Shahnawaz Ahmed

Hi, I am Shahnawaz. I am a deep learning researcher exploring how large neural networks can be compressed to run on embedded systems at Embedl. I work with research and development of deep learning model optimization techniques, including quantization, pruning, neural architecture search. My work involves optimizing models for edge devices using hardware like Qualcomm and NVIDIA GPUs, conducting workshops to train engineers on model optimization strategies, and collaborating with customers to develop efficient deep learning models.

I am also collaborating with the quantum machine learning team at Xanadu with Dr. Maria Schuld on benchmarking quantum vs classical machine learning. My work involves developing and testing quantum machine learning models on high-performance computing clusters (NERSC) to assess their properties and capabilities in solving real-world problems.

I completed my Ph.D. at the Wallenberg Centre for Quantum Technology, Chalmers University of Technology, Göteborg. My research focused 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. In my Ph.D., 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 Prof. Juan Carrasquilla at VectorAI (not at ETH, Zurich) and Nathan Killoran at Xanadu as a MITACS fellow. 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 development. You can also 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!