awesome-quantum-ml
github.com/artix41/awesome-quantum-ml ↗Curated list of awesome papers and resources in quantum machine learning
528
GitHub Stars
119
Curated Resources
5
Categories
23 hours ago
Last Refreshed
Variational circuitsQRAM-based quantum MLApplicationsSoftwareKernel methods and SVM
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me barren plateau resources from awesome-quantum-ml"
Installation instructions →What's inside
Variational circuits
- Absence of Barren Plateaus in Quantum Convolutional Neural NetworksBarren plateau
- A Continuous Variable Born Machine
- A generative modeling approach for benchmarking and training shallow quantum circuitsGenerative models
- Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculusBarren plateau
- An Artificial Neuron Implemented on an Actual Quantum ProcessorClassification and regression
- An initialization strategy for addressing barren plateaus in parametrized quantum circuitsBarren plateau
QRAM-based quantum ML
- Advances in Quantum Reinforcement LearningReinforcement learning
- A quantum-inspired classical algorithm for recommendation systemsDequantization of QRAM-based QML
- Classical and Quantum Algorithms for Orthogonal Neural NetworksClassification and regression
- Experimental quantum speed-up in reinforcement learning agentsReinforcement learning
- Exponential improvements for quantum-accessible reinforcement learningReinforcement learning
- Generalized Quantum Reinforcement Learning with Quantum TechnologiesReinforcement learning
Kernel methods and SVM
Resources
- A non-review of Quantum Machine Learning: trends and explorations
- A Survey of Quantum Learning Theory
- Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
- Quantum-enhanced machine learning
- Quantum Machine Learning
- Quantum Machine Learning: a classical perspective
Applications
Showing a sample of 119 resources. View the full list on GitHub →