The advancement of computational chemistry is currently stalled by the exponential memory scaling required to simulate strongly correlated electron systems on classical supercomputers. This fundamental barrier significantly impedes the rational design of complex pharmaceuticals and next-generation catalytic materials. This research aims to rigorously validate the immediate utility of Noisy Intermediate-Scale Quantum (NISQ) processors, demonstrating that “Quantum Advantage” has shifted from a theoretical milestone to a practical industrial reality. We employed a comparative research design utilizing the Variational Quantum Eigensolver (VQE) algorithm on the IBM Eagle quantum processor. The study targeted the electronic structure of iron-sulfur clusters and KRAS-G12C inhibitor binding sites, benchmarking quantum outputs against classical Density Functional Theory (DFT) and Full Configuration Interaction (FCI) standards, utilizing Zero-Noise Extrapolation for error mitigation. Results indicate that quantum simulations achieved chemical accuracy (within 1.6 kcal/mol) for these complex systems, whereas classical methods failed with deviations exceeding 8 kcal/mol. The data confirms that quantum hardware can now resolve electronic correlations invisible to classical approximation. We conclude that quantum computing offers a tangible, immediate pathway to accelerate discovery cycles in drug development and material science, necessitating the integration of hybrid quantum workflows into modern R&D pipelines.
The advancement of computational chemistry is currently stalled by the exponential memory scaling required to simulate strongly correlated electron systems on classical supercomputers. This fundamental barrier significantly impedes the rational design of complex pharmaceuticals and next-generation catalytic materials. This research aims to rigorously validate the immediate utility of Noisy Intermediate-Scale Quantum (NISQ) processors, demonstrating that “Quantum Advantage” has shifted from a theoretical milestone to a practical industrial reality. We employed a comparative research design utilizing the Variational Quantum Eigensolver (VQE) algorithm on the IBM Eagle quantum processor. The study targeted the electronic structure of iron-sulfur clusters and KRAS-G12C inhibitor binding sites, benchmarking quantum outputs against classical Density Functional Theory (DFT) and Full Configuration Interaction (FCI) standards, utilizing Zero-Noise Extrapolation for error mitigation. Results indicate that quantum simulations achieved chemical accuracy (within 1.6 kcal/mol) for these complex systems, whereas classical methods failed with deviations exceeding 8 kcal/mol. The data confirms that quantum hardware can now resolve electronic correlations invisible to classical approximation. We conclude that quantum computing offers a tangible, immediate pathway to accelerate discovery cycles in drug development and material science, necessitating the integration of hybrid quantum workflows into modern R&D pipelines.
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.
Early-stage decisions in the pharmaceutical development process carry outsized consequences for eventual clinical success, yet the computational tools underpinning these decisions remain fundamentally constrained. Computer-aided drug design (CADD) has transformed how researchers navigate chemical space and predict ligand–target interactions, but classical implementations rely on mechanical force-field approximations that fail to capture polarisation, charge transfer, and electron correlation effects central to molecular recognition and reactivity. Quantum-mechanical treatments that correctly describe these phenomena scale exponentially with system size on classical hardware, rendering them impractical for drug-relevant biomolecules. Quantum computing offers a physically motivated path beyond this scaling barrier: by exploiting superposition, entanglement, and interference, quantum algorithms can in principle simulate electronic structure with polynomial resource requirements for targeted problem classes. This article provides a theoretical review of how quantum computation integrates throughout the drug discovery pipeline, from target identification to lead optimisation. Its primary contribution is a pipeline-level mapping of quantum methods—including the Variational Quantum Eigensolver (VQE), quantum machine learning, and quantum-enhanced optimisation—to specific drug development stages. The review critically distinguishes near-term NISQ (Noisy Intermediate-Scale Quantum: current devices with 50–1,000 noisy qubits operating without full error correction) capabilities from fault-tolerant quantum computing (FTQC) requirements, quantifies current resource gaps through a worked CYP3A4 case study, and identifies algorithmic limitations (barren plateaus, ansatz expressibility, measurement overhead), hardware scalability, and error correction overhead as the principal barriers to practical deployment.
