Computational Work Extraction: The Complexity of Catalysts

Abstract:We prove maximal separations: $n$-qubit systems can have $\Theta(n)$ ergotropy, while every efficient process extracts negligible work, even for Hamiltonians consisting of single-qubit terms. We establish an unconditional existential separation and give an explicit construction in the random oracle model. Assuming the existence of quantum-secure pseudorandom functions, this separation extends to the plain model. This work uncovers an important connection between ergotropy and the complexity of catalytic computation---computation where auxiliary qubits must be finally restored to their initial state. Relative to a random oracle, we establish relational and decision problems that: (i) can be solved efficiently with $\lambda$ catalysts; but (ii) cannot be solved by any algorithm with $c\lambda$ catalysts, for any $c<1$. We show this by proving query lower bounds for quantum-space bounded algorithms. As a consequence, for computational ergotropy, catalysts prove to be surprisingly powerful---there is a family of Hamiltonians and states for which catalysts enable efficient extraction of the full $\Theta(n)$ ergotropy, while every efficient non-catalytic process extracts negligible work. Furthermore, catalysts also allow us to introduce and instantiate the notion of pseudoergotropy---analogous to pseudorandomness. On the other hand, we show catalysts do not change (information-theoretic) ergotropy. Finally, our work also sheds light on the classical aspect of the problem. First, most of our constructions rely on classical states and Hamiltonians and therefore imply analogous results for classical ergotropy. Second, we show that certain proof of quantumness protocols can be used to generically separate classical and quantum catalytic ergotropy.

Quantum Physics (quant-ph); Computational Complexity (cs.CC)

arXiv:2609.40323 [quant-ph]

(or arXiv:2609.40323v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.40323 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.