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.
Featured image: Quantum pattern engine extracts work using both memory-enhanced prediction and quantum operations.
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