Solar cell technology has reached a limiting efficiency, now known as the Shockley-Queisser efficiency limit, and researchers are investigating novel strategies like quantum mechanics to surpass this limit. Restriction is imposed by the fact that singlet fission singlet fission , a quantum process, doubles the number of charge carriers charge carriers per photon generated, which results in giving two low-energy excitons from one high-energy photon. But classical simulations would find it inherently difficult to avoid the cryptic nature of its underlying quantum structure, that is, based in coherence, spin states, and environmental influence. The principle of quantum mechanics enables the development of special quantum simulation quantum simulation as a revolutionary way of performing and improving singlet fission research. The material can be screened, the electrical structure studied, and the device or optoelectronic optimized while keeping the material quantum coherent. Hybrid quantum-classical approaches reduce the computational burdens of existing hardware, while sophisticated algorithms such as quantum phase estimation and variational quantum eigensolvers are used to accurately model molecular energetics. To efficiently simulate singlet fission in molecular systems (both simple models, such as linear H 4 molecules, as well as more complicated materials), this work relies on the PDS( K ) energy functional framework. Such a technique alleviates quantum resource limits by carrying out fewer Pauli string measurements, shorter qubits, and using commutativity principles to simplify calculation. Researchers thus find potential materials by modeling quantum states and excitonic transitions and improve device topologies to find the most efficient paths for converting that energy. But there are still distances to go, from producing stable, affordable materials to scaling up quantum technology. This, however, demonstrates that quantum simulation can propel solar cells to entirely novel heights, where they can be paired with existing science, such as perovskite solar cells. The potential for lower energy expenses and environmental gains are business implications. This is a revolutionary development in device engineering, quantum computing, and material discovery material discovery – the bellwether for the future of sustainable energy. Long-lasting solar cells result from quantum hardware and algorithms progressing alongside quantum theory and experimental testing, culminating in ultraefficient processes. So, quantum simulation represents an important breakthrough with respect to the efforts needed to solve the world’s energy problems and to promote an environmentally friendly and energy-secure future.
Solar cell technology has reached a limiting efficiency, now known as the Shockley-Queisser efficiency limit, and researchers are investigating novel strategies like quantum mechanics to surpass this limit. Restriction is imposed by the fact that singlet fission singlet fission , a quantum process, doubles the number of charge carriers charge carriers per photon generated, which results in giving two low-energy excitons from one high-energy photon. But classical simulations would find it inherently difficult to avoid the cryptic nature of its underlying quantum structure, that is, based in coherence, spin states, and environmental influence. The principle of quantum mechanics enables the development of special quantum simulation quantum simulation as a revolutionary way of performing and improving singlet fission research. The material can be screened, the electrical structure studied, and the device or optoelectronic optimized while keeping the material quantum coherent. Hybrid quantum-classical approaches reduce the computational burdens of existing hardware, while sophisticated algorithms such as quantum phase estimation and variational quantum eigensolvers are used to accurately model molecular energetics. To efficiently simulate singlet fission in molecular systems (both simple models, such as linear H 4 molecules, as well as more complicated materials), this work relies on the PDS( K ) energy functional framework. Such a technique alleviates quantum resource limits by carrying out fewer Pauli string measurements, shorter qubits, and using commutativity principles to simplify calculation. Researchers thus find potential materials by modeling quantum states and excitonic transitions and improve device topologies to find the most efficient paths for converting that energy. But there are still distances to go, from producing stable, affordable materials to scaling up quantum technology. This, however, demonstrates that quantum simulation can propel solar cells to entirely novel heights, where they can be paired with existing science, such as perovskite solar cells. The potential for lower energy expenses and environmental gains are business implications. This is a revolutionary development in device engineering, quantum computing, and material discovery material discovery – the bellwether for the future of sustainable energy. Long-lasting solar cells result from quantum hardware and algorithms progressing alongside quantum theory and experimental testing, culminating in ultraefficient processes. So, quantum simulation represents an important breakthrough with respect to the efforts needed to solve the world’s energy problems and to promote an environmentally friendly and energy-secure future.
Abstract Vibronic interactions between nuclear motion and electronic states are critical for the accurate modeling of photochemistry. However, accurate simulations of fully quantum non-adiabatic dynamics are often prohibitively expensive for classical methods beyond small systems. In this work, we present a quantum algorithm based on product formulas for simulating time evolution under a general vibronic Hamiltonian in real space, capable of handling an arbitrary number of electronic states and vibrational modes. We develop the first trotterization scheme for vibronic Hamiltonians beyond two electronic states and introduce an array of optimization techniques for the exponentiation of each fragment in the product formula, resulting in a remarkably low cost of implementation. To demonstrate practical relevance, we outline a proof-of-principle integration of our algorithm into a materials discovery pipeline for designing more efficient singlet fission-based organic solar cells. We estimate that 100 fs of propagation using a second-order Trotter product formula for a 6-state, 21-mode model of exciton transport at an anthracene dimer requires 154 qubits and 2.76 × 106 Toffoli gates. While a 4-state, 246-mode model describing charge transfer at an anthracene-fullerene interface requires 1053 qubits and 2.66 × 107 Toffoli gates.
molecule as a simple model for singlet fission. We use the Peeters-Devreese-Soldatov energy functional to calculate the necessary energetics based on the moments of the Hamiltonian estimated on the quantum computer. To reduce the number of required measurements, we use several independent strategies: 1) reduction of the size of the relevant Hilbert space by tapering off qubits; 2) measurement optimization via rotations to eigenbases shared by groups of qubit-wise commuting Pauli strings; and 3) parallel execution of multiple state preparation and measurement operations using all 20 qubits available on the Quantinuum H1-1 quantum hardware. Our results meet the energetic requirements for singlet fission, are in excellent agreement with exact transition energies (for the chosen one-particle basis), and outperform classical methods considered computationally feasible for singlet fission candidates.
