Oxford University · Quantum Computing · AI · Mathematics

Fyodor Amanov

UNESCO Quantum 100 Laureate and Founder of QuanTech.

I am a quantum computing researcher and postgraduate student at Oxford University, focusing on quantum computation, quantum machine learning, and distributed quantum computing.

My work spans quantum algorithms, quantum communications, quantum finance, and mathematical methods for machine learning.

I have held research roles at Harvard University, Khalifa University, and Sejong University, and conducted quantum simulation research with NYU Abu Dhabi and Khalifa University.

Research & Experience Curriculum Vitae GitHub ↗
Main Photo
Research experience

Across leading research institutions

Academic research roles and collaborations in quantum computing, mathematics, and machine learning.

Oxford University

Researcher · Postgraduate studies · 2026–present

Quantum computation, quantum machine learning, and distributed quantum computing.

Harvard University

Research Assistant · 2023

Developed and optimised deep neural network models with more than 70 million parameters.

NYU Abu Dhabi & Khalifa University

Independent Researcher · 2025

Variational quantum simulations on quantum hardware; additional mathematics research at Khalifa University.

QAI Ventures, National University of Singapore, Nanyang Technological University

Researcher · 2025

Quantum Monte Carlo simulations and quantum machine learning.

Sejong University

Research Assistant · 2025–2026

Quantum optimisation techniques for machine learning and quantum graph-state protocols.

Gdansk University

Researcher · 2023

Quantum Cryptography: post quantum cryptography and quantum key distribution

12
Publications
6+
Years of AI/Quantum Research Experience
10
TEDx Talks
3
Published Books
About

Quantum research grounded in mathematics

I now pursue my postgraduate studies at Oxford. My mathematical training includes numerical methods and optimisation theory, differential geometry, category theory, and abstract algebra.

I use these foundations to study quantum computing and machine learning, from theoretical models to algorithms on quantum hardware.

Mathematical equations on a chalkboard

Research focus

At Oxford, my academic focus is quantum computation, quantum machine learning, and distributed quantum computing. My wider work spans photonic quantum reservoir computing, quantum Monte Carlo for finance, variational quantum simulations of crystal structures, categorical quantum computation, quantum programming languages, and two published books on quantum computing. I bridge deep mathematical theory with practical quantum applications.

Current projects also include distributed quantum machine learning and transformer- and LSTM-based quantum circuit optimisation.

Research & Publications →
Quantum computing chip

Education & training

After finishing my BSc under the academic direction of MIT and the Technical University of Munich, I began postgraduate studies at Oxford. I also studied on exchange at Khalifa University and Sejong University, and completed academic programmes at Google Quantum AI, EPFL, MIT–KAIST, and MIT & Cornell.

Full CV →
Featured Work

Selected projects

A sample of quantum computing, machine learning, and engineering projects.

Presenting Quantum Monte Carlo in Singapore Quantum Finance

Quantum Monte Carlo & QLSTM for Finance

Quantum Monte Carlo simulations and QLSTM models for option pricing, developed at QAI Ventures in Singapore. Run on real quantum hardware.

QiskitPennyLanePyTorchMonte Carlo
View on GitHub →
NYUAD Quantum Hackathon team Quantum Chemistry

EnergetiQ — NYUAD Hackathon

Quantum-powered battery design using VQE & QEOM for energy spectra extraction, with ML-driven resource allocation. 2nd place at NYUAD Quantum Hackathon.

VQEQEOMAzure QuantumK-Means
View on GitHub →
Optimization landscape QML · Finance

Photonic QRC for Swaption Surfaces

Hybrid photonic quantum reservoir computing achieving RMSE 0.0425 with sub-millisecond inference. Zero trainable quantum parameters.

PercevalRidgeAutoencoderFock States
View on GitHub →
Quantum computing hardware Quantum Networking

Quantum Graph States for Networking

Protocols on quantum graph states for system routing and networking, developed at Sejong University.

Graph StatesRoutingNetworking
Project details →
Quantum computing software Quantum Algorithms

Quantum Circuit Optimisation

Research on transformer- and LSTM-based approaches to quantum circuit optimisation.

TransformersLSTMQuantum Circuits
Project details →
Mathematical derivations Quantum ML

Distributed Quantum Machine Learning

Research on distributed quantum machine learning algorithms and their applications.

Distributed ComputingQuantum ML
Project details →
All Projects →
Moments

Conferences, hackathons & research

Quantum hackathon team at NYUAD Presenting Quantum Monte Carlo research Mathematical derivations
Contact

Get in touch

I'm open to collaborations in quantum computing, QML, and machine learning.

fyodor.amanov@chch.ox.ac.uk LinkedIn GitHub Scholar