Quantum computational models are emerging as powerful tools for understanding and optimizing photoelectrochemical (PEC) processes that underpin next-generation solar fuel and energy conversion systems. These systems involve complex interactions between photons, charge carriers, catalytic surfaces, and molecular intermediates, making accurate predictive modeling challenging for classical simulation frameworks. This study investigates quantum-enabled approaches for simulating light–matter interactions, exciton dynamics, charge-transfer pathways, and catalytic reaction coordinates within PEC architectures. By leveraging quantum algorithms for electronic structure, nonadiabatic dynamics, and reaction energetics, the research explores how quantum simulations can resolve strongly correlated states, multi-electron excitations, and interfacial charge separation with higher fidelity. The analysis highlights the potential of quantum models to accelerate the discovery of efficient photoelectrode materials, enhance solar-to-fuel conversion efficiencies, and support the rational design of artificial photosynthesis systems. Finally, the paper discusses the integration of quantum computational predictions with experimental PEC workflows and outlines opportunities for future hybrid quantum–classical research.
Quantum computational models are emerging as powerful tools for understanding and optimizing photoelectrochemical (PEC) processes that underpin next-generation solar fuel and energy conversion systems. These systems involve complex interactions between photons, charge carriers, catalytic surfaces, and molecular intermediates, making accurate predictive modeling challenging for classical simulation frameworks. This study investigates quantum-enabled approaches for simulating light–matter interactions, exciton dynamics, charge-transfer pathways, and catalytic reaction coordinates within PEC architectures. By leveraging quantum algorithms for electronic structure, nonadiabatic dynamics, and reaction energetics, the research explores how quantum simulations can resolve strongly correlated states, multi-electron excitations, and interfacial charge separation with higher fidelity. The analysis highlights the potential of quantum models to accelerate the discovery of efficient photoelectrode materials, enhance solar-to-fuel conversion efficiencies, and support the rational design of artificial photosynthesis systems. Finally, the paper discusses the integration of quantum computational predictions with experimental PEC workflows and outlines opportunities for future hybrid quantum–classical research.
Quantum-enhanced computational catalysis offers a transformative framework for modeling complex reaction pathways with unprecedented accuracy and scalability. As catalytic processes often involve strongly correlated electrons, multidimensional potential energy surfaces, and dynamic coupling between nuclear and electronic degrees of freedom, classical simulation methods face steep computational barriers. This study explores a hybrid quantum–classical workflow that integrates quantum algorithms for electronic structure with advanced tensor decomposition techniques to compress, analyze, and optimize high-dimensional reaction data. By leveraging tensor-network representations—such as matrix product states, tensor-train formats, and hierarchical Tucker decompositions—the approach systematically reduces computational cost while maintaining chemically relevant accuracy. The research demonstrates how tensor-optimized quantum simulations can accelerate transition-state discovery, enhance reaction pathway resolution, and improve predictions of catalytic efficiency across heterogeneous, homogeneous, and photocatalytic systems. The synergy between quantum solvers and tensor decomposition establishes a powerful platform for next-generation catalytic modeling and materials discovery.
Embedding-based quantum simulations have emerged as a promising paradigm for bridging quantum and classical scales in complex chemical systems. As molecular processes span multiple spatial and temporal regimes—from localized electron correlation to mesoscale reactivity—traditional quantum chemistry and classical potentials often fail to describe these interactions with sufficient accuracy and scalability. This study explores hybrid embedding frameworks that integrate quantum subspace models, density functional embedding, and quantum many-body solvers within hierarchical multi-scale workflows. By partitioning chemically active regions and treating them with quantum processors while embedding them within larger classical environments, the approach improves accuracy in reaction barrier estimation, excited-state dynamics, and strongly correlated interactions. We evaluate state-of-the-art quantum embedding techniques, discuss advances in quantum resource allocation and error mitigation, and demonstrate how embedding can reduce computational depth while preserving chemically essential features. The study highlights key opportunities for developing next-generation quantum-enhanced multi-scale simulation pipelines capable of accelerating catalyst design, energy storage materials, and biochemical modeling.
Abstract Hydrogen has emerged as a promising energy source, holding the key to achieve low-carbon and sustainable mobility. However, its applications are still limited by modest conversion efficiency in the electrocatalytic oxygen reduction reaction (ORR) within fuel cells. Consequently, the development of novel catalysts and a profound understanding of the underlying reactions have become of paramount importance. The complex nature of the ORR potential energy landscape and the presence of strong electronic correlations present challenges to atomistic modelling using classical computers. This scenario opens new avenues for the implementation of novel quantum computing workflows to address these molecular systems. Here, we present a pioneering study that combines classical and quantum computational approaches to investigate the ORR on pure platinum and platinum/cobalt surfaces. Our research demonstrates, for the first time, the feasibility of implementing this workflow on the H1-series trapped-ion quantum computer and identify the challenges of the quantum chemistry modelling of this reaction. The results highlight the involvement of strongly correlated species in the cobalt-containing catalyst, suggesting their potential as ideal candidates for showcasing quantum advantage in future applications.
Multi-scale modeling of coupled thermal, mechanical, and electromagnetic phenomena presents one of the most computationally demanding challenges in scientific simulation. These interactions often span quantum-level processes, mesoscopic transport behavior, and macroscopic material responses, requiring integrated frameworks capable of resolving complex nonlinearity, strong coupling, and multi-physics feedback loops. Quantum computing offers a transformative pathway toward more accurate and scalable simulations of such systems. This study investigates quantum-enhanced approaches for modeling heat transfer, stress evolution, electromagnetic field propagation, and their mutual interactions across scales. By integrating quantum algorithms for partial differential equations, quantum linear solvers, and quantum-accelerated spectral and tensor representations, the research explores how quantum models can capture high-dimensional dependencies and strong correlations that classical methods struggle to resolve. The analysis highlights the potential of hybrid quantum–classical workflows to accelerate multi-physics simulation, improve predictive fidelity, and support the design of advanced materials, energy devices, and engineering systems that rely on thermal–mechanical–electromagnetic coupling.
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 urgent need for high-performance, sustainable electric vehicle (EV) batteries has driven the exploration of advanced computational methods to accelerate material discovery. Traditional approaches, such as Density Functional Theory (DFT) and Hartree-Fock, face inherent limitations in simulating the complex quantum behaviors of novel battery materials. This paper introduces a pioneering framework leveraging quantum computing, specifically the Variational Quantum Eigen solver (VQE), to overcome these challenges and optimize solid-state battery materials. We focus on Lithium Thiophosphate (Li3PS44), a promising electrolyte for next-generation batteries, and demonstrate how quantum simulations can provide a deeper understanding of electronic structures and electrochemical reactions at an unprecedented level of precision. By benchmarking quantum results against classical methods, we highlight the transformative potential of quantum algorithms to capture intricate electron correlations and reaction dynamics, offering more accurate predictions for material performance. Our findings suggest that quantum computing not only offers a significant leap in the accuracy of battery material simulations but also paves the way for scalable, data-driven optimization of next-generation energy storage systems.