QRSI spans degenerate quantum eigenspaces almost surely by conjugating the Hamiltonian with random unitaries on g parallel branches and using subspace estimation, while exactly preserving the spectral gap.
Supervised Learning with Quantum-Enhanced Feature Spaces
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
QARIMA applies quantum autocorrelation via swap tests and fixed variational quantum circuits to automate lag discovery and AR/MA coefficient estimation in classical ARIMA models, reporting lower out-of-sample errors than automated classical ARIMA on tested datasets.
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
A hybrid geometric classifier using correlation groups and overlap similarities achieves 0.85-0.96 accuracy on standard tabular datasets and 0.85 minority recall on highly imbalanced fraud data via a variational quantum refinement layer.
Hybriqu Encoder delivers 5.4% faster pure angle encoding at 64 qubits on Apple Silicon by using AVX SIMD and cache-friendly precalculations, with gains increasing beyond L1 cache size while full-state updates remain memory-bound.
citing papers explorer
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Quantum Randomized Subspace Iteration
QRSI spans degenerate quantum eigenspaces almost surely by conjugating the Hamiltonian with random unitaries on g parallel branches and using subspace estimation, while exactly preserving the spectral gap.
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Reachability Constraints in Variational Quantum Circuits: Optimization within Polynomial Group Module
A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
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QARIMA: A Quantum Approach To Classical Time Series Analysis
QARIMA applies quantum autocorrelation via swap tests and fixed variational quantum circuits to automate lag discovery and AR/MA coefficient estimation in classical ARIMA models, reporting lower out-of-sample errors than automated classical ARIMA on tested datasets.
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Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
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Quantum-Inspired Geometric Classification with Correlation Group Structures and VQC Decision Modeling
A hybrid geometric classifier using correlation groups and overlap similarities achieves 0.85-0.96 accuracy on standard tabular datasets and 0.85 minority recall on highly imbalanced fraud data via a variational quantum refinement layer.
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Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking
Hybriqu Encoder delivers 5.4% faster pure angle encoding at 64 qubits on Apple Silicon by using AVX SIMD and cache-friendly precalculations, with gains increasing beyond L1 cache size while full-state updates remain memory-bound.