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Found 6 papers, 2 papers with code
Quantum Compiling with Reinforcement Learning on a Superconducting Processor
To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology.
Recent Advances for Quantum Neural Networks in Generative Learning
Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
We systematically evaluate the performance of our proposal in compiling quantum operators with both inverse-closed and inverse-free universal basis sets.
The dilemma of quantum neural networks
The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bound than their classical counterparts to ensure better reliability and interpretability.
Machine Learning techniques for state recognition and auto-tuning in quantum dots
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i. e. tuning up devices.
Quantum Physics
Exponential improvements for quantum-accessible reinforcement learning
Quantum computers can offer dramatic improvements over classical devices for data analysis tasks such as prediction and classification.
