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 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 report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation.We use a programmable array of superconducting qubits to compute the energy surface of molecular hydrogen using two distinct quantum algorithms.First, we experimentally execute the unitary coupled cluster method using the variational quantum eigensolver.Our efficient implementation predicts the correct dissociation energy to within chemical accuracy of the numerically exact result.Second, we experimentally demonstrate the canonical quantum algorithm for chemistry, which consists of Trotterization and quantum phase estimation.We compare the experimental performance of these approaches to show clear evidence that the variational quantum eigensolver is robust to certain errors.This error tolerance inspires hope that variational quantum simulations of classically intractable molecules may be viable in the near future.
Quantum chemistry models atoms and molecules using quantum mechanics, with the electronic structure problem at its core. This problem, which determines the energy and properties of electrons in a molecule, is exponentially hard to solve exactly on classical computers. To approximate solutions, effective models introduce layers of simplifications. However, these approximations can fail for strongly correlated systems, necessitating alternative approaches. Quantum computers, capable of processing quantum information natively, offer a promising path to simulate molecular systems beyond classical reach, with potential applications in catalysis, materials science, and photochemistry. Despite rapid advancements in quantum hardware, practical quantum computations remain challenging due to limited resources. This thesis optimizes quantum resource usage in computational chemistry, introducing novel embedding techniques and exploring applications beyond ground-state energies. Chapter 1 establishes the foundation by reviewing quantum chemistry methods, from Hartree-Fock and Density Functional Theory (DFT) to active space approaches. It also provides an introduction to quantum computing for chemistry, focusing on near-term algorithms. The following chapters present novel methods aimed at reducing quantum resource requirements and tackling problems classical methods struggle with. Chapter 2 introduces a technique to reduce the
λ-norm of the electronic structure Hamiltonian, a key measure of quantum resource demand. By transforming molecular orbitals, this method enhances sampling efficiency in Noisy Intermediate-Scale Quantum (NISQ) algorithms and reduces runtime in Fault-Tolerant Quantum Computing (FTQC). Localized orbitals outperform canonical ones, and an orbital-optimization scheme is proposed to minimize the
λ-norm. Benchmarks on hydrogen chains, FeMoco, and Ruthenium complexes demonstrate its effectiveness. Chapter 3 presents the FragPT2 framework for simulating large systems with multiple interacting fragments. This method embeds each fragment in the mean field of others and applies multi-reference perturbation theory to capture inter-fragment correlations such as dispersion and charge transfer efficiently. The approach reduces computational overhead and enables scalable simulations, as shown in applications to \ce{N2} dimers, aromatic dimers, and butadiene. Chapter 4 focuses on photochemical systems, where multiple electronic states must be treated equally. The state-averaged orbital-optimized variational quantum eigensolver (SAOOVQE) is extended with analytical energy gradients and non-adiabatic couplings, enabling geometry optimizations and simulations of non-adiabatic dynamics on quantum hardware. This is demonstrated by locating the conical intersection in formaldimine, highlighting SAOOVQE’s potential for studying photoisomerization in biomolecules. Building on this, Chapter 5 develops a quantum algorithm to detect conical intersections, where the Born-Oppenheimer approximation breaks down. These intersections are characterized by a discrete Berry phase (0 or
π), representing their topological nature. The algorithm extracts this phase as a single bit of information, ensuring resilience to noise. Analytical proof and numerical validation using a toy molecular model illustrate its effectiveness for studying light-perception biochemistry and related photochemical processes. Finally, Chapter 6 explores machine learning density functionals trained on noisy quantum data. While DFT is computationally efficient, its accuracy depends on approximated exchange-correlation functionals. This work proposes a neural network-based approach to learn the exact functional from quantum data, showing that training on noisy outputs from NISQ devices can mitigate sampling errors. Applications to the Fermi-Hubbard model and other systems demonstrate its potential to generalize and provide accurate results under noise. This thesis advances quantum computational chemistry by optimizing quantum resource usage, developing new embedding strategies, and expanding the scope of quantum simulations beyond ground-state properties. These contributions pave the way for practical quantum algorithms that could revolutionize molecular simulations in the near future.
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.
Solving interacting multi-particle systems is a central challenge in quantum chemistry and condensed matter physics. In this work, we investigate the computation of ground states and ground-state energies for the He-H+ and H2O molecules using quantum computing techniques. We employ the variational quantum eigensolver (VQE), implemented both on a quantum computer simulator and on an IBM quantum device. The resulting energies are benchmarked against exact ground-state energies obtained via classical methods. Simulations of the H2O molecule were performed on Nottingham's High Performance Computing (HPC) facilities.