TNPA uses tensor-network contractions only in a reliable temperature window to seed population annealing, with an effective-sample-size diagnostic to pick the switch-over temperature.
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Nonlinear cross-entropy benchmark and heavy-output classifier enable sample-efficient distinction between noisy quantum and classical spoofers for shallow-depth all-to-all random circuits.
Spectral functions of SYK, p-spin, and SU(M) Heisenberg models show exponential tails in spin-glass phases and quasiparticle families in spin-liquid phases, with a proof that exponential decay blocks detection of bulk causal structure.
KLR Hopfield networks store up to 16-20 times their neuron count before dynamical instability from crosstalk noise causes collapse, with sharp attractor boundaries observed via morphing and SNR analysis.
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Tensor-Network Population Annealing
TNPA uses tensor-network contractions only in a reliable temperature window to seed population annealing, with an effective-sample-size diagnostic to pick the switch-over temperature.
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Sample-efficient benchmarking of shallow all-to-all random quantum circuits
Nonlinear cross-entropy benchmark and heavy-output classifier enable sample-efficient distinction between noisy quantum and classical spoofers for shallow-depth all-to-all random circuits.
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Searching for emergent spacetime in spin glasses
Spectral functions of SYK, p-spin, and SU(M) Heisenberg models show exponential tails in spin-glass phases and quasiparticle families in spin-liquid phases, with a proof that exponential decay blocks detection of bulk causal structure.
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Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks
KLR Hopfield networks store up to 16-20 times their neuron count before dynamical instability from crosstalk noise causes collapse, with sharp attractor boundaries observed via morphing and SNR analysis.