We developed a method to include solvent effects on electronic transition calculations using quantum computing, employing the subspace-search variational quantum eigensolver (SSVQE) algorithm and the average solvent electrostatic configuration (ASEC) model. The SSVQE algorithm is used to calculate the energy of ground and excited states of organic molecules, and the ASEC model is used to describe the electrostatic contribution of the solvent effect at room temperature. By comparing the results with complete active space configuration interaction calculations, we demonstrate that the solvent effect is properly accounted for in the calculation of each state energy value performed by the quantum algorithm. Furthermore, the gas-liquid shift of the electronic transitions is also correctly described, with values that present semiquantitative agreement with results obtained through more sophisticated treatment using classical computers and experimental values, demonstrating that the solvation model is robust even when paired with quantum algorithms. The ASEC-SSVQE method opens the possibility of using noisy intermediate-scale quantum devices to study the spectroscopy properties of solvated molecules, with the inclusion of thermodynamic effects.
We developed a method to include solvent effects on electronic transition calculations using quantum computing, employing the subspace-search variational quantum eigensolver (SSVQE) algorithm and the average solvent electrostatic configuration (ASEC) model. The SSVQE algorithm is used to calculate the energy of ground and excited states of organic molecules, and the ASEC model is used to describe the electrostatic contribution of the solvent effect at room temperature. By comparing the results with complete active space configuration interaction calculations, we demonstrate that the solvent effect is properly accounted for in the calculation of each state energy value performed by the quantum algorithm. Furthermore, the gas-liquid shift of the electronic transitions is also correctly described, with values that present semiquantitative agreement with results obtained through more sophisticated treatment using classical computers and experimental values, demonstrating that the solvation model is robust even when paired with quantum algorithms. The ASEC-SSVQE method opens the possibility of using noisy intermediate-scale quantum devices to study the spectroscopy properties of solvated molecules, with the inclusion of thermodynamic effects.
Quantum chemical calculations on quantum computers have been focused mostly on simulating molecules in the gas phase. Molecules in liquid solution are, however, most relevant for chemistry. Continuum solvation models represent a good compromise between computational affordability and accuracy in describing solvation effects within a quantum chemical description of solute molecules. In this work, we extend the variational quantum eigensolver to simulate solvated systems using the polarizable continuum model. To account for the state dependent solute-solvent interaction we generalize the variational quantum eigensolver algorithm to treat non-linear molecular Hamiltonians. We show that including solvation effects does not impact the algorithmic efficiency. Numerical results of noiseless simulations for molecular systems with up to 12 spin-orbitals (qubits) are presented. Furthermore, calculations performed on a simulated noisy quantum hardware (IBM Q, Mumbai) yield computed solvation free energies in fair agreement with the classical calculations.
We present a combination of three-dimensional reference interaction site model self-consistent field (3D-RISM-SCF) theory and the variational quantum eigensolver (VQE) to consider the solvent distribution effects within the framework of quantum-classical hybrid computing. The present method, 3D-RISM-VQE, does not include any statistical errors from the solvent configuration sampling owing to the analytical treatment of the statistical solvent distribution. We apply 3D-RISM-VQE to compute the spatial distribution functions of solvent water around a water molecule, the potential and Helmholtz energy curves of NaCl, and to analyze the Helmholtz energy component and related properties of H 2 O and NH 4 + . Moreover, we utilize 3D-RISM-VQE to analyze the extent to which solvent effects alter the efficiency of quantum calculations compared with calculations in the gas phase using the L 1 -norms of molecular electronic Hamiltonians. Our results demonstrate that the efficiency of quantum chemical calculations on a quantum computer in solution is virtually the same as that in the gas phase.
We present a hybrid quantum-classical framework for molecular simulations that combines the variational quantum eigensolver (VQE) with projection-based embedding (PBE) and an implicit solvation treatment. Solvent effects are incorporated through the solvation model based on density (SMD), enabling the investigation of chemically relevant processes in condensed phase environments within a quantum computing workflow. The embedding strategy partitions the molecular system into active and environment subsystems, allowing correlated quantum simulations of the chemically important subsystem while retaining an efficient classical description of the surroundings. As a representative application, we compute the potential energy surface of a bimolecular nucleophilic substitution (SN2) reaction between the chloride ion and chloromethane in multiple solvents, demonstrating that quantum eigensolver methods can be extended beyond gas-phase models to include solvent effects relevant for reaction energetics.
