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A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead
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Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.
Forward citations
Cited by 11 Pith papers
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Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery
Hybrid QCNNs with 2–4 qubits classify SEVIRI volcanic-cloud scenes with F1 up to 1.00, matching or beating classical models with far fewer parameters.
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VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
Compile-once PyTorch-native statevector simulation with native autograd yields large median speedups for static VQC inference and training, with an open selector for when to use it.
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PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation
Retrieval over a 13,389-example verified PennyLane corpus raises QHack pass@5 from 36/43/24% to 64/68/52% across 2022–2024 with Claude Sonnet 4.6.
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Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
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QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
Hybrid quantum-classical models using structured entanglement keep high accuracy on MNIST, OrganAMNIST and CIFAR-10 while lowering adversarial attack success rates and raising the computational cost of generating attacks.
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RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Partial adversarial coverage in simulated quantum federated learning improves small-perturbation robustness with little clean-accuracy loss, but label-sorted non-IID data removes about half the robustness benefit.
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Universal Fluctuations in the Tail Probability for d=2 Random Walks in Space-Time Random Environments
The reported d=2 random-walk universality result is unsupported: the full text is a quantum federated learning survey that never mentions random walks, tail probabilities, or lambda_ext.
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Towards quantum machine learning for assessing the resilience of post-quantum cryptography
A 16-qubit QGAN can approximate the first-byte distribution of SPHINCS+ signatures in simulation, but the result is a small-scale, unbenchmarked demonstration with no attack.
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