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Quantum Machine Learning

Papers

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Showing 551–600 of 699 papers

TitleStatusHype
Variational Quantum Kernels with Task-Specific Quantum Metric Learning—0
Variational Quantum Neural Networks (VQNNS) in Image Classification—0
Policy Gradients using Variational Quantum Circuits—0
Explainable quantum regression algorithm with encoded data structure—0
Verifiable cloud-based variational quantum algorithms—0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum—0
Weight Re-Mapping for Variational Quantum Algorithms—0
What can we learn from quantum convolutional neural networks?—0
Quantum Differential Privacy: An Information Theory Perspective—0
Discrete Randomized Smoothing Meets Quantum Computing—0
Certifiably Robust Encoding Schemes—0
Superposed Parameterised Quantum Circuits—0
VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning—0
3D-QAE: Fully Quantum Auto-Encoding of 3D Point Clouds—0
A Case For Noisy Shallow Gate-Based Circuits In Quantum Machine Learning—0
Lean classical-quantum hybrid neural network model for image classification—0
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models—0
The role of entanglement for enhancing the efficiency of quantum kernels towards classification—0
Active Learning on a Programmable Photonic Quantum Processor—0
Adaptive Non-local Observable on Quantum Neural Networks—0
Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles—0
Advantage of Quantum Machine Learning from General Computational Advantages—0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis—0
Adversarial Robustness Guarantees for Quantum Classifiers—0
Adversarial Robustness of Partitioned Quantum Classifiers—0
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models—0
A Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods—0
Agnostic Process Tomography—0
A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud—0
A Hybrid Quantum Neural Network for Split Learning—0
A Hyperparameter Study for Quantum Kernel Methods—0
AI-driven Reverse Engineering of QML Models—0
A Matrix Product State Model for Simultaneous Classification and Generation—0
A Modified Depolarization Approach for Efficient Quantum Machine Learning—0
Analyzing Machine Learning Performance in a Hybrid Quantum Computing and HPC Environment—0
An Amalgamation of Classical and Quantum Machine Learning For the Classification of Adenocarcinoma and Squamous Cell Carcinoma Patients—0
Analytic theory for the dynamics of wide quantum neural networks—0
An efficient combination strategy for hybird quantum ensemble classifier—0
An efficient quantum algorithm for generative machine learning—0
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients—0
An example of use of Variational Methods in Quantum Machine Learning—0
An exponentially-growing family of universal quantum circuits—0
An Exponential Separation Between Quantum and Quantum-Inspired Classical Algorithms for Linear Systems—0
An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data—0
An Introduction to Quantum Machine Learning for Engineers—0
An Invitation to Distributed Quantum Neural Networks—0
A novel feature selection method based on quantum support vector machine—0
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All datasetshttps://www.kaggle.com/datasets/saurabhshahane/classification-of-malwaresiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Quantum Neural NetworkF1 score0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1Best ModelAverage F11—Unverified