Elton Zhu

About

Elton is a quantum research scientist at the Fidelity Center for Applied Technology (FCAT), where he leads Fidelity's research into how quantum computing can be applied across a wide variety of use cases across the firm. Prior to his current role, Elton was a data scientist at Fidelity Capital Markets.

Elton has published research papers on various topics in quantum algorithms, quantum machine learning, quantum information and quantum communication. He is broadly interested in the intersection of quantum computing, finance and AI.

Elton holds a Bachelor of Arts and a Master’s Degree in mathematics from the University of Cambridge and Ph.D. in physics from MIT.

https://www.linkedin.com/in/elton-zhu/

Google Scholar Page

1068925.3.0

LATEST WORK

Quantum

Solving QUBOs with a Quantum-Amenable branch and bound method

By: Elton Zhu | July 30, 2024

FCAT quantum researcher Elton Zhu delves into a proposed optimization solver and explores how this combination of high-performance and quantum computing techniques may hold the power to spark breakthroughs in finance and other fields.

Quantum

Article

07/30/2024

Quantum

Explainable AI Using Expressive Boolean Formulas

By: Elton Zhu and Serdar Kadioglu | June 8, 2023

In this POC, Amazon Quantum Solutions Lab and FCAT proposed and implemented an interpretable machine learning model for Explainable AI (XAI) based on expressive Boolean formulas.

Quantum

Artificial Intelligence

Article

06/08/2023

Quantum

Quantum State Preparation of Normal Distributions using Matrix Product States

BY: Elton Zhu | March 6, 2023

Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to predict outcomes of complex scenarios with multiple unknowns. They’re commonly used in the financial industry to evaluate risks, price derivatives, and create long-term strategies, as in the case of retirement planning.

Quantum

Article

03/02/2023

ARTIFICAL INTELLIGENCE

Iteration Complexity of Variational Quantum Algorithms

BY: Elton Zhu | September 21, 2022

In collaboration with Czech Technical University, FCAT released a paper analyzing the effect of noise on variational quantum algorithms. Variational quantum algorithms are a family of algorithms where an optimization algorithm implemented on a classical computer evaluates a parametrized quantum circuit as an objective function. The researchers obtained the missing guarantees on the rate convergence in the presence of noise.

Quantum

Article

09/23/2022

Connect with us.

This website is operated by Fidelity Center for Applied Technology (FCAT)^® which is part of Fidelity Wealth Technologies, LLC, a Fidelity Investments company. FCAT experiments with and provides innovative products, services, content and tools, as a service to its affiliates and as a subsidiary of FMR LLC. Based on user reaction and input, FCAT is better able to engage in technology research and planning for the Fidelity family of companies. FCATalyst.com is independent of fidelity.com. Unless otherwise indicated, the information and items published on this web site are provided by FCAT and are not intended to provide tax, legal, insurance or investment advice and should not be construed as an offer to sell, a solicitation of an offer to buy, or a recommendation for any security by any Fidelity entity or any third-party. In circumstances where FCAT is making available either a product or service of an affiliate through this site, the affiliated company will be identified. Third party trademarks appearing herein are the property of their respective owners. All other trademarks are the property of FMR LLC.

1150441.1.0

This is for persons in the U.S. only.

245 Summer St, Boston MA