Mimicking natural cooling processes in quantum systems proves surprisingly difficult, according to a new study of the challenges facing quantum simulators and processors. The work reveals that a fundamental assumption in statistical mechanics, a vastly larger energy-absorbing system than the system itself, is often unmet in current, limited-size devices, leading to system recurrences instead of stable states.
Researchers find that sampling from the equilibrium distribution with inverse temperature β, σ^β ∝ e^-βH^S, is a formidable algorithmic challenge despite the ease with which physical systems cool; this is further complicated by the quantum energy-time uncertainty relation limiting cooling speed. The study details how recent advances, including experiments on a Google quantum processor and new theoretical frameworks, are beginning to address these obstacles. Experiments on a Google quantum processor have demonstrated preparation of low-energy states of up to 35 qubits by coupling to a small, resettable bath of auxiliary qubits.
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