Level attraction from interference in two-tone driving
Alan Gardin, Guillaume Bourcin, Christian Person, Christophe Fumeaux, Romain Lebrun, Isabella Boventer, Giuseppe C. Tettamanzi, Vincent Castel

TL;DR
This paper clarifies that the observed level attraction in two-tone driven bosonic modes results from measurement interference effects, not dissipative coupling, and demonstrates how to derive effective Hamiltonians for such systems.
Contribution
It provides a theoretical analysis and finite-element simulations showing that level attraction arises from interference, not dissipative coupling, and introduces methods to derive effective Hamiltonians.
Findings
Level attraction is caused by measurement interference effects.
Dissipative coupling is not responsible for the observed level attraction.
Effective Hamiltonians can be derived to accurately describe the system physics.
Abstract
Coherent and dissipative couplings, respectively characterised by energy level repulsion and attraction, each have different applications for quantum information processing. Thus, a system in which both coherent and dissipative couplings are tunable on-demand and in-situ is tantalising. A first step towards this goal is the two-tone driving of two bosonic modes, whose experimental signature was shown to exhibit controllable level repulsion and attraction by changing the phase and amplitude of one drive. However, whether the underlying physics is that of coherent and dissipative couplings has not been clarified, and cannot be concluded solely from the measured resonances (or anti-resonances) of the system. Here, we show how the physics at play can be analysed theoretically. Combining this theory with realistic finite-element simulations, we deduce that the observation of level attraction…
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
TopicsNeural Networks and Applications · Neural dynamics and brain function · stochastic dynamics and bifurcation
