Machine Learning Identifies Quantum Thermodynamic Arrow
Xiang-Qian Meng et al. (arXiv preprint submitted March 11, 2026) use machine learning to detect the thermodynamic arrow of time from individual trajectories on a programmable ten-qubit nitrogen-vacancy centre quantum processor. They implement circuits with heat flow and time-reversed counterparts, showing unsupervised clustering separates trajectories and a convolutional neural network identifies temporal direction with about 92% accuracy. A diffusion-based generative model reproduces directional energy flow and entropy signatures.
Scoring Rationale
Strong experimental ML demonstration across a programmable ten-qubit processor, limited by arXiv preprint status and niche focus.
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