Quantum computers must operate in concert with classical computers to deliver on the promise of quantum advantage for practical problems. To achieve that, it is important to understand how quantum and classical computing can interact together, and how one can characterize the scalability and efficiency of hybrid quantum–classical workflows. So far, early experiments with quantum-centric supercomputing workflows have been limited in scale and complexity. Here, we use a Heron quantum processor deployed on premises with the entire supercomputer Fugaku to perform the largest computation of electronic structure involving quantum and classical high-performance computing. We design a closed-loop workflow between the quantum processors and 152,064 classical nodes of Fugaku, to approximate the electronic structure of chemistry models beyond the reach of exact diagonalization, with accuracy comparable to some all-classical approximation methods. Our work pushes the limits of the integration of quantum and classical high-performance computing, showcasing computational resource orchestration at the largest scale possible for current classical supercomputers.
Quantum computers must operate in concert with classical computers to deliver on the promise of quantum advantage for practical problems. To achieve that, it is important to understand how quantum and classical computing can interact together, and how one can characterize the scalability and efficiency of hybrid quantum–classical workflows. So far, early experiments with quantum-centric supercomputing workflows have been limited in scale and complexity. Here, we use a Heron quantum processor deployed on premises with the entire supercomputer Fugaku to perform the largest computation of electronic structure involving quantum and classical high-performance computing. We design a closed-loop workflow between the quantum processors and 152,064 classical nodes of Fugaku, to approximate the electronic structure of chemistry models beyond the reach of exact diagonalization, with accuracy comparable to some all-classical approximation methods. Our work pushes the limits of the integration of quantum and classical high-performance computing, showcasing computational resource orchestration at the largest scale possible for current classical supercomputers.
A universal quantum computer can simulate diverse quantum systems, with electronic structure for chemistry offering challenging problems for practical use cases around the hundred-qubit mark. Although current quantum processors have reached this size, deep circuits and a large number of measurements lead to prohibitive runtimes for quantum computers in isolation. Here, we demonstrate the use of classical distributed computing to offload all but an intrinsically quantum component of a workflow for electronic structure simulations. Using a Heron superconducting processor and the supercomputer Fugaku, we simulate the ground-state dissociation of N 2 and the ground state properties of [2Fe-2S] and [4Fe-4S] clusters, with circuits up to 77 qubits and 10,570 gates. The proposed algorithm processes quantum samples to produce upper bounds for the ground-state energy and sparse approximations to the ground-state wave functions. Our results suggest that, for current error rates, a quantum-centric supercomputing architecture can tackle challenging chemistry problems beyond sizes amenable to exact diagonalization.
We develop a workflow decomposing a molecule into fragments via quantum embedding and simulating them with a heterogeneous quantum-classical (HQC) method. We sample fragment electronic configurations on two 156-qubit quantum processors (ibm_cleveland, ibm_kobe), using up to 94 qubits, running 21,006 circuits for over 239 hours, collecting 3.0⋅109 measurement outcomes -- the most resource-intensive HQC computation for quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on supercomputers Fugaku, Miyabi-G, and ROQUO, achieving 72.5% parallel efficiency with scalable distributed linear algebra kernels. We simulate two protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608, 12,635 atoms), demonstrate >40× increase in system size and up to 210× improvement in accuracy over previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies. We present the first HQC protein-ligand binding prediction using a mixed-basis set and an automated end-to-end workflow enabling practical HQC calculations of large protein systems.
Quantum computers have demonstrated utility in simulating quantum systems beyond brute-force classical approaches. As the community builds on these demonstrations to explore using quantum computing for applied research, algorithms and workflows have emerged that require leveraging both quantum computers and classical high-performance computing (HPC) systems to scale applications, especially in chemistry and materials, beyond what either system can simulate alone. Today, these disparate systems operate in isolation, forcing users to manually orchestrate workloads, coordinate job scheduling, and transfer data between systems -- a cumbersome process that hinders productivity and severely limits rapid algorithmic exploration. These challenges motivate the need for flexible and high-performance Quantum-Centric Supercomputing (QCSC) systems that integrate Quantum Processing Units (QPUs), Graphics Processing Units (GPUs), and Central Processing Units (CPUs) to accelerate discovery of such algorithms across applications. These systems will be co-designed across quantum and classical HPC infrastructure, middleware, and application layers to accelerate the adoption of quantum computing for solving critical computational problems. We envision QCSC evolution through three distinct phases: (1) quantum systems as specialized compute offload engines within existing HPC complexes; (2) heterogeneous quantum and classical HPC systems coupled through advanced middleware, enabling seamless execution of hybrid quantum-classical algorithms; and (3) fully co-designed heterogeneous quantum-HPC systems for hybrid computational workflows. This article presents a reference architecture and roadmap for these QCSC systems.
