6 papers
COS-TT-CHF: A Tensor-Train Characteristic-Function COS Method for Multi-Asset Option Pricing
Lucas Arenstein, Michael Kastoryano
This paper considers European multi-asset option pricing under Lévy and affine characteristic-function models. The main obstruction is the curse of dimensionality: direct multidime…
(MPO): Multivariate Polynomial Optimization based on Matrix Product Operators
Niccolò Ciolli, Anders Vestergaard Nørskov, Michael Kastoryano +2
Central to machine learning and signal processing is the ability to perform universal function approximation and learn complex input-output relationships from limited numbers of ob…
A tensor-train multidimensional inverse Laplace transform
Martin Mikkelsen, Michael Kastoryano
Laplace transforms and their numerical inverses arise throughout applied mathematics, physics, finance, and probability theory. Numerical inversion, however, quickly becomes intrac…
Full grid solution for multi-asset options pricing with tensor networks
Lucas Arenstein, Michael Kastoryano
Pricing multi-asset options via the Black-Scholes PDE is limited by the curse of dimensionality: classical full-grid solvers scale exponentially in the number of underlyings and ar…
Fast and Flexible Quantum-Inspired Differential Equation Solvers with Data Integration
Lucas Arenstein, Martin Mikkelsen, Michael Kastoryano
Accurately solving high-dimensional partial differential equations (PDEs) remains a central challenge in computational mathematics. Traditional numerical methods, while effective i…
Functional matrix product state simulation of continuous variable quantum circuits
Andreas Bock Michelsen, Frederik K. Marqversen, Michael Kastoryano
We introduce a functional matrix product state (FMPS) based method for simulating the real-space representation of continuous-variable (CV) quantum computation. This approach effic…