Finite-size reliability of homothetic quantum Otto engines
Gabriella G. Damas, Clebson Cruz, Norton G. de Almeida, Gao Xianlong, G. D. de Moraes Neto
Abstract
Homothetic quantum Otto engines---where all populated energy gaps are rescaled by a common factor---provide a reference model in which the quasistatic stochastic efficiency is trajectory-independent while work remains fluctuating. For arbitrary finite homothetic spectra we derive the two-point-measurement work distribution and reduce the first two work moments to endpoint energy moments. Specializing to a uniformly spaced ladder gives closed finite-N expressions for the full work distribution, mean work, variance, and signal-to-width reliability. This ladder connects the qubit and oscillator limits, reveals a finite-N reliability crossover, and demonstrates that the high-temperature and infinite-dimensional limits do not commute. The noncommutation reflects a bounded-versus-unbounded spectral distinction: at fixed finite N the Gibbs state has a normalizable infinite-temperature limit, whereas the oscillator retains an ever-expanding thermal tail. The exact formulas are used to compare standard mean-output prescriptions with work reliability, showing that maximum mean output and maximum dimensionless reliability select different operating points. The benchmark is extended to incomplete diagonal reset and to finite-time unitary strokes described by transition matrices, with a finite-ladder protocol and a harmonic sudden-switch oscillator benchmark as controlled examples. Weak deviations from exact homothety are treated perturbatively, showing how level-dependent gap distortions reintroduce quasistatic efficiency fluctuations and modify work reliability. Together, these results separate finite-size, incomplete thermalization, finite-time, and weak spectral-distortion contributions to work unreliability in quantum Otto engines.
Create a lesson
Related papers
Trading Circuit Depth for Pulse Sparsity in Chromatic Dynamical Decoupling
Amy F. Brown, Daniel A. Lidar
Optimal spectrum estimation
Ainesh Bakshi, Apoorv Vikram Singh, Xinyu Tan
Non-Abelian sheaf quantum LDPC codes: good and magical
Zimu Li, Fuchuan Wei, Zhengyi Han et al.
Learning and interpreting policies for simultaneous entanglement requests in quantum networks
Leon Rode, Sumeet Khatri, Supartha Podder
Sharp universal death of entanglement threshold for Pauli Hamiltonians
Bobak T. Kiani
Proper Agnostic Learning of Matrix Product States and Tree Tensor Networks
Constantin Cedillo Vayson de Pradenne, Jordan Cotler