Natansh Mathur

Senior R&D Scientist, Quantinuum

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Singapore

Nationality: Indian

Hello! I am Natansh Mathur (Hindi: नतांश माथुर), a Senior R&D Scientist at Quantinuum in Singapore, working on quantum machine learning and optimisation algorithms.

Before Quantinuum, I spent six years at QC Ware in Paris, most recently as an Associate Staff Scientist, where I built quantum machine learning solutions for industrial partners across pharmaceuticals, finance, and automotive. Alongside this, I completed an industrial PhD (CIFRE) in Computer Science at IRIF, CNRS & Université Paris Cité, supervised by Prof. Iordanis Kerenidis, on the theory and applications of Hamming-weight preserving quantum neural networks. I hold a Master’s (MPRI) from Université Paris Cité and a B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Roorkee.

My research focuses on quantum algorithms for machine learning: orthogonal and Hamming-weight preserving quantum neural networks, quantum vision transformers, Bayesian quantum neural networks, and their applications to real-world problems in medical imaging, anomaly detection, and financial forecasting.

news

Aug 01, 2026 Joined Quantinuum in Singapore as a Senior R&D Scientist, working on quantum machine learning and optimisation algorithms.
Jun 15, 2026 Our paper “Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation” was accepted at IEEE QCE 2026, with a talk at IEEE Quantum Week 2026 in Toronto, Canada.
Sep 01, 2025 Our paper “Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection” was accepted at IEEE QCE 2025, with talks at QTML 2025 (Singapore) and IEEE Quantum Week 2025 (Albuquerque), and a poster at QCTiP 2025 (Berlin).

selected publications

  1. IEEE QCE
    Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
    Natansh Mathur, Panagiotis Kl Barkoutsos, Masako Yamada, and 2 more authors
    IEEE International Conference on Quantum Computing and Engineering (QCE), 2026
  2. IEEE QCE
    Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
    Natansh Mathur, Brian Coyle, Nishant Jain, and 4 more authors
    In IEEE International Conference on Quantum Computing and Engineering (QCE), 2025
  3. npj QI
    Training-efficient density quantum machine learning
    Brian Coyle, El Amine Cherrat, Nishant Jain, and 4 more authors
    npj Quantum Information, 2025
  4. Quantum
    Quantum vision transformers
    Iordanis Kerenidis, Natansh Mathur, Jonas Landman, and 3 more authors
    Quantum, 2024