DecompRL is an RL method that learns modular code decomposition for LLMs, enabling exponential candidate generation via recombination to solve harder coding problems with lower GPU cost.
Operating two exchange-only qubits in parallel
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
Quasi-zero pulses achieve high-fidelity exchange gates on Intel's Tunnel Falls device with identical durations and fewer tuning parameters than distortion-compensating filters.
A 3D phase tomography method extracts the voltage-dependent exchange phase volume in silicon quantum dots using holography-style measurements and PUMA unwrapping to identify optimal pi-pulse locations.
citing papers explorer
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DecompRL: Solving Harder Problems by Learning Modular Code Generation
DecompRL is an RL method that learns modular code decomposition for LLMs, enabling exponential candidate generation via recombination to solve harder coding problems with lower GPU cost.
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Driving Exchange Interaction in Spin Qubits with Quasi-Zero Pulses
Quasi-zero pulses achieve high-fidelity exchange gates on Intel's Tunnel Falls device with identical durations and fewer tuning parameters than distortion-compensating filters.
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3D tomography of exchange phase in a Si/SiGe quantum dot device
A 3D phase tomography method extracts the voltage-dependent exchange phase volume in silicon quantum dots using holography-style measurements and PUMA unwrapping to identify optimal pi-pulse locations.