Quantum systems engineering

Abstract:

We propose and analyze a scheme for parametrically cooling bilayer cuprates based on the selective driving of a c-axis vibrational mode. The scheme exploits the vibration as a transducer making the Josephson plasma frequencies time dependent. We show how modulation at the difference frequency between the intrabilayer and interbilayer plasmon substantially suppresses interbilayer phase fluctuations, responsible for switching c-axis transport from a superconducting to a resistive state. Our calculations indicate that this may provide a viable mechanism for stabilizing nonequilibrium superconductivity even above Tc, provided a finite pair density survives between the bilayers out of equilibrium.

Abstract:

Estimating the expected value of an observable appearing in a nonequilibrium stochastic process usually involves sampling. If the observable’s variance is high, many samples are required. In contrast, we show that performing the same task without sampling, using tensor network compression, efficiently captures high variances in systems of various geometries and dimensions. We provide examples for which matching the accuracy of our efficient method would require a sample size scaling exponentially with system size. In particular, the high-variance observable exp(−βW), motivated by Jarzynski’s equality, with W the work done quenching from equilibrium at inverse temperature β, is exactly and efficiently captured by tensor networks.