A microarchitecture-aware compiler for lattice surgery that exploits C-Phase commutativity to enable concurrent multi-target operations and dynamic event-driven scheduling, cutting execution time by up to 59.7 times versus standard baselines.
An introduction to quantum machine learning,
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QSMOTE variants with PGM and KPGM classifiers outperform Random Forest on imbalanced Telco churn data, reaching 0.8512 accuracy and 0.8234 F1 using stereo encoding with two quantum copies.
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C-Phase-Aware Compilation for Efficient Fault-Tolerant Quantum Execution
A microarchitecture-aware compiler for lattice surgery that exploits C-Phase commutativity to enable concurrent multi-target operations and dynamic event-driven scheduling, cutting execution time by up to 59.7 times versus standard baselines.
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QSMOTE-PGM/kPGM: QSMOTE Based PGM and kPGM for Imbalanced Dataset Classification
QSMOTE variants with PGM and KPGM classifiers outperform Random Forest on imbalanced Telco churn data, reaching 0.8512 accuracy and 0.8234 F1 using stereo encoding with two quantum copies.