Develops a threshold-regularized Moore-Penrose pseudoinverse formulation of PGM with hybrid classical-quantum circuit implementation using block-encoding for stable discrimination in ill-conditioned and rank-deficient ensembles.
An introduction to quantum machine learning
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 5representative citing papers
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.
Proposes a heterogeneous quantum repeater network architecture using recursive designs and RuleSets with a new bridging building block, but states that full-scale resource trade-off analysis remains future work.
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.
Constants defining the new SI are reinterpreted as conversion factors forming a geometric table of units, distinguishing human-chosen values from natural ones.
citing papers explorer
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Robust Pretty Good Measurement via Hybrid Classical-Quantum Pseudoinverse Approximation and Circuit-Level Realization
Develops a threshold-regularized Moore-Penrose pseudoinverse formulation of PGM with hybrid classical-quantum circuit implementation using block-encoding for stable discrimination in ill-conditioned and rank-deficient ensembles.
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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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Resource Management in Heterogeneous Quantum Repeater Networks
Proposes a heterogeneous quantum repeater network architecture using recursive designs and RuleSets with a new bridging building block, but states that full-scale resource trade-off analysis remains future work.
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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.
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The structure of the new SI
Constants defining the new SI are reinterpreted as conversion factors forming a geometric table of units, distinguishing human-chosen values from natural ones.