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Found 9 papers, 5 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.
The complexity of quantum support vector machines
We show that the dual problem can be solved in $O(M^{4. 67}/\varepsilon^2)$ quantum circuit evaluations, where $M$ denotes the size of the data set and $\varepsilon$ the solution accuracy compared to the ideal result from exact expectation values, which is only obtainable in theory.
Effective dimension of machine learning models
Making statements about the performance of trained models on tasks involving new data is one of the primary goals of machine learning, i. e., to understand the generalization power of a model.
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.
Exact and practical pattern matching for quantum circuit optimization
An important building block for many quantum circuit optimization techniques is pattern matching, where given a large and a small quantum circuit, we are interested in finding all maximal matches of the small circuit, called pattern, in the large circuit, considering pairwise commutation of quantum gates.
Quantum Physics Data Structures and Algorithms
Improving Variational Quantum Optimization using CVaR
The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically.
Quantum Physics
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
Quantum Risk Analysis
Additionally, we show how to implement this algorithm and how to trade off the convergence rate of the algorithm and the circuit depth.
Quantum Physics
