We present a multiscale workflow that couples quantum computing algorithms for quantum chemical calculations with conventional High-Performance Computing (HPC) software. The resulting embedding framework, implemented in the multiscale computational chemistry environment ChemShell, combines a generic N-layer subtractive Quantum Mechanics/Molecular Mechanics (QM/MM) scheme and active orbital space embedding for the quantum computing region. We have benchmarked the embedding models for gas phase and solvated molecules to understand the performance, benefits and current limitations of embedding quantum computing algorithms within a QM/MM model. We compare the 2 performance of simulated Quantum Phase Estimation (QPE) calculations on an HPC cluster using Graphical Processing Units (GPUs) and Central Processing Units (CPUs). Using GPUs to simulate Quantum Phase Estimation requires only 16% of the runtime when compared to CPU.
We present a multiscale workflow that couples quantum computing algorithms for quantum chemical calculations with conventional High-Performance Computing (HPC) software. The resulting embedding framework, implemented in the multiscale computational chemistry environment ChemShell, combines a generic N-layer subtractive Quantum Mechanics/Molecular Mechanics (QM/MM) scheme and active orbital space embedding for the quantum computing region. We have benchmarked the embedding models for gas phase and solvated molecules to understand the performance, benefits and current limitations of embedding quantum computing algorithms within a QM/MM model. We compare the 2 performance of simulated Quantum Phase Estimation (QPE) calculations on an HPC cluster using Graphical Processing Units (GPUs) and Central Processing Units (CPUs). Using GPUs to simulate Quantum Phase Estimation requires only 16% of the runtime when compared to CPU.
We present a multiscale workflow for coupling quantum computing algorithms for quantum chemical calculations with conventional High-Performance Computing (HPC) software. The resulting embedding framework, implemented in the multiscale computational chemistry environment ChemShell, combines a generic N-layer subtractive Quantum Mechanics/Molecular Mechanics (QM/MM) scheme and selection of an active orbital space for the quantum algorithm region. We have benchmarked the embedding models for gas phase and solvated molecules to understand the performance, benefits and current limitations of embedding quantum computing algorithms within a QM/MM model. We compare the performance of simulated Quantum Phase Estimation (QPE) calculations on an HPC cluster using Graphical Processing Units (GPUs) and Central Processing Units (CPUs).
We present a multiscale workflow for coupling quantum computing algorithms for quantum chemical calculations with conventional high-performance computing (HPC) software. The resulting embedding framework, implemented in the multiscale computational chemistry environment ChemShell, combines a generic N-layer subtractive quantum mechanics/molecular mechanics (QM/MM) scheme and selection of an active orbital space for the quantum algorithm region. We have benchmarked the embedding models for gas phase and solvated molecules to understand the performance, benefits and current limitations of embedding quantum computing algorithms within a QM/MM model. We compare the performance of simulated quantum phase estimation (QPE) calculations on an HPC cluster using graphical processing units (GPUs) and central processing units (CPUs).
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 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.
We present a prototype implementation of the Quantum-Selected Configuration Interaction (QSCI) method on a heterogeneous platform that integrates quantum simulation with high-performance computing (HPC). In this workflow, a GPU-based simulator emulates a quantum processor to prepare and sample reference states via the variational quantum eigensolver (VQE), while CPU nodes perform optimization and large-scale subspace diagonalization using EigenExa. This design enables seamless coordination between quantum state preparation and numerical linear algebra within HPC environments. The framework efficiently handles subspace dimensions up to 100,000 and exhibits favorable scaling with system size. Benchmark experiments on hydrogen chains demonstrate that relatively large systems can be processed within practical runtimes, highlighting the potential of hybrid HPC-QC integration for scalable quantum chemistry.
We present a computational platform for modeling chemical reactions in complex molecular environments, focused on ligand-protein binding in drug discovery. The platform implements our new quantum-in-quantum-in-classical (QM/QM/MM) multiscale embedding model that integrates molecular dynamics with a quantum-information-enhanced density matrix embedding theory and quantum chemistry solvers, including explicit solvent. Quantum-information metrics are utilized to generate entanglement-consistent orbitals, enabling a high-accuracy description of strongly correlated regions. The framework supports multiple computational backends, including multi-CPU, NVIDIA multi-GPU architectures, and quantum hardware (IQM, IonQ, IBM) integrated under CUDA-Q, and is designed for compatibility with future fault-tolerant quantum systems. The new platform's capabilities are demonstrated by modeling covalent docking of zanubrutinib to Bruton's tyrosine kinase via a Michael addition mechanism, computing the full reaction energy profiles and energy barriers at a reduced computational cost relative to existing methods. As a 2nd-generation anticancer agent, zanubrutinib serves as a proof of concept for covalent inhibitor discovery. Accurate first-principles reaction barrier estimations provided by our method can contribute to reducing false positive and negative rates in drug discovery pipelines. Scalability is validated through benchmarks on GPU clusters, cloud-based CPU infrastructures. We demonstrate integration with quantum devices (up to 20 qubits), alongside resource estimates for fault-tolerant quantum computing, indicating potential speedups of up to 20x. Beyond single reactions, the platform supports the construction of reaction networks in chemical metric space, facilitating ligand screening and systematic exploration of reactive pathways.