Conditioning on rare boundary measurement outcomes in a quantum East circuit generates states with finite two-point correlations at arbitrary distances and an underlying Sierpiński-triangle fractal structure.
Scalable, high-fidelity all-electronic control of trapped-ion qubits,
5 Pith papers cite this work. Polarity classification is still indexing.
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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.
An error-resilient gate search scheme using multi-objective optimization and pulse symmetries enables microsecond two-qubit gates with fidelities approaching 99.9% in linear ion traps of up to 50 ions.
A two-fold quantum embedding strategy combined with machine learning integrates accurate quantum-mechanical energies into free energy calculations for biomolecular complexes and analyzes requirements for quantum computers to enhance such modeling.
A composition-only ML framework with Rashomon ensembles extracts consensus design rules and screens ~45,000 compounds to identify 122 high-confidence quantum defect hosts, recovering known materials and predicting new ones validated by limited DFT.
citing papers explorer
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Exact large deviations and emergent long-range correlations in sequential quantum East circuits
Conditioning on rare boundary measurement outcomes in a quantum East circuit generates states with finite two-point correlations at arbitrary distances and an underlying Sierpiński-triangle fractal structure.
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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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Error-Resilient Fast Entangling Gates for Scalable Ion-Trap Quantum Processors
An error-resilient gate search scheme using multi-objective optimization and pulse symmetries enables microsecond two-qubit gates with fidelities approaching 99.9% in linear ion traps of up to 50 ions.
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How to use quantum computers for biomolecular free energies
A two-fold quantum embedding strategy combined with machine learning integrates accurate quantum-mechanical energies into free energy calculations for biomolecular complexes and analyzes requirements for quantum computers to enhance such modeling.
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Beyond Diamond: Interpretable Machine Learning Reveals Design Principles for Quantum Defect Host Materials
A composition-only ML framework with Rashomon ensembles extracts consensus design rules and screens ~45,000 compounds to identify 122 high-confidence quantum defect hosts, recovering known materials and predicting new ones validated by limited DFT.