In a monumental stride towards the future of computational chemistry, Caltech professor of chemistry Sandeep Sharma, alongside experts from IBM and Japan’s RIKEN Center for Computational Science, has pioneered a groundbreaking quantum–classical hybrid computing approach. This novel method harnesses the complementary strengths of cutting-edge quantum processors and classical supercomputers to tackle one of the most formidable challenges in quantum chemistry: accurately determining the electronic energy levels of a complex molecule. Their work not only marks a watershed moment for computational methods in chemistry but also holds transformative implications for materials science, nanotechnology, and the development of novel pharmaceuticals, where understanding the electronic nature of substances underpins their functionality.
The core achievement of this interdisciplinary team lies in their innovative use of quantum-centric supercomputing, a hybrid framework that marries high-performance classical computation with the growing capabilities of quantum algorithms. Professor Sharma articulates the significance of this fusion, emphasizing that classical algorithms running on traditional supercomputers have been combined with quantum algorithms executed on IBM’s Heron quantum processor. This synergy has enabled obtaining meaningful chemical insights that were previously beyond reach. The novelty lies in the capacity of quantum algorithms to rigorously pinpoint the most pivotal components within a vast computational matrix, a feat where classical heuristics have historically fallen short.
Central to their study is the exploration of the [4Fe-4S] molecular cluster, a complex iron–sulfur system foundational to a myriad of biological processes. This molecular assembly’s electron configuration is notoriously challenging to analyze due to the combinatorial explosion of quantum states. The cluster’s role in key enzymatic reactions, such as nitrogen fixation facilitated by nitrogenase enzymes, underscores the importance of precise quantum chemical modeling. Nitrogen fixation is the biochemical process converting nitrogen gas into ammonia, a reaction essential for plant growth and global agriculture. The ability to model such a system with ultra-high accuracy bears profound scientific and practical significance.
At the heart of quantum chemical computations is the endeavor to find the ground state of a molecular system—the lowest energy state that governs chemical properties such as reactivity, stability, and catalytic behavior. This ground state is described mathematically via a wave function, a complex probabilistic description of electron positions and energies. The wave function is derived by solving the Schrödinger equation, a formidable quantum-mechanical equation whose solution scales exponentially with increasing electron count, rapidly overwhelming classical computational resources. Previous classical methods often resort to approximations or heuristics to tame this exponential complexity, but such shortcuts may omit critical details that define the system’s true behavior.
The quantum algorithmic approach devised by this team cleverly circumvents these limitations by employing quantum processors to identify the elements of the Hamiltonian matrix—an enormous matrix representing the energy interactions in the system—that most significantly affect the wave function. Classically, this matrix grows exponentially large, making direct diagonalization computationally untenable for systems of biological relevance. The quantum processor effectively acts as a filter, supplanting classical heuristics with a rigorous quantum method that maps out dominant contributions within the Hamiltonian, ensuring that subsequent calculations remain manageable without sacrificing accuracy.
After the quantum processor’s selection of the important Hamiltonian components, the reduced matrix is handed off to one of the world’s most powerful classical supercomputers, RIKEN’s Fugaku in Japan, to perform precise computations. This division of labor exemplifies a seamless quantum-classical hybrid strategy: quantum devices reduce the problem size by identifying key matrix components, while classical supercomputing infrastructure carries out the intensive numerical diagonalization. Leveraging up to 77 qubits—a notably high number compared to previous chemical quantum computing experiments—this methodology pushes the scale of quantum computation in chemistry well beyond earlier attempts, edging closer to the era when quantum advantage can be declared unambiguously.
While the current results are not yet definitive proof that quantum algorithms surpass classical algorithms across the board for such molecular systems, the research constitutes a significant leap forward. It represents progress beyond precedents set in the past, demonstrating the feasibility of quantum-centric supercomputing for real chemical problems previously considered out of reach. The team’s work illustrates a tangible pathway for future quantum hardware and algorithms to eventually eclipse classical methods in both efficiency and accuracy, a milestone eagerly anticipated by scientists across disciplines.
Fundamentally, the research reveals how quantum computing can enrich classical computational chemistry rather than wholly replace it. Classical methods offer high precision but struggle with scalability, while quantum computers have shown great promise in handling large, complex linear algebra problems intrinsic to quantum systems. The quantum-classical hybrid model acknowledges the strengths of each platform and synergistically combines them, opening avenues to solve chemically and biologically significant problems previously unattainable by either method alone.
The methodological innovation also extends to quantum algorithm design itself. Replacing classical heuristics—which are often ad hoc and potentially error-prone—with quantum algorithms introduces a rigorous, mathematically principled way of pruning computational complexity. This work sets a new standard, validating the concept that quantum processes can guide and enhance classical calculations by identifying the core variables that matter most to physical phenomena in molecular systems.
Importantly, this breakthrough is supported by a collaboration of globally renowned institutions including Caltech, IBM, and RIKEN, highlighting the interdisciplinary and international effort driving quantum technology forward. The combined expertise of professors, quantum algorithm developers, and computational scientists from these institutions has been imperative for tackling the multi-faceted challenges inherent to this project—from hardware engineering and software development to in-depth quantum chemical theory.
Published in the prestigious journal Science Advances, the paper entitled "Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer" is featured prominently on the cover. Its detailed findings have received acclaim for not only addressing a long-standing challenge in quantum chemistry but also for showcasing how quantum computing can pragmatically integrate with existing classical supercomputers to unlock new frontiers of scientific discovery.
This research holds considerable implications beyond the immediate application to iron–sulfur clusters. Its quantum-centric supercomputing paradigm may accelerate progress across fields relying on precise energy level calculations—from the rational design of catalysts and advanced materials to quantum-aware drug development platforms. As quantum hardware matures and algorithms improve, such hybrid computational strategies promise to revolutionize how complex molecular systems are studied and understood.
To summarize, this pioneering effort marries 21st-century quantum technologies with classical computational might to illuminate the quantum mechanics of biologically essential molecules. The successful modeling of the [4Fe-4S] cluster exemplifies the transformative scientific possibilities unlocked when quantum processors and classical supercomputers work hand in hand, charting a promising path toward profound advancements in chemistry and the broader physical sciences.
Subject of Research: Quantum computing application in computational chemistry for modeling complex iron–sulfur molecular clusters.
Article Title: Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer
News Publication Date: 18-Jun-2025
Web References:
https://www.science.org/doi/10.1126/sciadv.adu9991
References:
Sharma, S., Robledo-Moreno, J., Motta, M., Mezzacapo, A., et al. (2025). Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer. Science Advances. DOI: 10.1126/sciadv.adu9991
Keywords: Computational chemistry, quantum computing, quantum processors, qubits, algorithms, quantum-centric supercomputing, iron–sulfur clusters, nitrogen fixation, Schrödinger equation, Hamiltonian matrix, hybrid computing, materials science
