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Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
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This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine learning. For self-consistency, this tutorial covers foundational principles, representative QML algorithms, their potential applications, and critical aspects such as trainability, generalization, and computational complexity. In addition, practical code demonstrations are provided in https://qml-tutorial.github.io/ to illustrate real-world implementations and facilitate hands-on learning. Together, these elements offer readers a comprehensive overview of the latest advancements in QML. By bridging the gap between classical machine learning and quantum computing, this tutorial serves as a valuable resource for those looking to engage with QML and explore the forefront of AI in the quantum era.
Forward citations
Cited by 8 Pith papers
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LCQNN: Linear Combination of Quantum Neural Networks
LCQNN combines several trainable unitaries through a learned superposition on control qubits, yielding gradient variance bounds that scale polynomially with local system size rather than exponentially with total qubit count.
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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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Pulsed learning for quantum data re-uploading models
A pulse-level data re-uploading classifier outperforms its gate-based counterpart in noisy superconducting-qubit simulation.
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Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
Classical surrogates using truncated trigonometric expansions emulate noisy quantum processors and cut measurement overhead in VQE pre-training and Floquet phase identification.
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Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems
A hybrid quantum CNN with a shared parameterized quantum circuit across layers achieves about 98% of exhaustive-search sum throughput for cell-free massive MIMO pilot assignment while using fewer parameters than class...
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Overcoming Barren Plateaus in Variational Quantum Circuits using a Two-Step Least Squares Approach
A two-stage convex/nonconvex least-squares algorithm is claimed to remove the condition-number barrier in variational quantum optimization and achieve high-fidelity BB84 quantum-state cloning.
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Q-Detection: A Quantum-Classical Hybrid Poisoning Attack Detection Method
A quantum-classical hybrid method trains a weighting network on QUBO solvers to filter poisoned image training samples, reaching clean-subset quality comparable to Meta-Sift in simulations.
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Artificial intelligence for representing and characterizing quantum systems
A review organizes AI-based quantum system characterization into ML, deep learning, and language model paradigms, covering property prediction and implicit state reconstruction.
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