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Found 14 papers, 1 papers with code
Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers.
Quantum linear algebra is all you need for Transformer architectures
Generative machine learning methods such as large-language models are revolutionizing the creation of text and images.
Quantum Algorithms for the Pathwise Lasso
We present a novel quantum high-dimensional linear regression algorithm with an $\ell_1$-penalty based on the classical LARS (Least Angle Regression) pathwise algorithm.
Learning Restricted Boltzmann Machines with greedy quantum search
Restricted Boltzmann Machines (RBMs) are widely used probabilistic undirected graphical models with visible and latent nodes, playing an important role in statistics and machine learning.
Provable learning of quantum states with graphical models
Several works consider subclasses of quantum states that can be learned in polynomial sample complexity such as stabilizer states or high-temperature Gibbs states.
Post-variational quantum neural networks
Hybrid quantum-classical computing in the noisy intermediate-scale quantum (NISQ) era with variational algorithms can exhibit barren plateau issues, causing difficult convergence of gradient-based optimization techniques.
Robust quantum minimum finding with an application to hypothesis selection
We demonstrate a quantum algorithm for noisy quantum minimum-finding that preserves the quadratic speedup of the noiseless case: our algorithm runs in time $\tilde O(\sqrt{N (1+\Delta)})$, where $\Delta$ is an upper-bound on the number of elements within the interval $\alpha$, and outputs a good approximation of the true minimum with high probability.
Quantum and Classical Algorithms for Approximate Submodular Function Minimization
Our classical result improves on the algorithm of [CLSW17] and runs in time $\widetilde{O}(n^{3/2}/\epsilon^2 \cdot \mathrm{EO})$.
Bayesian Deep Learning on a Quantum Computer
Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.
Smooth input preparation for quantum and quantum-inspired machine learning
Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage.
Quantum Machine Learning
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.
Quantum algorithms for supervised and unsupervised machine learning
Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces.
Quantum Physics
Quantum principal component analysis
The usual way to reveal properties of an unknown quantum state, given many copies of a system in that state, is to perform measurements of different observables and to analyze the measurement results statistically.
Quantum Physics
Quantum support vector machine for big data classification
Supervised machine learning is the classification of new data based on already classified training examples.
