High Resolution Image Download MS PowerPoint Slide This study explores the application of quantum computing to metal cluster analysis through the development and implementation of a quantum-DFT embedding workflow. Classical computational methods, while transformative, often face limitations in achieving chemical accuracy and computational efficiency, particularly for nanoscale systems. To address these challenges, we integrate the Variational Quantum Eigensolver (VQE) with density functional theory (DFT), leveraging the capabilities of quantum computing aiming to improve the modeling of electronic structures. Aluminum and gold clusters were used as model systems to test the established workflow. The workflow successfully determined electronic properties for aluminum clusters up to Al 7 – . Although gold clusters were used as a test case to investigate the potential reduction of nitric oxide (NO), memory limitations, the lack of relativistic corrections, and the inability to handle open-shell systems presented challenges that underscore the need for advancements in quantum hardware and algorithms. This proof-of-concept study demonstrates the potential of quantum DFT embedding to advance materials discovery, including applications in catalysis and nanomaterial design, while providing insights into the current limitations of near-term quantum devices.
High Resolution Image Download MS PowerPoint Slide This study explores the application of quantum computing to metal cluster analysis through the development and implementation of a quantum-DFT embedding workflow. Classical computational methods, while transformative, often face limitations in achieving chemical accuracy and computational efficiency, particularly for nanoscale systems. To address these challenges, we integrate the Variational Quantum Eigensolver (VQE) with density functional theory (DFT), leveraging the capabilities of quantum computing aiming to improve the modeling of electronic structures. Aluminum and gold clusters were used as model systems to test the established workflow. The workflow successfully determined electronic properties for aluminum clusters up to Al 7 – . Although gold clusters were used as a test case to investigate the potential reduction of nitric oxide (NO), memory limitations, the lack of relativistic corrections, and the inability to handle open-shell systems presented challenges that underscore the need for advancements in quantum hardware and algorithms. This proof-of-concept study demonstrates the potential of quantum DFT embedding to advance materials discovery, including applications in catalysis and nanomaterial design, while providing insights into the current limitations of near-term quantum devices.
Quantum computing has emerged as a promising platform for simulating strongly correlated systems in chemistry, for which the standard quantum chemistry methods are either qualitatively inaccurate or too expensive. However, due to the hardware limitations of the available noisy near-term quantum devices, their application is currently limited only to small chemical systems. One way for extending the range of applicability can be achieved within the quantum embedding approach. Herein, we employ the projection-based embedding method for combining the variational quantum eigensolver (VQE) algorithm, although not limited to, with density functional theory (DFT). The developed VQE-in-DFT method is then implemented efficiently on a real quantum device and employed for simulating the triple bond breaking process in butyronitrile. The results presented herein show that the developed method is a promising approach for simulating systems with a strongly correlated fragment on a quantum computer.
Scientific simulations demand methods combining scalability with predictive accuracy. Density Functional Theory (DFT) on High-Performance Computing (HPC) enables large-scale electronic-structure simulations but is limited by approximations affecting strongly correlated systems and band-gap predictions. Quantum computing offers a pathway to address this, though current Noisy Intermediate-Scale Quantum (NISQ) hardware remains constrained by qubit resources, noise, and execution cost.
This work presents a hybrid DFT-Quantum Embedding (QDFT) framework integrating classical HPC-based DFT with a quantum electronic-structure solver. Large systems are partitioned to isolate a chemically relevant active space, treated via the Variational Quantum Eigensolver (VQE), while the remaining degrees of freedom are described by DFT. The framework incorporates active-space selection, embedded Hamiltonian construction, symmetry preservation, operator mapping, self-consistent density updating, and modular classical-quantum coupling.
We focus on noiseless quantum simulation to systematically evaluate accuracy, convergence, active-space dependence, computational cost, and HPC scalability without hardware noise. Detailed profiling identifies computational bottlenecks and highlights limitations of CPU-based quantum simulation. A QPU runtime-estimation methodology is additionally developed to assess execution requirements on actual quantum hardware.
