Sample-based quantum simulation of vibrational structure of polyyne chains
quant-ph
Submitted: 2026-10-07
Updated: 2026-10-07
Comments: 19 pages, 9 figures
License: http://creativecommons.org/licenses/by/4.0/
The gist: Predicting anharmonic vibrational spectra is a complex computational task, which becomes quickly infeasible as molecular size and the number of vibrational basis functions increase, leading to an
Abstract
Predicting anharmonic vibrational spectra is a complex computational task, which becomes quickly infeasible as molecular size and the number of vibrational basis functions increase, leading to an exponential growth of the underlying Hilbert space. Here, we employ a sample-based quantum diagonalization (SQD) framework to compute anharmonic vibrational energies and spectra. The approach combines quantum state preparation and sampling with configuration recovery, diagonalization of the Hamiltonian projected in a subspace of basis configurations, and calculation of transition-dipole moments to obtain both vibrational energies and infrared spectra. We investigate two approaches for sampling, namely, vib-LUCJ and vib-SqDRIFT and apply the method to study the polyyne molecules C 2 H 2 and C 4 H 2, reaching representations requiring up to 104 qubits. Beyond energies, transition-dipole calculations reveal that infrared intensities and excited-state composition continue to evolve with basis size through anharmonic mixing and intensity redistribution, even when low-energy eigenvalues appear converged. These results show that sample-based quantum subspace methods can capture spectroscopic information in vibrational spaces far beyond the regime of full diagonalization. Moreover, our simulation identify multi-state recovery and scalable projected-space treatment as central challenges for further scaling sample-based approaches.
Sources
- Closed-loop calculations of electronic structure on a quantum processor and a classical supercomputer at full scale
- Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization
- Quantum chemistry with provable convergence via randomized sample-based Krylov quantum diagonalization
- Quantum Filter Diagonalization: Quantum Eigendecomposition without Full Quantum Phase Estimation
- Quantum computing with Qiskit
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