Distributed Quantum-Centric Supercomputing with Multi-Layered Generative Learning for Intermolecular Interactions
Abstract
Quantum-centric supercomputing (QCSC) approaches, such as sample-based quantum diagonalization (SQD), combine quantum processors with high-performance computers (HPC), offering a promising route toward solving challenging electronic structure problems. However, implementation of QCSC approaches to large active space calculations is constrained by the associated number of qubits, deep quantum circuits, sampling cost, and large-scale Hamiltonian diagonalization. To overcome these limitations, we introduce a distributed QCSC framework that decomposes complex molecular problems into parallel sub-problems requiring fewer qubits, shallower circuits, and reduced shot budgets. A physically motivated, two-step aggregation scheme then reconstructs the global solution: first, a computational bit assignment strategy optimizes a cost function to appropriately map local bit indices to global ones, yielding a mapped tensor product state (MTPS); second, an inter-fragment correlation recovery module restores missing cross-fragment interactions. Herein, we employ a multi-layered generative machine learning architecture, starting with utilizing the recently proposed physics-informed generative SQD (PIGen-SQD) as a distributed QCSC solver, followed by a secondary generative module to model configuration distributions and identify dominant sectors of the global Hilbert space. For water and formamide dimers, our method achieves up to 95% reduction in the diagonalization dimension while enhancing energy accuracy by an order of magnitude over conventional SQD. Furthermore, application to the water hexamer ((H2O)6) cage successfully decomposes a 120-qubit problem into six 20-qubit monomeric subsystems, and stitches the monomers under a dimeric approximation, demonstrating excellent agreement with the reference classical methods.
Related
- Influence of Small Changes in Intermolecular Distances on Results of Interaction Energy Decomposition for Stacking Interactions, Hydrogen Bonds, and Antiparallel Interactions
- Symmetry-adapted perturbation theory for cavity-modified intermolecular interactions: QED-SAPT0 theory and implementation
- A benchmark dataset of 86 intermolecular interactions of neutrals, ions, radicals and radical ions with water and hydrogen sulfide
- Magnetic exchange interactions in the molecular orbital spin system NaV2O5 from a distributed moment point of view
- Reducing quantum and classical resources for quantum-centric supercomputing workloads on near-term hardware
BibTeX
@misc{patra2026distributed,
title = {{Distributed Quantum-Centric Supercomputing with Multi-Layered Generative Learning for Intermolecular Interactions}},
author = {Chayan Patra and Prayas Ghosh and Adwait Patkhedkar and Rahul Maitra},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.32968},
eprint = {2610.32968},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.32968},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Distributed Quantum-Centric Supercomputing with Multi-Layered Generative Learning for Intermolecular Interactions
AU - Chayan Patra
AU - Prayas Ghosh
AU - Adwait Patkhedkar
AU - Rahul Maitra
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.32968
UR - https://arxiv.org/abs/2610.32968
AB - Quantum-centric supercomputing (QCSC) approaches, such as sample-based quantum diagonalization (SQD), combine quantum processors with high-performance computers (HPC), offering a promising route toward solving challenging electronic structure problems. However, implementation of QCSC approaches to large active space calculations is constrained by the associated number of qubits, deep quantum circuits, sampling cost, and large-scale Hamiltonian diagonalization. To overcome these limitations, we introduce a distributed QCSC framework that decomposes complex molecular problems into parallel sub-problems requiring fewer qubits, shallower circuits, and reduced shot budgets. A physically motivated, two-step aggregation scheme then reconstructs the global solution: first, a computational bit assignment strategy optimizes a cost function to appropriately map local bit indices to global ones, yielding a mapped tensor product state (MTPS); second, an inter-fragment correlation recovery module restores missing cross-fragment interactions. Herein, we employ a multi-layered generative machine learning architecture, starting with utilizing the recently proposed physics-informed generative SQD (PIGen-SQD) as a distributed QCSC solver, followed by a secondary generative module to model configuration distributions and identify dominant sectors of the global Hilbert space. For water and formamide dimers, our method achieves up to 95% reduction in the diagonalization dimension while enhancing energy accuracy by an order of magnitude over conventional SQD. Furthermore, application to the water hexamer ((H_2O)_6) cage successfully decomposes a 120-qubit problem into six 20-qubit monomeric subsystems, and stitches the monomers under a dimeric approximation, demonstrating excellent agreement with the reference classical methods.
ER -