The integration of quantum chemistry, machine learning, and optimization calculations is expected to accelerate materials discovery by making large chemical spaces amenable to computational study, a challenging task for classical computers. In this study, we develop a quantum-classical computing scheme involving the computational-basis variational quantum deflation (cVQD) method for calculating the excited states of a general classical Hamiltonian, such as an Ising Hamiltonian. We apply this scheme to the practical use case of generating photochromic diarylethene (DAE) derivatives for photopharmacology applications. Using a dataset of 384 DAE derivatives from quantum chemistry calculation results, we show that machine learning can accurately predict the wavelength of maximum absorbance, λ max , of 4,096 DAE derivatives. After screening over 4,096 molecules using the computing scheme, we identified 5 DAE candidates that have important applications in photopharmacology. In detail, a 12-qubit cVQD calculation provides the ground state and 4 excited states of an Ising Hamiltonian corresponding to DAE candidates possessing large λ max . On a quantum simulator, results are found to be in excellent agreement with those obtained by an exact eigensolver. Utilizing error suppression and mitigation techniques, cVQD on a real quantum device produces results with accuracy comparable to calculations on the simulator. Finally, we show that quantum chemistry calculations for the DAE candidates provides a path to achieving large λ max and oscillator strengths by means of the molecular engineering of DAE derivatives.
The integration of quantum chemistry, machine learning, and optimization calculations is expected to accelerate materials discovery by making large chemical spaces amenable to computational study, a challenging task for classical computers. In this study, we develop a quantum-classical computing scheme involving the computational-basis variational quantum deflation (cVQD) method for calculating the excited states of a general classical Hamiltonian, such as an Ising Hamiltonian. We apply this scheme to the practical use case of generating photochromic diarylethene (DAE) derivatives for photopharmacology applications. Using a dataset of 384 DAE derivatives from quantum chemistry calculation results, we show that machine learning can accurately predict the wavelength of maximum absorbance, λ max , of 4,096 DAE derivatives. After screening over 4,096 molecules using the computing scheme, we identified 5 DAE candidates that have important applications in photopharmacology. In detail, a 12-qubit cVQD calculation provides the ground state and 4 excited states of an Ising Hamiltonian corresponding to DAE candidates possessing large λ max . On a quantum simulator, results are found to be in excellent agreement with those obtained by an exact eigensolver. Utilizing error suppression and mitigation techniques, cVQD on a real quantum device produces results with accuracy comparable to calculations on the simulator. Finally, we show that quantum chemistry calculations for the DAE candidates provides a path to achieving large λ max and oscillator strengths by means of the molecular engineering of DAE derivatives.
Determining the spectral structure of molecular Hamiltonians is one of the most important and challenging research fields in quantum chemistry. In the near future, such a problem may be solvable by means of quantum simulation. However, constrained by a handful of qubits and noisy quantum gates available in the near-term quantum devices, hybrid quantum-classical algorithms based on variational methods such as variational quantum eigensolver have become a preferable choice. Here we propose an interesting variational quantum algorithm for obtaining the excited states of many-body Hamiltonians. To obtain the kth excited states, the previous variational quantum deflation algorithm requires the use of k different variational circuits, which is now reduced to only two circuits with our algorithm. The feasibility of the algorithm was numerically tested on the hydrogen molecule with and without random noise, where the chemical accuracy is reached for both cases. This algorithm is expected to significantly reduce the resource requirements of quantum simulation.
Abstract This study describes a hybrid quantum-classical computational approach for designing synthesizable deuterated Alq3 emitters possessing desirable emission quantum efficiencies (QEs). This design process has been performed on the tris(8-hydroxyquinolinato) ligands typically bound to aluminum in Alq3 and involves optimization of the hydrogen/deuterium (H/D) ratios in these molecules for the discovery of optimally deuterated high-efficiency OLED emitters. It involves a multi-pronged approach which first utilizes classical quantum chemistry to predict the emission QEs of the Alq3 ligands. These initial results were then used as a machine learning dataset for a factorization-machine-based model which was applied to construct an Ising Hamiltonian to predict emission quantum efficiencies on a classical computer. We show that such a factorization machine-based approach can yield accurate property predictions for all 64 deuterated Alq3 emitters with 13 training values. Moreover, another Ising Hamiltonian could be constructed by including synthetic constraints which could be used to perform optimizations on a quantum simulator and device using the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA) to discover a molecule possessing the optimal QE and synthetic cost. We observe that both VQE and QAOA can correctly predict the optimal molecule with greater than 0.95 probability on quantum simulators. These probabilities decrease to 0.83 and 0.075 for simulations with VQE and QAOA, respectively, on a quantum device, while the application of the readout error mitigation technique only improves probabilities to 0.90 and 0.084. We have developed the Recursive Probabilistic Variable Elimination (RPVE) method for simulations on noisy devices that recursively eliminates qubits depending on the probability that each qubit possesses a binary value and have demonstrated that RPVE improves the accuracies of VQE and QAOA for predictions of optimal solutions with probabilities of 0.97 on a quantum device.
