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.


Google Scholar Page

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.
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.
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.

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