Rodríguez-Briones, N. A., & Park, D. K. (2026). Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling. PRX Quantum, 7(1), Article 010350. https://doi.org/10.1103/24n3-tskj
This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling process. Building on this concept, we develop a quantum refrigerator protocol that enhances sample efficiency during training and prediction without the need for Grover iterations or quantum phase estimation. Inspired by heat-bath algorithmic cooling protocols, our method alternates entropy compression and thermalization steps to decrease the entropy of qubits, increasing polarization toward the dominant bias. This technique minimizes the computational overhead associated with estimating classification scores and gradients, presenting a practical and efficient solution for QML algorithms compatible with noisy intermediate-scale quantum devices.
en
Research facilities:
Vienna Scientific Cluster
-
Project title:
MUlti State logic In cluster state Quantum computing: 914030 (FFG - Österr. Forschungsförderungs- gesellschaft mbH) Control and complexity in quantum statistical mechanics: 101043705 (European Commission) Quantenthermodynamischer Rahmen zur Förderung der fehlertoleranten Quantenwissenschaft: 101204616 (European Commission)
-
Project (external):
MSCA QTF_Ataulfo Korea government Yonsei University Ministry of Trade, Industry, and Energy (MOTIE), Korea