Closed-form first-passage-time distribution for an underdamped harmonic oscillator (short-time Hamiltonian + long-time Kramers) agrees with micro-cantilever data and yields the power of an information engine.
Virtual potential created by a feedback loop: taming the feedback demon to explore stochastic thermodynamics of underdamped systems
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abstract
Virtual potentials are an elegant, precise and flexible tool to manipulate small systems and explore fundamental questions in stochastic thermodynamics. In particular double-well potentials have applications in information processing, such as the demonstration of Landauer's principle. In this chapter, we detail the implementation of a feedback loop for an underdamped system, in order to build a tunable virtual double-well potential. This feedback behaves as a demon acting on the system depending on the outcome of a continuously running measurement. It can thus modify the energy exchanges with the thermostat and create an out-of-equilibrium state. To create a bi-stable potential, the feedback consists only in switching an external force between two steady values when the measured position crosses a threshold. We show that a small delay of the feedback loop in the switches between the two wells results in a modified velocity distribution. The latter can be interpreted as a cooling of the kinetic temperature of the system. Using a fast digital feedback, we successfully address all experimental issues to create a virtual potential that is statistically indistinguishable from a physical one, with a tunable barrier height and energy step between the two wells.
fields
cond-mat.stat-mech 2years
2026 2representative citing papers
A velocity-input neural Maxwell demon learns cold damping and extracts work near the theoretical bound from an underdamped thermal oscillator.
citing papers explorer
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First passage time for an underdamped harmonic oscillator and application to the power of an information engine
Closed-form first-passage-time distribution for an underdamped harmonic oscillator (short-time Hamiltonian + long-time Kramers) agrees with micro-cantilever data and yields the power of an information engine.
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A neural-network Maxwell's demon learns cold damping for work extraction
A velocity-input neural Maxwell demon learns cold damping and extracts work near the theoretical bound from an underdamped thermal oscillator.