Search Results for author:
Found 5 papers, 0 papers with code
Tensor Networks for Explainable Machine Learning in Cybersecurity
In this paper we show how tensor networks help in developing explainability of machine learning algorithms.
Application of Tensor Neural Networks to Pricing Bermudan Swaptions
The Cheyette model is a quasi-Gaussian volatility interest rate model widely used to price interest rate derivatives such as European and Bermudan Swaptions for which Monte Carlo simulation has become the industry standard.
Quantum-Inspired Tensor Neural Networks for Option Pricing
Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions.
Quantum-Inspired Tensor Neural Networks for Partial Differential Equations
We demonstrate that TNN provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN).
Quantum Portfolio Optimization with Investment Bands and Target Volatility
In this paper we show how to implement in a simple way some complex real-life constraints on the portfolio optimization problem, so that it becomes amenable to quantum optimization algorithms.
