Engines for predictive work extraction from memoryful quantum stochastic processes

TL;DR

This paper introduces a quantum predictive work extraction method from non-Markovian processes, surpassing previous protocols and revealing a unique phase transition in quantum memory efficacy.

Contribution

It combines computational mechanics with quantum information to develop a novel technique for extracting work from quantum stochastic processes with memory.

Findings

The new method outperforms non-predictive quantum work extraction.

It uncovers a phase transition in the effectiveness of quantum memory.

Demonstrates potential for quantum machines to harness environmental free energy.

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 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.