Quantum Autoencoder-Driven Hamiltonian Reduction for VQE Applications
We propose a quantum machine learning-based method to compress molecular Hamiltonians and efficiently compute ground-state energies (GSEs) using the variational quantum eigensolver (VQE), significantly reducing resource requirements.
Authors
- M R Nirmal
- M Girish Chandra (ORCID: https://orcid.org/0000-0002-1479-0504)
- Ankit Khandelwal (ORCID: https://orcid.org/0000-0003-0020-6546)
Institutions
- Tata Consultancy Services (India) (IN)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/quantum.2025.qtu3a.22
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00