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This paper proposes a novel paradigm for quantum reservoir computing where information is encoded directly into the time-dependent temperature of a thermal bath, effectively turning heat dissipation into a computational resource. The authors derive rigorous trade-offs between memory and nonlinearity, demonstrating that computational power does not strictly depend on large Hilbert spaces but rather on specific observable choices and bath engineering. This work advances fundamental understanding of thermally-native quantum information processing by shifting the focus from Hamiltonian control to measurement channel optimization.
Thermally-driven quantum reservoir computing
arXiv:2611.07187v1 Announce Type: new Abstract: Heat generation limits computation, classical and quantum, from single logic gates to entire data centers. However, an in principle limiting physical factor can sometimes be turned into a resource. In reservoir computing, energy dissipation is what gives a reservoir its fading memory. Here, we introduce a paradigm where the data injection itself is encoded on the time-dependent temperature of a thermal bath. We derive an exact expression for the nonlinear response of any fixed readout, and prove a rigorous memory-nonlinearity trade-off which turns out to be a structural necessity, rather than a tunable dynamical regime, since the same coupling that injects data also enforces equilibration. We illustrate both results with a driven qubit, using a single free parameter, and with a pair of coupled qubits, where the breaking of a discrete symmetry opens a fifteen-dimensional space to a single observable, yet that observable alone recovers just over half of it. This refutes the misconception that large Hilbert spaces guarantee computational power. Our results relocate the memory-nonlinearity trade-off from the reservoir's dynamics to its measurement channel, pointing to observable choice and bath engineering (instead of Hamiltonian control) as the physical levers for a thermally-native quantum reservoir computer.
Abstract:Heat generation limits computation, classical and quantum, from single logic gates to entire data centers. However, an in principle limiting physical factor can sometimes be turned into a resource. In reservoir computing, energy dissipation is what gives a reservoir its fading memory. Here, we introduce a paradigm where the data injection itself is encoded on the time-dependent temperature of a thermal bath. We derive an exact expression for the nonlinear response of any fixed readout, and prove a rigorous memory-nonlinearity trade-off which turns out to be a structural necessity, rather than a tunable dynamical regime, since the same coupling that injects data also enforces equilibration. We illustrate both results with a driven qubit, using a single free parameter, and with a pair of coupled qubits, where the breaking of a discrete symmetry opens a fifteen-dimensional space to a single observable, yet that observable alone recovers just over half of it. This refutes the misconception that large Hilbert spaces guarantee computational power. Our results relocate the memory-nonlinearity trade-off from the reservoir's dynamics to its measurement channel, pointing to observable choice and bath engineering (instead of Hamiltonian control) as the physical levers for a thermally-native quantum reservoir computer.
Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech)
arXiv:2611.07187 [quant-ph]
(or arXiv:2611.07187v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2611.07187 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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