Quantum Machine Learning
Benchmarks
Greatest papers with code
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations.
Variational quantum Gibbs state preparation with a truncated Taylor series
By performing numerical experiments, we show that shallow parameterized circuits with only one additional qubit can be trained to prepare the Ising chain and spin chain Gibbs states with a fidelity higher than 95%.
Continuous-variable quantum neural networks
• XanaduAI/quantum-neural-networks
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The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field.
Quantum Natural Gradient
A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits.
Quantum Wasserstein Generative Adversarial Networks
The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines.
Transfer learning in hybrid classical-quantum neural networks
• XanaduAI/quantum-transfer-learning
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We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements.
Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning
Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions.
Variational Quantum Circuits for Deep Reinforcement Learning
• ycchen1989/Var-QuantumCircuits-DeepRL
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To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network.
A quantum-inspired classical algorithm for recommendation systems
We give a classical analogue to Kerenidis and Prakash's quantum recommendation system, previously believed to be one of the strongest candidates for provably exponential speedups in quantum machine learning.