REVIEW 16 cited by
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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 16 Pith papers
-
Exponential quantum advantage in processing massive classical data
A polylog-sized quantum computer achieves exponential advantage over classical machines in classification and dimension reduction of massive classical data using quantum oracle sketching combined with classical shadows.
-
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.
-
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.
-
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 classica...
-
HQ-UNet: A Hybrid Quantum-Classical U-Net with a Quantum Bottleneck for Remote Sensing Image Segmentation
HQ-UNet places a shallow quantum circuit at the U-Net bottleneck and reports 0.805 mean IoU and 94.76% accuracy on LandCover.ai, beating the classical baseline.
-
QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
A CNN-plus-quantum-circuit classifier with learned fusion reports lower attack success rates and much higher attack-generation cost than a CNN baseline on MNIST, OrganAMNIST, and CIFAR-10.
-
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.
-
HQF-Net: A Hybrid Quantum-Classical Multi-Scale Fusion Network for Remote Sensing Image Segmentation
HQF-Net reports mIoU gains on three remote-sensing benchmarks by adding quantum circuits to skip connections and a mixture-of-experts bottleneck inside a classical U-Net fused with a DINOv3 backbone.
-
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.
-
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.
-
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...
-
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.
-
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.
-
A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
IA-QCNN applies quantum principles via ring-topology convolution and importance weighting to achieve claimed high-accuracy MGMT methylation prediction from MRI with fewer parameters and noise robustness than classical models.
-
A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
-
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.
Discussion (0). Sign in to comment.