Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
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Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
Encodes M by N matrix into quantum state using Θ(log(MN)) qubits in O(log²(MN)) time via segment tree embedded in bucket brigade QRAM with constant ancillas and O(MN) memory cells.
Proposes a quantum-walker qRAM on a single binary tree using local operations that reduces resources while preserving optimal query complexity.
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
Quantum circuits implement Hamming-distance-like genomic classifiers via active and symmetric inner products on IBM quantum processors with fixed qubit requirements for arbitrary training samples.
citing papers explorer
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Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
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Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation
Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
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Efficient Quantum State Preparation with Bucket Brigade QRAM
Encodes M by N matrix into quantum state using Θ(log(MN)) qubits in O(log²(MN)) time via segment tree embedded in bucket brigade QRAM with constant ancillas and O(MN) memory cells.
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A resource-efficient quantum-walker Quantum RAM
Proposes a quantum-walker qRAM on a single binary tree using local operations that reduces resources while preserving optimal query complexity.
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Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
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Implementation of a Hamming-Distance-Like Genomic Quantum Classifier Using Inner Products on IBMQX4 and IBMQX16
Quantum circuits implement Hamming-distance-like genomic classifiers via active and symmetric inner products on IBM quantum processors with fixed qubit requirements for arbitrary training samples.