High Resolution Image Download MS PowerPoint Slide The recent developments of quantum computing present novel potential pathways for quantum chemistry as the scaling of the computational power of quantum computers could be harnessed to naturally encode and solve electronic structure problems. Theoretically exact quantum algorithms for chemistry have been proposed (e.g., quantum phase estimation), but the limited capabilities of current noisy intermediate-scale quantum devices motivated the development of less demanding hybrid algorithms. In this context, the variational quantum eigensolver (VQE) algorithm was successfully introduced as an effective method to compute the ground-state energies of small molecules. This study investigates the folded spectrum (FS) method as an extension of the VQE algorithm for the computation of molecular excited states. It provides the possibility of directly computing excited states around a selected target energy using the same or quantum circuit as for the ground-state calculation. Inspired by the variance-based methods from the quantum Monte Carlo literature, the FS method minimizes the energy variance, thus, in principle, requiring a computationally expensive squared Hamiltonian to be applied. We alleviate this potentially poor scaling by employing a Pauli grouping procedure to identify sets of commuting Pauli strings that can be evaluated simultaneously. This allows for a significant reduction in the computational cost. We applied the FS-VQE method to small molecules (H 2, LiH), obtaining all electronic excited states with chemical accuracy on ideal quantum simulators. Furthermore, we explore the application of quantum error mitigation techniques, demonstrating improved energy accuracy on noisy simulators compared with simulations without mitigation.
Quantum systems in excited states are attracting significant interest with the advent of noisy intermediate-scale quantum (NISQ) devices. While ground states of small molecular systems are typically explored using hybrid variational algorithms like the variational quantum eigensolver (VQE), the study of excited states has received much less attention, partly due to the absence of efficient algorithms. In this work, we introduce the subspace search quantum imaginary time evolution (SSQITE) method, which calculates excited states using quantum devices by integrating key elements of the subspace search variational quantum eigensolver (SSVQE) and the variational quantum imaginary time evolution (VarQITE) method. The effectiveness of SSQITE is demonstrated through calculations of low-lying excited states of benchmark model systems including H 2 and LiH molecules. A toy Hamiltonian is also employed to demonstrate that the robustness of VarQITE in avoiding local minima extends to its use in excited state algorithms. With this robustness in avoiding local minima, SSQITE shows promise for advancing quantum computations of excited states across a wide range of applications.
Abstract Quantum computing is a technology that utilizes the principles of quantum mechanics to perform complex computational processes. In this work, we use Qiskit Module from IBM to do quantum computational calculation using Variational Quantum Eigensolver (VQE) algorithm. VQE is a hybrid quantum-classical method that combines a quantum computer to measure energies and a classical computer to process the measurement results and update the parameters of the quantum computer. The purpose of VQE is to find the ground state energy of a chemical system. In the previous study, many of the VQE calculations have been done on simple molecules. So, in this study, we would like to use Hydrazine molecule as our object of VQE calculation. Furthermore, these results will be compared with the results from the classical calculation (MP2, CCSD(T), QCISD(T), and CASSCF) methods for testing the effectiveness of VQE using Unitary Coupled-Cluster Single and Double excitations (UCCSD) Ansatz. The quantum algorithm based on the UCCSD Ansatz led to a simplification of the algorithm by reducing the circuit depth. Then, the possibility to use active space approximation, can be used to reduce the quantum gates while trying to keep a good level of accuracy. In this study, we chose (2,2) and (4,4) active spaces. Based on the results, as we increase the size of the active space during the evaluation of the single-point energy, the estimated ground states obtained from the VQE algorithm yield nearly identical values. Conversely, in CASSCF calculations, expanding the active space introduces more energy corrections, thus making it more sensitive. Additionally, when examining potential energy surfaces, VQE demonstrates results that gradually align with CCSD(T) and QCISD(T) methods.