Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems

• URL: http://arxiv.org/abs/2608.12884v1
• Date: Thu, 13 Aug 2026 06:57:25 GMT
• Title: Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems
• Abstract summary: This work presents a hybrid DFT-Quantum Embedding framework integrating classical HPC-based DFT with a quantum electronic-structure solver.<n>We focus on noiseless quantum simulation to systematically evaluate accuracy, convergence, active-space dependence, computational cost, and HPC scalability without hardware noise.
• Score: 0.9368753183086049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Efficient Quantum Simulation of Non-Adiabatic Molecular Dynamics with Precise Electronic Structure [6.554055153293176]
  We introduce a quantum-adapted extension to the Landau-Zener-Surface-Hopping NAMD.<n>We also develop a sub-microhartree-accurate PES calculation protocol.<n>This work paves the way for practical application of quantum computing in NAMD.
  arXiv  Detail & Related papers  (2025-12-02T03:34:00Z)
• Molecular resonance identification in complex absorbing potentials via integrated quantum computing and high-throughput computing [36.94429692322632]
  We develop an algorithm that exploits the complex absorbing potential formalism to distill the problem of molecular resonance identification into a network of hybrid quantum-classical variational quantum eigensolver tasks.<n>We show qDRIVE successfully identifies resonance energies and wavefunctions in simulated quantum processors with current and planned specifications.
  arXiv  Detail & Related papers  (2025-11-20T02:28:05Z)
• Qumode-Based Variational Quantum Eigensolver for Molecular Excited States [43.148034499498586]
  We introduce the Qumode Subspace Variational Quantum Eigensolver (QSS-VQE), a hybrid quantum-classical algorithm for computing molecular excited states.<n>We demonstrate the performance of QSS-VQE through simulations of molecular excited states, including dihydrogen and a conical intersection in cytosine.
  arXiv  Detail & Related papers  (2025-09-05T00:53:51Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Training Hybrid Deep Quantum Neural Network for Efficient Reinforcement Learning [3.753031740069576]
  Quantum circuits embed data in a Hilbert space whose dimensionality grows exponentially with the number of qubits.<n>We introduce qtDNN, a tangential surrogate that locally approximates a quantum circuit.<n>We design hDQNN-TD3, a hybrid deep quantum neural network for continuous-control reinforcement learning.
  arXiv  Detail & Related papers  (2025-03-12T07:12:02Z)
• Efficient charge-preserving excited state preparation with variational quantum algorithms [33.03471460050495]
  We introduce a charge-preserving VQD (CPVQD) algorithm, designed to incorporate symmetry and the corresponding conserved charge into the VQD framework. Results show applications in high-energy physics, nuclear physics, and quantum chemistry.
  arXiv  Detail & Related papers  (2024-10-18T10:30:14Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• Multi-GPU-Enabled Hybrid Quantum-Classical Workflow in Quantum-HPC Middleware: Applications in Quantum Simulations [1.9922905420195367]
  This study introduces an innovative distribution-aware Quantum-Classical-Quantum architecture. It integrates cutting-edge quantum software framework works with high-performance classical computing resources. It addresses challenges in quantum simulation for materials and condensed matter physics.
  arXiv  Detail & Related papers  (2024-03-09T07:38:45Z)
• Quantum Embedding Method for the Simulation of Strongly Correlated Systems on Quantum Computers [0.0]
  We introduce the projection-based embedding method for combining the variational quantum eigensolver (VQE) algorithm 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 developments will benefit many different chemical areas including the computer aided drug design as well as the study of metalloenzymes with a strongly correlated fragment.
  arXiv  Detail & Related papers  (2023-02-06T19:00:03Z)
• Exploring the scaling limitations of the variational quantum eigensolver with the bond dissociation of hydride diatomic molecules [0.0]
  Materials simulations involving strongly correlated electrons pose fundamental challenges to state-of-the-art electronic structure methods. No quantum computer has simulated a molecule of a size and complexity relevant to real-world applications, despite the fact that the variational quantum eigensolver algorithm can predict chemically accurate total energies. We show that the inclusion of d-orbitals and the use of the UCCSD ansatz, which are both necessary to capture the correct TiH physics, dramatically increase the cost of this problem.
  arXiv  Detail & Related papers  (2022-08-15T19:21:17Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• Estimating Phosphorescent Emission Energies in Ir(III) Complexes using Large-Scale Quantum Computing Simulations [0.0]
  We apply the iterative qubit coupled cluster (iQCC) method on classical hardware to the calculation of the transition energies in nine phosphorescent iridium complexes. Our simulations would require a gate-based quantum computer with a minimum of 72 fully-connected and error-corrected logical qubits. The iQCC quantum method is found to match the accuracy of the fine-tuned DFT functionals, has a better Pearson correlation coefficient, and still has considerable potential for systematic improvement.
  arXiv  Detail & Related papers  (2021-11-07T20:02:10Z)
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale [57.84751206630535]
  We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators. By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
  arXiv  Detail & Related papers  (2021-04-21T13:26:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.