Results demonstrate quantum embedding's potential to improve selected electronic-structure properties while retaining classical HPC's scalability. Noisy quantum simulation and QPU execution remain key future directions, providing a pathway toward practical, scalable HPC-quantum hybrid simulations as hardware matures.
Quantum chemistry plays a crucial role in advancing fundamental understanding of chemical processes and driving innovation in energy, medicine, and materials science. Recent progress in quantum computing has opened new possibilities for molecular simulations that are beyond the practical limits of classical approaches. Academic studies have demonstrated hybrid Density Functional Theory (DFT) and Variational Quantum Eigensolver (VQE) benchmarks on small transition-metal systems, such as iron porphyrin and heme analogues, using current noisy intermediate-scale quantum (NISQ) hardware. Although full-scale simulations of complex biological systems such as the complete Cytochrome P450 (CYP450) active site remain a long-term goal due to limitations in qubit numbers, coherence times, and error correction, the underlying methodologies are now well established. At present, quantum chemistry applications in drug discovery remain largely experimental, and widespread practical medical impact is expected to require further technological advances over the next 5–10 years. Nevertheless, quantum software and algorithm development for chemical applications is progressing rapidly. This perspective summarizes recent advances in quantum computing algorithms, hardware, and software relevant to chemistry, and critically discusses the remaining challenges and opportunities for applying quantum computing to chemical problems, with particular emphasis on drug discovery and development.
Heterogeneous catalysis plays a critical role in many industrial processes, including the production of fuels, chemicals, and pharmaceuticals, and research to improve current catalytic processes is important to make the chemical industry more sustainable. Despite its importance, the challenge of identifying optimal catalysts with the required activity and selectivity persists, demanding a detailed understanding of the complex interactions between catalysts and reactants at various length and time scales. Density functional theory (DFT) has been the workhorse in modeling heterogeneous catalysis for more than three decades. While DFT has been instrumental, this review explores the application of quantum computing algorithms in modeling heterogeneous catalysis, which could bring a paradigm shift in our approach to understanding catalytic interfaces. Bridging academic and industrial perspectives by focusing on emerging materials, such as multicomponent alloys, single-atom catalysts, and magnetic catalysts, we delve into the limitations of DFT in capturing strong correlation effects and spin-related phenomena. The review also presents important algorithms and their applications relevant to heterogeneous catalysis modeling to showcase advancements in the field. Additionally, the review explores embedding strategies where quantum computing algorithms handle strongly correlated regions, while traditional quantum chemistry algorithms address the remainder, thereby offering a promising approach for large-scale heterogeneous catalysis modeling. Looking forward, ongoing investments by academia and industry reflect a growing enthusiasm for quantum computing's potential in heterogeneous catalysis research. The review concludes by envisioning a future where quantum computing algorithms seamlessly integrate into research workflows, propelling us into a new era of computational chemistry and thereby reshaping the landscape of modeling heterogeneous catalysis.
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.
Hybrid artificial intelligence–quantum (AI–Q) models arise at the intersection of two rapidly advancing computational paradigms, promising unprecedented capabilities for real-time molecular structure prediction. In classical computational chemistry, the accurate determination of ground-state energies and optimized geometries relies on ab initio methods such as density functional theory (DFT) and coupled-cluster approaches, which, while highly precise, exhibit steep polynomial or exponential scaling with system size. Conversely, deep neural networks (DNNs) have demonstrated remarkable success in learning potential energy surfaces (PES) from large datasets, achieving rapid inference but sometimes lacking the ultimate chemical accuracy required for predictive applications in drug discovery or materials design. On the other hand, variational quantum eigensolvers (VQEs) leverage the intrinsic parallelism of quantum hardware to approximate electronic structure problems, yet they remain constrained by qubit coherence times, gate fidelities, and circuit depth limitations inherent to noisy intermediate-scale quantum (NISQ) devices.