Quantum Machine Learning
88 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Quantum Machine Learning
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Libraries
Use these libraries to find Quantum Machine Learning models and implementations
iris
Most implemented papers
Equivariant quantum circuits for learning on weighted graphs
When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm.
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning
Our framework can also be used as a library and integrated into pre-existing software, maximizing code reuse.
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e. g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection.
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.
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.
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.
Quantum adiabatic machine learning with zooming
The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.
Quantum enhancements for deep reinforcement learning in large spaces
LeaMarion/quantum-enhanceable-deep-rl
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In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods.
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.
Variational Quantum Circuits for Quantum State Tomography
We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator.
