Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations

• URL: http://arxiv.org/abs/2606.30551v1
• Date: Mon, 29 Jun 2026 16:48:44 GMT
• Title: Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations
• Abstract summary: This report presents a hybrid quantum-classical workflow performed on the Fujitsu FX700.<n>It addresses two structural inefficiencies in quantum-based diagonalization.<n>It enables more efficient and economical drug discovery simulations for the industry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit depths. This report presents a hybrid quantum-classical workflow performed on the Fujitsu FX700 ideal state-vector simulator using QARP that addresses two structural inefficiencies in quantum-sampling-based diagonalization workflows. First, we integrate the Linear Scaling CNOT UCCSD (LCNot-UCCSD) ansatz into the QSCI framework, replacing the $\mathcal{O}(N^6)$ CCSD parameter initialization of the competing LUCJ ansatz approach with $\mathcal{O}(N^4)$ MP2-amplitude initialization. Second, we introduce QSCI-RBM, a variant that replaces the configuration recovery of the SQD framework with a Restricted Boltzmann Machine (RBM) acting as a compact generative subspace expansion model. Both are evaluated on eight different molecules in STO-3G across 14 controlled artificial error levels with 100 independent runs each, validated on potential energy surface scans of the N$_2$ molecule in cc-pVDZ, and embedded within DMET to treat the FDA-approved antiviral Amantadine (C$_{10}$H$_{17}$N, 11 DMET fragments) and the active region of the SARS-CoV-2 main protease complexed with its covalent inhibitor Carmofur (PDB: 7BUY, C$_{15}$H$_{28}$N$_4$O$_5$S, 10 fragments). To our knowledge, this is the first deployment of LCNot-UCCSD within QSCI on a quantum computing simulator, and the first DMET-QSCI(LCNot-UCCSD)-RBM application to an industry-relevant protein-ligand system. By utilizing a fraction of the classical computing resources required by the current state-of-the-art work by Cleveland Clinic, RIKEN, and IBM Quantum, this approach enables more efficient and economical drug discovery simulations for the industry.

Related papers

• An Analytically Trained Variational Surrogate for Quantum Phase Estimation on NISQ Hardware [0.3908906925636173]
  Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct hardware execution impractical on Noisy Intermediate-Scale Quantum (NISQ) devices.<n>We present an analytically grounded variational surrogate framework in which a shallow Variational Quantum Circuit (VQC) is trained to reproduce the QPE measurement distribution without any quantum circuit simulation.
  arXiv  Detail & Related papers  (2026-07-23T05:52:17Z)
• $\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning [39.146761527401424]
  We present a Quantum Multiple Kernel Learning framework, dubbed NextGen $mathttQMKL$.<n>By encoding molecular descriptors into large Hilbert spaces, our approach substantially enhances non-linear modeling.<n>As quantum computing matures, this framework paves the way for autonomous drug discovery and cognitive architectures.
  arXiv  Detail & Related papers  (2026-07-13T15:33:05Z)
• Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes [2.0403796650328854]
  Ab initio wavefunction methods provide accurate molecular simulations but their computational scaling restricts applications to small systems.<n>We develop a workflow combining quantum embedding to decompose a molecule into fragments with a heterogeneous quantum-classical (HQC) method to simulate fragments.
  arXiv  Detail & Related papers  (2026-05-01T22:30:56Z)
• Towards Chemically Accurate and Scalable Quantum Simulations on IQM Quantum Hardware: A Quantum-HPC Hybrid Approach [0.37475012732344193]
  We present a large-scale experimental study of quantum-computing-based molecular simulation carried out on IQM's Sirius 24-qubit superconducting processor.<n>The work employs Sample-based Quantum Diagonalization (SQD) together with the Local Unitary Cluster Jastrow (LUCJ) ansatz to estimate ground-state energies.<n>To move beyond small benchmark systems, we combine SQD(LUCJ) with Density Matrix Embedding Theory (DMET) to compute active-space energies.
  arXiv  Detail & Related papers  (2026-04-02T12:43:43Z)
• Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework [0.0]
  This work combines Density Matrix Embedding Theory (DMET) with Sample-based Quantum Diagonalization (SQD) to compute ground-state energies of a set of natural ligand-like molecules.<n>The resulting embedded Hamiltonians are solved on IBM's Eagle R3 superconducting quantum hardware.<n>Results demonstrate that sample-based quantum methods, when integrated with a robust embedding framework, can reliably extend quantum computation towards simulation of chemically relevant molecular systems.
  arXiv  Detail & Related papers  (2025-11-27T06:50:52Z)
• FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report [36.1999598554273]
  This report investigates and compares the efficacy of classical, quantum, and quantum-hybrid machine learning models for the binary behavioural classification of fraudulent financial activities.<n>We implement and evaluate a range of models on the IBM Anti-Money Laundering (AML) dataset.<n>We propose Fraud Detection for Quantum Computing (FD4QC), a practical, API-driven system architecture designed for real-world deployment.
  arXiv  Detail & Related papers  (2025-07-25T16:08:22Z)
• Coupled cluster method tailored by quantum selected configuration interaction [0.0]
  We present a hybrid quantum-classical scheme that tailors coupled-cluster (CC) theory with a quantum-selected configuration interaction (QSCI) wave function.<n>QSCI provides a scalable, shot-efficient approach to reconstructing the many-electron state prepared on quantum hardware.
  arXiv  Detail & Related papers  (2025-06-20T11:07:26Z)
• Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery [39.58317527488534]
  This research demonstrates the successful application of a Quantum Multiple Kernel Learning framework to enhance QSAR classification.<n>We apply this methodology to a dataset for identifying DYRK1A kinase inhibitors.<n>By benchmarking the QMKL-SVM against a classical Gradient Boosting model, we show that the quantum-enhanced approach achieves a superior AUC score.
  arXiv  Detail & Related papers  (2025-06-17T19:00:47Z)
• Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers [37.69303106863453]
  We propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers. Our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average.
  arXiv  Detail & Related papers  (2024-06-17T14:10:10Z)
• Evaluating Ground State Energies of Chemical Systems with Low-Depth Quantum Circuits and High Accuracy [6.81054341190257]
  We develop an enhanced Variational Quantum Eigensolver (VQE) ansatz based on the Qubit Coupled Cluster (QCC) approach. We evaluate our enhanced QCC ansatz on two distinct quantum hardware, IBM Kolkata and Quantinuum H1-1.
  arXiv  Detail & Related papers  (2024-02-21T17:45:03Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Preparation of excited states for nuclear dynamics on a quantum computer [117.44028458220427]
  We study two different methods to prepare excited states on a quantum computer. We benchmark these techniques on emulated and real quantum devices. These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
  arXiv  Detail & Related papers  (2020-09-28T17:21:25Z)

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.