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About this paper
Oriol Balló-Gimbernat, Emiliano Tolotti, Laia Domingo, Fernando Vilariño, Adan Garriga
The conditional IQP Born machine learns temperature-dependent spin distributions by encoding each condition in its input state and training one shared unitary across conditions. Classical sampling and amplitude ratios support optimization of a kernel-based distribution-matching objective. On a 16-spin Curie–Weiss model, it reproduces held-out distribution changes with mean total variation distance 0.046 ± 0.004; this does not establish quantum advantage.
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