Neural networks trained on local 3x3 tensor-network charge-stability data can predict on-site disorder with high accuracy (R²>0.99) for the central dot in larger 5x5 disordered Hubbard model arrays, enabling scalable tuning of quantum dot spin qubits.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Multiplexed cryo-CMOS control enables stable biasing and fast pulsing of an isolated silicon double quantum dot at 0.5 K, supporting deterministic multi-electron loading and resolution of tunneling events across charge transitions.
Simulations indicate a semiconducting cQED quantum annealer could complete MHT tasks in ~50 ms, positioning the technology as promising for real-time tracking applications.
citing papers explorer
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Large Scale Optimization of Disordered Hubbard Models through Tensor and Neural Networks
Neural networks trained on local 3x3 tensor-network charge-stability data can predict on-site disorder with high accuracy (R²>0.99) for the central dot in larger 5x5 disordered Hubbard model arrays, enabling scalable tuning of quantum dot spin qubits.
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Multiplexed cryo-CMOS control of an isolated double quantum dot
Multiplexed cryo-CMOS control enables stable biasing and fast pulsing of an isolated silicon double quantum dot at 0.5 K, supporting deterministic multi-electron loading and resolution of tunneling events across charge transitions.
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Simulation of quantum annealing on a semiconducting cQED device for Multiple Hypothesis Tracking (MHT) benchmark
Simulations indicate a semiconducting cQED quantum annealer could complete MHT tasks in ~50 ms, positioning the technology as promising for real-time tracking applications.