Accurate prediction of molecular structures and properties is vital for chemistry; materials science, and drug discovery, yet classical electronic-structure methods often fail for strongly correlated systems, large basis sets, and complex potential-energy landscapes. Quantum technology encompassing quantum computing, quantum machine learning, and hybrid quantum classical strategies offers a fundamentally new paradigm by encoding many-electron wave functions directly on qubits and exploiting superposition and entanglement to explore exponentially large Hilbert spaces. This review synthesizes recent algorithmic and hardware advances relevant to molecular modelling, including the Variational Quantum Eigensolver (VQE), Quantum Phase Estimation (QPE), quantum unitary coupled-cluster (q-UCC) families, equation-of-motion and subspace methods for excited states, tensor-network hybrids, and quantum kernel and variational QML approaches. We examine noise-aware hybrid workflows, error-mitigation techniques, symmetry-preserving ansätze, and operator-factorization methods that reduce measurement and gate overhead. Representative applications are discussed: ground- and excited-state energy prediction, potential-energy surface mapping, geometry and transition-state optimization, spectroscopic property estimation (IR, UV–Vis, NMR, EPR), and reaction-dynamics scenarios where non-adiabatic effects and conical intersections dominate. Resource estimates and scaling analyses clarify current NISQ limitations qubit counts, circuit depth, shot complexity and delineate the roadmap to fault-tolerant QPE for chemical accuracy. We compare quantum approaches with classical baselines (DFT, CCSD (T), multireference methods), identifying domains where quantum methods already show promise (strong correlation, multi-reference dissociation, spin-state ordering) and where classical methods remain competitive. Finally, we highlight near-term industrial opportunities in drug design, catalysis, CO2 capture, and energy materials, and outline critical research directions: algorithmic reductions in measurement/precision cost, hardware improvements in fidelity and connectivity, scalable ansatz design, and integrated software stacks for reproducible hybrid simulations. Together, these developments indicate that while practical, large-scale quantum advantage for general chemistry remains future work, quantum technologies are rapidly maturing into powerful tools for targeted molecular problems that are intractable with existing classical techniques.
The possibility of performing quantum chemical calculations using quantum computers has attracted much interest. In this regard, variational quantum deflation (VQD) is a quantum-classical hybrid algorithm for the calculation of excited states with noisy intermediate-scale quantum (NISQ) devices. Although the validity of this method has been demonstrated, there have been few practical applications, primarily because of the uncertain effect of calculation conditions on the results. In the present study, calculations of the core-excited and core-ionized states for common molecules based on the VQD method were simulated using a classical computer, focusing on the effects of the weighting coefficients applied in the penalty terms of the cost function. Adopting a simplified procedure for estimating the weighting coefficients based on molecular orbital levels allowed these core-level states to be successfully calculated. The O 1s core-ionized state for a water molecule was calculated with various weighting coefficients and the resulting ansatz states were systematically examined. The application of this technique to functional materials was demonstrated by calculating the core-level states for titanium dioxide (TiO2) and nitrogen-doped TiO2 models. The results demonstrate that VQD calculations employing an appropriate cost function can be applied to the analysis of functional materials in conjunction with an experimental approach.
A programmable quantum device that has a large number of qubits without fault-tolerance has emerged recently. Variational quantum eigensolver (VQE) is one of the most promising ways to utilize the computational power of such devices to solve problems in condensed matter physics and quantum chemistry. As the size of the current quantum devices is still not large for rivaling classical computers at solving practical problems, Fujii et al. proposed a method called ``Deep VQE'', which can provide the ground state of a given quantum system with the smaller number of qubits by combining the VQE and the technique of coarse graining [K. Fujii, K. Mitarai, W. Mizukami, and Y. O. Nakagawa, arXiv:2007.10917]. In this paper, we extend the original proposal of Deep VQE to obtain the excited states and apply it to quantum chemistry calculation of a periodic material, which is one of the most impactful applications of the VQE. We first propose a modified scheme to construct quantum states for coarse graining in Deep VQE to obtain the excited states. We also present a method to avoid a problem of meaningless eigenvalues in the original Deep VQE without restricting variational quantum states. Finally, we classically simulate our modified Deep VQE for quantum chemistry calculation of a periodic hydrogen chain as a typical periodic material. Our method reproduces the ground-state energy and the first-excited-state energy with the errors up to O(1) despite the decrease in the number of qubits required for the calculation by two or four compared with the naive VQE. Our result will serve as a beacon for tackling quantum chemistry problems with classically-intractable sizes by smaller quantum devices in the near future.
Abstract: This study explores the implementation of hybrid quantum-classical algorithms in the realm of drug discovery and molecular simulation, with an emphasis on optimizing molecular interaction prediction. The integration of quantum computing paradigms, particularly the Variational Quantum Eigensolver (VQE), with classical machine learning techniques is evaluated for its efficacy in simulating molecular systems. Key results demonstrate superior accuracy in predicting binding affinities and reaction energetics using the hybrid models compared to traditional methods. The findings offer significant implications for accelerating the drug development pipeline, particularly in early-stage candidate screening. Keywords: Hybrid computing, Quantum simulation, Molecular interaction, Drug discovery, Variational Quantum Eigensolver, Quantum-classical integration