Simulating polaritonic ground states on noisy quantum devices
Mohammad Hassan, Fabijan Pavo\v{s}evi\'c, Derek S. Wang, Johannes Flick

TL;DR
This paper presents a framework for simulating electron-photon interactions in molecules within optical cavities using noisy quantum devices, employing VQE with symmetry-based qubit reduction and error mitigation to achieve accurate results.
Contribution
It introduces a VQE-based method with the PUCC ansatz and symmetry techniques for simulating polaritonic systems on noisy quantum hardware, enhancing accuracy and robustness.
Findings
Achieved chemical accuracy in ground-state energy calculations.
Successfully measured photon number as an indicator of electron-photon correlation.
Demonstrated robustness across various molecular and cavity parameters.
Abstract
The recent advent of quantum algorithms for noisy quantum devices offers a new route toward simulating strong light-matter interactions of molecules in optical cavities for polaritonic chemistry. In this work, we introduce a general framework for simulating electron-photon coupled systems on small, noisy quantum devices. This method is based on the variational quantum eigensolver (VQE) with the polaritonic unitary coupled cluster (PUCC) ansatz. To achieve chemical accuracy, we exploit various symmetries in qubit reduction methods, such as electron-photon parity, and use recently developed error mitigation schemes, such as the reference zero-noise extrapolation method. We explore the robustness of the VQE-PUCC approach across a diverse set of regimes for the bond length, cavity frequency, and coupling strength of the H molecule in an optical cavity. To quantify the performance, we…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStrong Light-Matter Interactions · Quantum Information and Cryptography · Neural Networks and Reservoir Computing
