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Found 5 papers, 1 papers with code
Quantum Theory and Application of Contextual Optimal Transport
In cases where paired data measurements ($\mu$, $\nu$) are coupled to a context variable $p_i$ , one may aspire to learn a global transportation map that can be parameterized through a potentially unseen con-text.
Quantum Kernel Alignment with Stochastic Gradient Descent
Specifically, we demonstrate that Pegasos is particularly effective for non-stationary data, which is an important challenge in real-world applications.
On Quantum Circuits for Discrete Graphical Models
In this work, we provide the first method that allows one to provably generate unbiased and independent samples from general discrete factor models with a quantum circuit.
The power of quantum neural networks
We show that quantum neural networks are able to achieve a significantly better effective dimension than comparable classical neural networks.
Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
Through the interplay of a quantum channel, such as a variational quantum circuit, and a classical neural network, the qGAN can learn a representation of the probability distribution underlying the data samples and load it into a quantum state.
Quantum Physics
