Year: 2026
Type: preprint
Abstract: Dissipative algorithms approach the problem of ground state preparation by mimicking the natural thermalization of a quantum system in contact with a large, low-temperature thermal environment. The environment can be efficiently simulated by a single ancilla qubit with a variable energy gap that is repeatedly coupled to the system qubits to generate a dissipative channel, and then reset after each interaction. Here we present an adaptive implementation of the dissipative algorithm, RL-Adapt, that uses single-shot reinforcement learning to optimize the selection of ancilla frequency and system-bath interaction operator to maximize energy dissipation without relying on a priori knowledge of the system spectrum. The adaptive implementation results in significantly reduced convergence times and can successfully find the ground state even for non-ideal operator pools that fail to converge using non-adaptive, uniform random operator and ancilla frequency selection.
Source: arXiv (Cornell University)
Institutions: Pacific Northwest National Laboratory, University of Michigan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G122673668, G125460123, G1286236842, G1286251239, G138090931, G1677143136, G1724155629, G1851334662, G1899721271, G213222014, G2270264153, G2506945562, G2510423872, G2618837358, G30714459, G391136020, G4108734848, G419881185, G42916606, G4501827968, G498139845, G5076365615, G5321382714, G5726405315, G613759197, G650090426, G6652958823, G7665161096, G8135867528, G821644656, G8960879569
OpenAlex ID: W7214575281