A vision-transformer neural network trained unsupervised on synthetic conductance data proposes Hamiltonian parameter updates that drive quantum dot chains into the topological phase with Majorana modes, often succeeding in a single step.
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A VAE learns a minimal latent representation from noisy quantum simulator snapshots that correlates with the sine-Gordon equilibrium parameter and detects anomalous post-quench dynamics including frozen-in solitons.
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AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
A vision-transformer neural network trained unsupervised on synthetic conductance data proposes Hamiltonian parameter updates that drive quantum dot chains into the topological phase with Majorana modes, often succeeding in a single step.
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Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
A VAE learns a minimal latent representation from noisy quantum simulator snapshots that correlates with the sine-Gordon equilibrium parameter and detects anomalous post-quench dynamics including frozen-in solitons.