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Found 8 papers, 3 papers with code
A Two-Scale Complexity Measure for Deep Learning Models
We introduce a novel capacity measure 2sED for statistical models based on the effective dimension.
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.
Error mitigation for universal gates on encoded qubits
The Eastin-Knill theorem states that no quantum error correcting code can have a universal set of transversal gates.
Quantum Physics
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
Generalized maximum entropy estimation
We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise.
