673 citations · 964 across the 19 of their papers we have counts for
20 papers
Synthetic Data Generation and Differential Privacy using Tensor Networks' Matrix Product States (MPS)
Alejandro Moreno R., Desale Fentaw, Samuel Palmer +7
Synthetic data generation is a key technique in modern artificial intelligence, addressing data scarcity, privacy constraints, and the need for diverse datasets in training robust…
Quantum-Inspired Solver for Simulating Material Deformations
Mazen Ali, Aser Cortines, Siddhartha Morales +5
This paper explores the application of tensor networks (TNs) to the simulation of material deformations within the framework of linear elasticity. Material simulations are essentia…
Encoding of Probability Distributions for Quantum Monte Carlo Using Tensor Networks
Antonio Pereira, Alba Villarino, Aser Cortines +5
The application of Tensor Networks (TN) in quantum computing has shown promise, particularly for data loading. However, the assumption that data is readily available often renders…
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Andrei Tomut, Saeed S. Jahromi, Abhijoy Sarkar +15
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such a…
Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks
Pablo Martin-Ramiro, Unai Sainz de la Maza, Sukhbinder Singh +2
Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector. In this work, we introduce a Tensor Convolutional Neur…
Application of Tensor Neural Networks to Pricing Bermudan Swaptions
Raj G. Patel, Tomas Dominguez, Mohammad Dib +12
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 s…
Quantum-Inspired Tensor Neural Networks for Option Pricing
Raj G. Patel, Chia-Wei Hsing, Serkan Sahin +12
Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing th…
Financial Risk Management on a Neutral Atom Quantum Processor
Lucas Leclerc, Luis Ortiz-Guitierrez, Sebastian Grijalva +19
Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm sugges…
Financial Index Tracking via Quantum Computing with Cardinality Constraints
Samuel Palmer, Konstantinos Karagiannis, Adam Florence +4
In this work, we demonstrate how to apply non-linear cardinality constraints, important for real-world asset management, to quantum portfolio optimization. This enables us to tackl…
Quantum-Inspired Tensor Neural Networks for Partial Differential Equations
Raj Patel, Chia-Wei Hsing, Serkan Sahin +11
Partial Differential Equations (PDEs) are used to model a variety of dynamical systems in science and engineering. Recent advances in deep learning have enabled us to solve them in…
Multi-disk clutch optimization using quantum annealing
John D. Malcolm, Alexander Roth, Mladjan Radic +5
In this work, we develop a new quantum algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how qua…
Quantum artificial vision for defect detection in manufacturing
Daniel Guijo, Victor Onofre, Gianni Del Bimbo +7
In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their…
Quantum portfolio value forecasting
Cristina Sanz-Fernandez, Rodrigo Hernandez, Christian D. Marciniak +5
We present an algorithm which efficiently estimates the intrinsic long-term value of a portfolio of assets on a quantum computer. The method relies on quantum amplitude estimation…
Quantum Portfolio Optimization with Investment Bands and Target Volatility
Samuel Palmer, Serkan Sahin, Rodrigo Hernandez +2
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…
Hybrid Quantum Investment Optimization with Minimal Holding Period
Samuel Mugel, Mario Abad, Miguel Bermejo +3
In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal po…
Use Cases of Quantum Optimization for Finance
Samuel Mugel, Enrique Lizaso, Roman Orus
In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of…
Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks
Samuel Mugel, Carlos Kuchkovsky, Escolastico Sanchez +4
In this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time,…
Towards Prediction of Financial Crashes with a D-Wave Quantum Computer
Yongcheng Ding, Javier Gonzalez-Conde, Lucas Lamata +7
Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can guarantee to find optimal solutions effici…
Forecasting financial crashes with quantum computing
Roman Orus, Samuel Mugel, Enrique Lizaso
A key problem in financial mathematics is the forecasting of financial crashes: if we perturb asset prices, will financial institutions fail on a massive scale? This was recently s…
Quantum computing for finance: overview and prospects
Roman Orus, Samuel Mugel, Enrique Lizaso
We discuss how quantum computation can be applied to financial problems, providing an overview of current approaches and potential prospects. We review quantum optimization algorit…