English

Autonomous oscillations in quantum electromechanics: tensor network treatment

Quantum Physics 2026-05-27 v1 Mesoscale and Nanoscale Physics Statistical Mechanics

Abstract

Transport-induced self-sustained oscillations in electromechanical systems convert a static electrochemical bias into robust, autonomous oscillatory motion in the absence of any external periodic drive. However, an exact description of such self-oscillations remains challenging in nanoscale electromechanical devices featuring a simultaneously large bosonic Hilbert space, strong interactions, and structured fermionic leads. We formulate a tensor-network framework that combines a binary representation of the vibrational mode with mesoscopic reservoir embeddings that enable controlled access to the self-oscillatory steady states and relevant transport observables without explicit real-time propagation. We demonstrate the emergence of mechanical self-oscillations across a broad set of operating conditions, in which strong electromechanical backaction, nonadiabatic oscillator dynamics, and energy-dependent electronic tunneling processes compete. Furthermore, we observe that for both slow and fast vibrating mechanical modes, suppressed vibrational occupation fluctuations in the self-oscillation window along the electromechanical coupling strength sweep is preceded by a peak in the occupation fluctuations. Collectively, we explore how both intrinsic system properties and environmental parameters govern such autonomous oscillations over a broad range of operating conditions. The generality of our framework will enable the method to be employed straightforwardly to more complicated or experimentally relevant scenarios.

Keywords

Cite

@article{arxiv.2605.27326,
  title  = {Autonomous oscillations in quantum electromechanics: tensor network treatment},
  author = {Mahasweta Pandit and Sheikh Parvez Mandal and Mark T. Mitchison and Javier Prior},
  journal= {arXiv preprint arXiv:2605.27326},
  year   = {2026}
}

Comments

23 pages, 11 figures. Comments are welcome!

R2 v1 2026-07-22T07:35:06.369Z