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.
Insufficient experimental cycles, costly material screening, and ineffective solar-material synthesis all slow the rapid growth of photovoltaic nanostructures. Conventional machine learning algorithms and traditional trial-and-error techniques are inadequate in capturing the quantum-scale interactions that govern nano-engineered solar absorbers, resulting in suboptimal energy conversion efficiency and delayed commercialization. This study proposes a Nano-Quantum Variational Diffusion Model (NQVDM) to expedite the identification and optimization of photovoltaic nanomaterials, potentially addressing these challenges. The proposed framework employs bandgap energy, surface morphology, defect density, lattice structure, and variational quantum circuits to model high-dimensional synthesis parameters. The diffusion model iteratively improves the candidate material distributions until they reach high-efficiency configurations. The quantum variational layer, on the other hand, uses a hybrid quantum-classical optimization technique to capture nonlinear quantum correlations. An experimental evaluation was conducted using a reference dataset of photovoltaic nanostructure parameters, which had been validated both theoretically and experimentally. The NQVDM outperforms standard optimization methods like DNNs and GANs, as well as Bayesian optimization, in terms of stability and generating power. The results reveal that the synthesis optimization error decreased by 31.2%, the expected power conversion efficiency (PCE) increased by 22.4%, and the material discovery speed increased by 28.6%. Also, the time required for the computer to converge was reduced by 26.8%, suggesting that the diffusion-driven quantum framework might speed up processes. The proposed NQVDM significantly enhances the efficiency of solar material production by integrating quantum variational learning with probabilistic diffusion modeling. This paradigm enables data-driven, quantum-aware nanostructure design, enhancing photovoltaic performance measures while reducing experimental overhead. This technology is a strategy to move forward, scalable for identifying smart solar materials for the next generation.
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.
The advancements in classical computational components such as CPUs and GPUs have had a reverberating impact on the democratization of computational power available, with the field of computational chemistry reaping significant benefits from the propulsion of the limits of systems studied. The accurate prediction of chemical properties and reaction dynamics through computational simulation is now a centerpiece of modern chemical research, significantly accelerating discoveries research areas of materials science and drug development, as well as industrially relevant processes such as the emission of aldehydes from soft foams, flame retardants and pyrolysis and the carbonylation of epoxides. These achievements of computational chemistry still are overshadowed in the face of fundamental bottlenecks where classical methods remain constrained by the exponential scaling when treating large and/or strongly correlated quantum many-body systems. This innate computational complexity often constitutes chemically accurate calculations based on first principles impractical, or plain impossible, for systems beyond modest sizes. Quantum computing offers a potential pathway around these limitations, originally formally formulated by David Deutsch as a tool for simulating quantum systems, with the first quantum algorithms indicating speed-ups compared to classical counterparts arising from Peter Shor and Lov Grover for the prime factorization of an integer number and unstructured search respectively. Relying on fundamental theorems by Claude Shannon, it has been demonstrated that through the use of error-correcting codes fault-tolerant quantum computers are able to safeguard information against noise and decoherence, strongly indicating towards exponential speedups in solving central computational chemistry problems, such as determining ground state energies or reaction pathways of chemical systems. Substantial strides in experimental quantum computing over the past decade, including proof-of-principle demonstrations of quantum error correction, mean to bring full-scale fault-tolerant quantum computing within the grasp of humanity. The breakthroughs needed to attain scalable, error-corrected quantum computation with capabilities exceeding those of classical methods in real-world scenarios is likely still some years away. While striving to address the engineering challenges standing in the way of achieving fault tolerant quantum computing, substantial effort has been focused toward developing and benchmarking hybrid algorithms tailored for the Noisy Intermediate- Scale Quantum (NISQ) devices, available now to researchers. These hybrid quantum-classical algorithms, with the most notable being the Variational Quantum Eigensolver (VQE), allow the probing of quantum computing’s utility in computational chemistry even in the face of significant hardware noise and limited coherence times. The work presented in this thesis introduces several key additions the VQE framework, geared especially towards to NISQ hardware, where the mindful use of available resources is imperative. First, the implementation of the Conjugate Model Search optimizer is described, an optimization method that integrates simple models that are surrogate to the original function, combined with conjugate gradient optimization to navigate effectively parameter landscapes typical of variational algorithms, prone to noise. Second, this thesis contains the proposal and extensive study of a hyperparameter tuning procedure employing genetic algorithms, allowing for comprehensive simulation benchmarks that augment the robustness and efficiency of VQE implementations with respect to convergence time through real wall-time models of quantum processors. Continuing, a bridge is presented between quantum simulation and quantum chemistry, through qubit-to-fermion mappings of existing quantum gates, further enabling the direct and accurate modeling of chemical systems on trapped atom quantum hardware with the added arsenal provided by techniques designed for qubit architectures. Additionally, experimental data contained in this thesis include detailed benchmarking data collected from state-of-the-art trapped ion quantum processors available at the Johannes Gutenberg Universität Mainz, demonstrating the actual viability and performance of various methodologies for extracting results. Lastly, the state of VQE is examined, as it served as a springboard for a plethora of advancements in the development of quantum computing hardware, as well as the direction the field of simulation of computational quantum chemistry via quantum devices is headed towards in the coming years.