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Found 10 papers, 0 papers with code
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.
Provable Advantage of Parameterized Quantum Circuit in Function Approximation
To achieve this, we utilize techniques from quantum signal processing and linear combinations of unitaries to construct PQCs that implement multivariate polynomials.
Learning Theory of Distribution Regression with Neural Networks
In contrast to the classical regression methods, the input variables of distribution regression are probability measures.
Distributed Gradient Descent for Functional Learning
In the information era, to face the big data challenges {that} stem from functional data analysis very recently, we propose a novel distributed gradient descent functional learning (DGDFL) algorithm to tackle functional data across numerous local machines (processors) in the framework of reproducing kernel Hilbert space.
Efficient information recovery from Pauli noise via classical shadow
The rapid advancement of quantum computing has led to an extensive demand for effective techniques to extract classical information from quantum systems, particularly in fields like quantum machine learning and quantum chemistry.
Power and limitations of single-qubit native quantum neural networks
Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization.
Optimal quantum dataset for learning a unitary transformation
In this work, we solve the problem on the minimum size of sufficient quantum datasets for learning a unitary transformation exactly, which reveals the power and limitation of quantum data.
Robust Kernel-based Distribution Regression
Regularization schemes for regression have been widely studied in learning theory and inverse problems.
Estimates on Learning Rates for Multi-Penalty Distribution Regression
The main contribution of the paper is to present a novel multi-penalty regularization algorithm to capture more features of distribution regression and derive optimal learning rates for the algorithm.
Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras
Our bilateral consistency metric is used to indicate the probability of occlusions by analyzing the SCams.
