Autonomous oscillations in quantum electromechanics: tensor network treatment

• URL: http://arxiv.org/abs/2605.27326v1
• Date: Tue, 26 May 2026 17:35:54 GMT
• Title: Autonomous oscillations in quantum electromechanics: tensor network treatment
• Abstract summary: Transport-induced self-sustained oscillations in electromechanical systems convert a static electrochemical bias into robust, autonomous motion.<n>We formulate a tensor-network framework that combines a binary representation of the vibrational mode with mesoscopic reservoir embeddings.<n>We demonstrate the emergence of mechanical self-oscillations across a broad set of operating conditions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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