However, accurately simulating molecular interactions constitutes the central, but open question of computer chemistry. This is a core task for efforts in drug development, catalyst design, and materials science. Most classical algorithms perform well on smaller systems, but incumbent exponential scaling penalties are unavoidable as models grow larger and more complex to describe quantum behaviour. An entirely new paradigm for this problem is now introduced by quantum computing: superposition and entanglement resources natively encode quantum states and model their electronic structures. The paper will thus take a high-level view of the leading quantum algorithms, with particular regard to those called Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE), while evaluating their performance on today's Noisy Intermediate Scale Quantum (NISQ) devices. Next will follow discussions on algorithm design and current hardware capabilities, before which open challenges on scalability, error mitigation, and algorithm design will be outlined. Emphasis shall also be placed on the ethics and accessibility issues as well as the timelines with which quantum computing might gain a practical advantage. Linking together the recent advances and limitations, this paper posits how quantum computer implementation may change the face of chemical modelling and scientific discovery.
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.
Abstract Quantum computers promise to solve electronic structure, lattice dynamics, and strongly correlated problems beyond classical reach. However, near-term noisy intermediate scale quantum (NISQ) processors require systematic benchmarking to establish where variational algorithms with error mitigation can achieve chemical accuracy and cor rectly predict physical phenomena. Here we present 28 quantum material simula tions implemented with Qiskit, spanning molecular hydrogen (H 2 ) through the iron molybdenum cofactor (FeMoco), one-dimensional Hubbard chains, Kitaev honeycomb spin liquids, topological insulators (SSH model), BCS superconductors, and disordered Anderson insulators. Using the variational quantum eigensolver (VQE) with UCCSD ansatz, we achieve chemical accuracy (±1.6 mHa) for H 2 across the full dissociation curve and recover exact ground-state energies for H4 within 9 mHa after zero-noise extrapolation (ZNE) — reducing raw VQE errors from 152 mHa to 9 mHa. We accu rately identify the transverse-field Ising quantum critical point (h c = J) with 4 qubits, resolve Majorana Dirac dispersions in the Kitaev spin liquid, and reproduce the Mott transition in the Fermi-Hubbard model at U c = 6t. Resource estimation for FeMoco reveals O(N 2.5 ) scaling, projecting chemical accuracy with ∼200 logical qubits. Across all benchmarks, we quantify the accuracy-circuit depth tradeoff, demonstrating that depth-6 VQE ans¨atze achieve chemical accuracy for small molecules while depth-10 is required for correlated lattices. These results establish a comprehensive benchmark suite for evaluating quantum hardware, error mitigation protocols, and variational al gorithms for materials science on NISQ processors.
This study examines how quantum computing (QC) is being applied to molecular design and drug discovery. This study aims to investigates how QC surpasses classical limitations, focusing on empirical performance in precision, accuracy, and optimisation tasks. Study design use PRISMA 2009 guidelines, 15 empirical studies (2020-2025) were included. Data were extracted on the drug-discovery stage, the algorithm used, evaluation metrics, benefits, and limitations. The findings show QC outperforms classical methods particularly through hybrid quantum–classical models. Thirteen studies reported superior gains, including AUC–ROC values of 0.80–0.95, +30% improvement in drug-likeness (QED), +6% increase in prediction accuracy, and up to 99% accuracy in drug–target interaction tasks. However, noisy intermediate-scale quantum (NISQ) hardware limitations and poor scalability limit real-world deployment, due to noise, and limited qubit counts. Consequently, current performance results are largely simulation-based rather than hardware-validated. In contrast to prior algorithm-centric reviews, this study provides a consolidated empirical synthesis and proposes a hybrid quantum–classical pipeline that maps high-performing algorithms across the drug discovery workflow under NISQ-era constraints. These findings inform pharmaceutical research and development by identifying realistic adoption pathways and the boundaries of current technological readiness.