Engines for predictive work extraction from memoryful quantum stochastic processes

Ruo Cheng Huang1, Paul M. Riechers1,2, Mile Gu1,3,4, and Varun Narasimhachar1,5

1Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
2Beyond Institute for Theoretical Science (BITS), San Francisco, CA, USA
3Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore
4MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit, UMI 3654, 117543, Singapore
5A*STAR Quantum Innovation Centre (Q.InC), Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Republic of Singapore 138632

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Abstract

Quantum information-processing techniques enable work extraction from a system's inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum stochastic processes. We combine tools from these two sciences to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. We demonstrate that this technique can extract more work than non-predictive quantum work extraction protocols, on the one hand, and predictive work extraction without quantum information processing, on the other. We discover a phase transition in the efficacy of memory for work extraction from quantum processes, which is without classical precedent. Our work opens up the prospect of machines that harness environmental free energy in an essentially quantum, essentially time-varying form.

Thermodynamics has taught us that engines can be fueled not only by heat flow, but by any process arising from objects being out of thermal equilibrium. Quantum engines are powered by uniquely quantum forms of such non-equilibrium processes. Meanwhile, information ratchets (predictive engines) use memory and prediction to harness processes' patterns over time, which would be inaccessible to memoryless, non-predictive engines. We build an engine that combines quantum and predictive functionalities, and thereby, extracts useful energy from processes in a way that neither non-predictive quantum engines nor non-quantum predictive engines can.

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