Abstract: Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are ...

For the preparation of high-dimensional functions on quantum computers, we introduce tensor network algorithms that are efficient with regard to dimensionality, optimize circuits composed of ...

Introduction: We present Quantum Adaptive Search (QAGS), a hybrid quantum-classical algorithm for global optimization of multivariate functions. The method employs an adaptive mechanism that ...

Abstract: Anomaly detection on multivariate key performance indicators (KPIs) is a key procedure for the quality and reliability of large-scale cyber-physical systems (CPSs). Although extensive ...

mvsp is a Python implementation of the protocols presented in Quantum state preparation for multivariate functions. The protocols are based on function approximations with finite Fourier or Chebyshev ...

1 Institute of Mathematics, University of Lübeck, Lübeck, Germany 2 Institute of Mathematics, National Academy of Sciences of Ukraine, Kyiv, Ukraine This paper ...

This work explores the representation of univariate and multivariate functions as matrix product states (MPS), also known as quantized tensor-trains (QTT). It proposes an algorithm that employs ...

Graph database vendor Neo4j Inc. is teaming up with Snowflake Inc. to make a library of Neo4j’s graph analytics functions available in the Snowflake cloud. The deal announced today allows users to ...

Find out why backpropagation and gradient descent are key to prediction in machine learning, then get started with training a simple neural network using gradient descent and Java code. Most ...