Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations
arXiv:2606.30551v1 Announce Type: cross 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
The continuous advancements in quantum computing hardware (NISQ era) and machine learning naturally lead to explorations of hybrid approaches to tackle computationally intensive scientific problems like molecular simulations.
Accurate molecular simulations are critical for drug discovery and materials science, and this approach suggests a pathway to overcome current hardware limitations, accelerating advancements in these fields.
This research introduces concrete strategies to mitigate inefficiencies in quantum-sampling-based diagonalization workflows for molecular simulations, potentially making quantum chemistry more viable on current and near-future quantum hardware.
- · Quantum computing hardware developers
- · Pharmaceutical companies
- · Materials science researchers
- · AI/ML in scientific computing
- · Classical high-performance computing (in specific niches)
- · Companies reliant solely on traditional drug discovery methods
Improved accuracy and efficiency in quantum chemistry calculations for complex molecular systems.
Faster discovery of new drugs and advanced materials due to enhanced simulation capabilities.
A potential shift in R&D paradigms across multiple high-tech industries, leveraging quantum-AI hybrid approaches for intractable problems.
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Read at arXiv cs.LG