I am Shahnawaz Ahmed, a Ph.D. candidate at the Wallenberg Centre for Quantum Technology, Chalmers University of Technology, Göteborg. My research focuses on the intersection of quantum information, computing, and machine learning. I am interested in exploring the merger of these fields, with a particular emphasis on applying machine-learning techniques to solve problems in quantum physics.
I developed adversarial neural networks for quantum tomography, a Riemannian optimization technique to speed up quantum process learning, and have written software for quantum physics and circuit simulations during my Ph.D. I have also collaborated with experimentalists to characterize quantum systems and create non-classical quantum states of light (GKP and CAT states). I am passionate about open-source software and am actively involved in the team developing QuTiP, the quantum toolbox in Python. Additionally, I have worked on PennyLane, where I have contributed to writing tools and tutorials that delve into Quantum Machine Learning.
In the past, I worked with Juan Carrasquilla at VectorAI and Nathan Killoran at Xanadu as a MITACS fellow. I am collaborating with the quantum machine learning team at Xanadu. I did my master’s thesis in the group of Prof. Franco Nori at Riken, Japan, where I focused on developing numerical approaches to model open quantum systems and explored how deep neural networks can learn the rules of games such as Sudoku.
Please contact me at shahnawaz.ahmed95@gmail.com to discuss collaborations, research opportunities, or any other topic related to quantum physics, machine learning, and software. You can also find me on Twitter @quantshah and connect with me on LinkedIn quantshah. Please visit my Google Scholar page to explore my research publications.
For a detailed overview of my experience and qualifications, you can look at my CV.
Thank you for visiting my GitHub page!