quantum resources and motivates extensions to larger systems and excited-state dynamics.
Applications of quantum simulation algorithms to obtain electronic energies of molecules on noisy intermediate-scale quantum (NISQ) devices require careful consideration of resources describing the complex electron correlation effects. In modeling second-quantized problems, the biggest challenge confronted is that the number of qubits scales linearly with the size of the molecular basis. This poses a significant limitation on the size of the basis sets and the number of correlated electrons included in quantum simulations of chemical processes. To address this issue and enable more realistic simulations on NISQ computers, we employ the double unitary coupled-cluster (DUCC) method to effectively downfold correlation effects into the reduced-size orbital space, commonly referred to as the active space. Using downfolding techniques, we demonstrate that properly constructed effective Hamiltonians can capture the effect of the whole orbital space in small-size active spaces. Combining the downfolding preprocessing technique with the variational quantum eigensolver, we solve for the ground-state energy of H 2, Li 2, and BeH 2 in the cc-pVTZ basis using the DUCC-reduced active spaces. We compare these results to full configuration-interaction and high-level coupled-cluster reference calculations.
Sample-based quantum diagonalization (SQD) offers a powerful route to accurate quantum chemistry on noisy intermediate-scale quantum (NISQ) devices by combining quantum sampling with classical diagonalization. Here we introduce HSQD, a novel half-qubit SQD approach that halves the qubit requirement for simulating a chemical system and drastically reduces overall circuit depth and gate counts, suppressing hardware noise. When modeling the dissociation of the nitrogen molecule with a (10e, 26o) active space, HSQD matches the accuracy of SQD on IBM quantum hardware using only half the number of qubits and 40% fewer measurements. We further enhance HSQD with a heat-bath configuration interaction (HCI) inspired selection of the samples, forming HCI-HSQD. This yields sub-millihartree accuracy across the N2 potential energy surface and produces subspaces up to 39% smaller than those from classical HCI, showing a significant improvement in the compactness of the ground-state representation. Finally, we demonstrate the scalability of HCI-HSQD using iron-sulfur clusters, reaching active spaces of up to (54e, 36o) while using only half as many qubits as the original SQD. For these systems, HCI-HSQD reduces SQD energy errors by up to 76% for [2Fe-2S] and 26% for [4Fe-4S], while also reducing subspace sizes, halving measurement requirements, and eliminating expensive post-processing. Together, these results establish half-qubit SQD as a noise-resilient and resource-efficient pathway toward practical quantum advantage in strongly correlated chemistry.
The practical application of quantum technologies to chemical problems faces significant challenges, particularly in the treatment of realistic basis sets and the accurate inclusion of electron correlation effects. A direct approach to these problems is currently infeasible due to limitations in the number of logical qubits, their fidelity, and the shallow circuit depths supported by existing hardware, all of which hinder simulations at the required level of accuracy. A promising alternative is hybrid quantum-classical computing, where classical resources are used to construct effective Hamiltonians characterized by dimensions that conform to the constraints of current quantum devices. In this paper, we demonstrate the performance of a hybrid approach: coupled cluster downfolded Hamiltonians are first evaluated in reduced-dimensionality active spaces, and the corresponding ground-state energies are subsequently computed using quantum algorithms. Our comprehensive analysis explores the achievable accuracy in recovering correlation energies when hundreds of orbitals are downfolded into a problem size tractable by today’s quantum hardware. We argue that such flexible hybrid algorithms, where problem size can be tailored to available quantum resources, can serve as a bridge between noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, marking a step toward the early realization of quantum advantage in chemistry.
Quantum-chemical simulations are essential for predicting energies of chemical reactions. Accurately solving the many-body Schrödinger equation for reagent and product states of most relevant chemical processes is, however, unfeasible. Quantum computing offers a pathway for predicting energies of correlated electronic systems with localized interactions. Here, we apply a quantum embedding approach for investigating oxygen reduction reactions at the electrode surface in Lithium batteries, a representative example of energetic analysis in localized chemical reactions. We employ an active space selection method based on density difference analysis for identifying the orbitals involved in the reaction. Leveraging the Local Unitary Cluster Jastrow ansatz for state preparation, the active-space orbitals are then processed on a quantum computer. As quantum algorithms, we use Sample-based Quantum Diagonalization, SQD, and its extended version, Ext-SQD, which integrates electronic excitations into the quantum-selected electronic configuration subspace. The largest configurations are represented by quantum circuits mapped onto 80 qubits of an IBM Heron R2 quantum processing unit. For up to 12 orbitals, we are able to benchmark the quantum-computed reaction energies against results obtained with Complete Active Space Configuration Interaction. For benchmarking results in active spaces as large as 32 orbitals, we resort to Heat-Bath Configuration Interaction and Coupled Cluster Singles and Doubles calculations, respectively. At 27 orbitals, the Ext-SQD results exhibit prediction accuracy improvements with regard to the standard, quantum-chemical reference methods that remain computationally feasible at that scale. The results indicate the potential of sample-based quantum diagonalization for performing high-accuracy reaction modeling in chemistry and materials science.
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.
Recent advances in quantum information and quantum science have inspired the development of various compact, dynamically structured ansätze that are expected to be realizable in Noisy Intermediate-Scale Quantum (NISQ) devices. However, such ansätze construction strategies hitherto developed involve considerable measurements, and thus, they deviate significantly in the NISQ platform from their ideal structures. Therefore, it is imperative that the usage of quantum resources be minimized while retaining the expressivity and dynamical structure of the ansatz that can adapt itself depending on the degree of correlation. We propose a novel ansatz construction strategy based on the ab initio many-body perturbation theory that requires no pre-circuit measurement and, thus, remains structurally unaffected by any hardware noise. The accuracy and quantum complexity associated with the ansatz are solely dictated by a pre-defined perturbative order, as desired, and, hence, are tunable. Furthermore, the underlying perturbative structure of the ansatz construction pipeline enables us to decompose any high-rank excitation that appears in higher perturbative orders into the product of various low-rank operators, and it thus keeps the execution gate-depth to its minimum. With a number of challenging applications on strongly correlated systems, we demonstrate that our ansatz performs significantly better, both in terms of accuracy, parameter count, and circuit depth, in comparison to the allied unitary coupled cluster based ansätze.