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Quantum Information Processing

Thermodynamic cost of erasing quantum correlation

Arun Kumar Pati

Published Apr 7, 2026• 0 citations
Publisher
DOI
10.1007/s11128-026-05125-0

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Erasure of information stored in a quantum state requires energy cost and is inherently an irreversible operation. If quantumness of a system is physical, does erasure of quantum correlation as measured by discord also need some energy cost? Here, we show that change in quantum correlation is never larger than the total entropy change of the system and the environment. The entropy cost of erasing correlation has to be at least equal to the amount of quantum correlation erased. Hence, quantum correlation can be regarded as genuinely physical. We show that the new bound leads to the Landauer erasure. The physical cost of erasing quantum correlation is well respected in the case of bleaching of quantum information, thermalization, and can have potential application for any channel leading to erasure of quantum correlation.

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Erasure of information stored in a quantum state requires energy cost and is inherently an irreversible operation. If quantumness of a system is physical, does erasure of quantum correlation as measured by discord also need some energy cost? Here, we show that change in quantum correlation is never larger than the total entropy change of the system and the environment. The entropy cost of erasing correlation has to be at least equal to the amount of quantum correlation erased. Hence, quantum correlation can be regarded as genuinely physical. We show that the new bound leads to the Landauer erasure. The physical cost of erasing quantum correlation is well respected in the case of bleaching of quantum information, thermalization, and can have potential application for any channel leading to erasure of quantum correlation.
Erasing memory is a fundamental operational task in quantum information processing, governed by Landauer's principle, which links information loss to thermodynamic work. We introduce and analyze assisted quantum erasure, where correlations with a remote system reduce the energetic cost of resetting a memory. We identify exclusive control of erasure as the central operational requirement: only a designated party should be able to achieve the minimal cost, whereas any adversary must fail. In the device-dependent regime, we show that entanglement of formation exactly characterizes exclusivity, establishing entanglement as the decisive thermodynamic resource. Moving to a one-sided device-independent scenario, in which only the memory holder's device is trusted, we develop an operational erasure protocol based on random dephasing and conditional operations. Finally, in a fully device-independent setting, we show how Bell nonlocality and self-testing translate observed violations into lower bounds on any adversary's erasure capability, yielding a device-independent notion of exclusive thermodynamic control. Taken together, these results elevate quantum erasure from a thermodynamic constraint to an operational primitive: the erasure work cost quantifies secure, exclusive control over quantum memory, ensuring that an unauthorized agent cannot fully erase information under a bounded work budget.
The energy cost of erasing quantum states depends on our knowledge of the states. We show that learning algorithms can acquire such knowledge to erase many copies of an unknown state at the optimal energy cost. This is proved by showing that learning can be made fully reversible and has no fundamental energy cost itself. With simple counting arguments, we relate the energy cost of erasing quantum states to their complexity, entanglement, and magic. We further show that the constructed erasure protocol is computationally efficient when learning is efficient. Conversely, under standard cryptographic assumptions, we prove that the optimal energy cost cannot be achieved efficiently in general. These results also enable efficient work extraction based on learning. Together, our results establish a concrete connection between quantum learning theory and thermodynamics, highlighting the physical significance of learning processes and enabling provably-efficient learning-based protocols for thermodynamic tasks.