«Raúl is a person who is easy to work with, with great technical skills and capacity for learning»
Raúl París Murillo
Madrid y alrededores
1 mil seguidores
Más de 500 contactos
Madrid y alrededores
1 mil seguidores
Más de 500 contactos
Acerca de
As a data scientist, I am passionate about applying data-driven insights to solve complex…
Actividad
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Google NotebookLM: Transform Your Documents into Podcasts Google’s NotebookLM just introduced Audio Overview, a feature that turns your text…
Google NotebookLM: Transform Your Documents into Podcasts Google’s NotebookLM just introduced Audio Overview, a feature that turns your text…
Compartido por Raúl París Murillo
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Introducing OpenAI o1: A New Milestone in AI Reasoning OpenAI’s o1 series is a groundbreaking advancement in artificial intelligence, specifically…
Introducing OpenAI o1: A New Milestone in AI Reasoning OpenAI’s o1 series is a groundbreaking advancement in artificial intelligence, specifically…
Compartido por Raúl París Murillo
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Boosting data manipulation in Python: If you're looking to boost Pandas' performance, here are three powerful alternatives: 1️⃣ cuDF (RAPIDS):…
Boosting data manipulation in Python: If you're looking to boost Pandas' performance, here are three powerful alternatives: 1️⃣ cuDF (RAPIDS):…
Compartido por Raúl París Murillo
Experiencia
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Educación
Licencias y certificaciones
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Generative AI with Large Language Models
DeepLearning.AI, Amazon Web Services
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Machine Learning Specialization
Stanford University & DeepLearning.AI
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Experiencia de voluntariado
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Hospital Volunteer
Capgemini
- 2 mes
Infancia
Participation with the Capgemini Foundation in 'Technology Stories in Hospitals' (Hospital Universitario La Paz)
Publicaciones
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VaR Estimation with Quantum Computing Noise Correction using Neural Networks
MDPI
In this paper, we present the development of a quantum computing method for calculating the value at risk (𝑉𝑎𝑅) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the 𝑉𝑎𝑅 of an arbitrary number of assets by employing random variable algebra and Taylor series approximation. The resulting algorithm is suitable to be executed in real quantum computers. However, the noise affecting current quantum computers renders them…
In this paper, we present the development of a quantum computing method for calculating the value at risk (𝑉𝑎𝑅) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the 𝑉𝑎𝑅 of an arbitrary number of assets by employing random variable algebra and Taylor series approximation. The resulting algorithm is suitable to be executed in real quantum computers. However, the noise affecting current quantum computers renders them almost useless for the task. We present a methodology to mitigate the noise impact using neural networks to compensate for the noise effects. The system combines the output from a real quantum computer with the neural network processing. The feedback is used to fine-tune the quantum circuits. The results show that this approach is useful for estimating the 𝑉𝑎𝑅 in finance institutions, particularly when dealing with many assets. We demonstrate the validity of the proposed method with up to 139 assets. The accuracy of the method is also proven. We achieved an error of less than 1% in the empirical measurements with respect to the parametric model.
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Español
Competencia bilingüe o nativa
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Inglés
Competencia profesional completa
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