Efficient simulation of strongly-interacting fermionic systems on quantum processing units (QPUs) is a challenging task due to nonlocal mode entanglement generation. However, it is not yet well understood how the structure of entanglement governs the hardness of large-scale quantum chemistry simulations or the scaling of distributing such workloads. Here, we introduce an algorithm for estimating the distribution complexity of hybrid quantum-classical simulation for electronic structure Hamiltonians over heterogeneous high-performance architectures. Our algorithm relies on efficient analytical evaluation of the low entanglement boundaries for the orbital rotations and dephasing-induced localization within tensor fragments, in a double-factorized representation. Our entanglement estimation scales as O(N3) for each fragment, where N is the number of orbitals. When QPUs are communicating via a quantum network, the cost of distribution per fragment is reduced quadratically from O(N2) to O(N). Similarly, for hybrid quantum-classical approaches, with access to only conventional HPC interconnects, the worst-case cost is reduced from O(exp(N2)) to O(exp(N)). We show that emergent entanglement patterns are induced by the interplay between coherent Gaussian orbital rotations and disordered Coulomb interactions. We discuss the underlying physical mechanisms that govern distribution complexity and introduce model systems that are tunable based on the localizability of fragments and the overlap of interfragment rotations. We characterize three different regimes of hardness for distribution complexity and classical simulability. The framework introduced here enables novel and more efficient quantum-classical application workflows towards utility-scale quantum computing.
We describe the Quantum-HPC hybrid system comprising the supercomputer Fugaku, the QPUs Quantinuum Reimei and ibm_kobe, and the Tierkreis hybrid workflow software. We present two demonstrations of the system at work. In the first, we compute the excited states of a biomolecular system using Fugaku and Reimei (simulator) to handle classical and quantum parts of the workflow respectively. In the second, Fugaku coordinates Reimei and ibm_kobe working together in a combined VQE + QPE workflow to compute the ground state energy of a simple system.
A study published on the preprint repository arXiv expanded the existing system scale by more than 40 times by combining quantum embedding technology, which decomposes a molecule into multiple fragments, with a heterogeneous quantum-classical (HQC) computing method. Utilizing two 156-qubit quantum processors (ibm_cleveland, ibm_kobe) and two supercomputers (Fugaku, Miyabi-G), the research successfully simulated the quantum chemistry of protein-ligand complexes consisting of more than 12,000 atoms. The HQC method matched the accuracy level of the classical coupled-cluster (CCSD) method in fragment energies, proving its practical, industrial-scale potential to drastically reduce the time and cost of the drug discovery pipeline. [Quantum Biology Society] Predicting the interaction between a target protein and a drug candidate is crucial in drug discovery, but simulating its fundamental quantum mechanical structure could previously only be applied to extremely small-scale systems due to limitations in computational scale. However, a recent study successfully and accurately simulated complex protein-ligand complexes consisting of over 12,000 atoms through "heterogeneous quantum-classical (HQC) computing," which combines quantum computers with classical supercomputers, drawing the attention of the global pharmaceutical and IT industries. The study, "Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes," published on the preprint repository arXiv, was conducted by a multinational joint research team. This included Kenneth M. Merz, Jr.'s research team at the Cleveland Clinic Foundation in the U.S., Yuichi Otsuka and Tomonori Shirakawa's research teams at RIKEN in Japan, and researchers from IBM Quantum. ■ Splitting Molecules with 'Quantum Embedding' and Combining them with 'Supercomputers' The core of this massive calculation is "quantum embedding" technology. Instead of calculating the entire giant protein molecule at once, the method decomposes the molecule into multiple smaller fragments and simulates each independently. The research team executed 9,200 quantum circuits for over 100 hours, utilizing up to 94 qubits across two 156-qubit quantum processors (ibm_cleveland, ibm_kobe), and collected 1.3×10⁹ measurement outcomes. This is the most resource-intensive HQC computation performed in the field of quantum chemistry to date. Simultaneously, they mobilized Japan's world-class supercomputers, Fugaku and Miyabi-G, to perform subspace diagonalization via scalable distributed linear algebra kernels, achieving a high parallel efficiency of 72.5%. ■ System Size Expanded by 40x, Accuracy Improved by up to 210x Through this innovative workflow, the research team successfully simulated two giant protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (comprising 11,608 and 12,635 atoms, respectively). As a result, compared to previous state-of-the-art technologies, they achieved the feat of expanding the size of the simulatable system by more than 40 times and improving computational accuracy by up to 210 times. In particular, it is highly encouraging that the HQC method reached the high accuracy of the classical coupled-cluster (CCSD) level in fragment energy calculations. This achievement establishes a scalable pathway for systematically improvable biomolecular simulations, demonstrating the potential to drastically reduce the time and massive costs of the drug discovery pipeline through accurate characterization of drug-protein complexes. Experts evaluate this study as a crucial milestone showing that the technology to explore small-molecule hit substances for target proteins using actual quantum computers can be implemented on a real industrial scale, moving well beyond the laboratory level. https://arxiv.org/abs/2605.01138v1