Cooling qubits to near their ground state is a prerequisite for reliable quantum computation, and algorithmic cooling schemes offer a systematic route to achieve it. A preprint posted to arXiv — not yet peer-reviewed — examines how the memory of a quantum reservoir alters the balance between two competing resources: the number of cooling steps required and the geometric cost of implementing the corresponding quantum circuit.
The authors focus on single-qubit heat-bath algorithmic cooling governed by the damped Jaynes–Cummings model, a canonical non-Markovian open-quantum-systems setting. By applying a pseudomode mapping, they translate the non-Markovian dynamics into a repeated-collision circuit amenable to both matrix-based numerical simulation and direct hardware execution on IBM's ibm_kawasaki Heron r2 processor. The paper claims a clear trade-off: suppressing reservoir memory — pushing the dynamics toward the Markovian limit — reduces the number of cooling steps needed but generally inflates the geometric complexity of the protocol's circuit implementation.
The result, if borne out by peer review, offers a resource-theoretic lens for reservoir engineering in quantum cooling: practitioners cannot freely minimize both step count and circuit cost simultaneously, and the optimal operating point depends on which resource is more constrained in a given hardware context. The authors frame their work as a step toward principled design of cooling protocols that account for implementation overhead, not just thermodynamic performance.