Abstract
The integration of Tensor Networks into quantum computing has unlocked new possibilities, particularly for efficiently loading complex datasets onto quantum systems. In this talk, we examine the tensor-train cross approximation (TT-cross) algorithm as an effective solution to the probability loading challenge in Quantum Monte Carlo (QMC). By applying TT-cross to high-dimensional financial distributions, we demonstrate its scalability and precision, emphasizing its ability to significantly reduce circuit depth requirements and computational complexity compared to conventional techniques. These advancements make QMC simulations more practical on near-term quantum hardware and provide a potential pathway for implementing QMC effectively on quantum machines.
Speaker Bios
Aser Cortines
Aser holds an MSc in Applied Mathematics from École Polytechnique, France, and a PhD in Applied Mathematics from the University of Paris Diderot – Paris 7. He has conducted postdoctoral research at Technion – Israel Institute of Technology and the University of Zurich. Aser’s professional experience includes working as a quantitative analyst for an electricity and gas supplier, where he developed models and libraries used by the front office to operate in energy markets. He also has expertise in model risk assessment of banking models. In 2022, Aser joined Multiverse Computing as a financial engineer, contributing to various projects that applied quantum technology to financial modeling. Since 2023, he has been serving as Director of Engineering, leading various projects in areas such as finance, optimization, and machine learning
Antonio Pereira
Engineer specializing in quantum computing, with expertise in quantum algorithms and tensor networks. Integrated Master’s (BSc + MSc) student in Physics Engineering from the University of Minho. Experience includes work on quantum and quantum-inspired solutions for network management and finance. Passionate about connecting theoretical advances with practical applications to foster interest in quantum technologies.
