What we’re about
The future of computing has come out of the labs. Software development for quantum computing is happening in the GTA, and this meetup aims at bringing people from this fledgling industry together with anybody who wants to learn about quantum computing technology, its disruptive potential, and its application in fields as far ranging as fintech and biology.
Upcoming events (3)See all
- Gaming for Quantum Literacy: Modern Approaches to Quantum Information AdvocacyLink visible for attendees
This talk will present recent successes of providing access to quantum information science for a fast-growing audience. Its integration into an educational setting are outlined and its future implications are discussed.
Tim Linke is a PhD candidate at the University of California, Davis. He serves as president of the Quantum Computing at Davis student-led research group, where he’s hosted campus-wide events and built collaborations with groups across the UC system.
Moderator: Il Young Chung, co-organizer of Quantum Computing and Data Science.
Zoom registration will be provided no later than one hour prior to the event start time.
Youtube live stream link will be posted on the forum about 15 minutes before the event.
- Accelerating Materials Discovery with AI and MLLink visible for attendees
Materials form the backbone of our society, presenting potential to at least 10/17 sustainable development goals. Traditional materials discovery relies on trial and error approaches thereby leading to a design to deploy period of 20-30 years. To address this challenge, in this talk, we will discuss the application of artificial intelligence (AI) and machine learning (ML) in accelerating materials modeling and discovery. Specifically, three aspects where AI and ML can be used include: (i) natural language processing (NLP) for extracting information from the materials literature, (ii) data-driven materials modeling, (iii) physics-informed machine learning for accelerated materials modeling. To demonstrate these aspects, three problems will be discussed. First focuses on extracting information on glasses and other materials from literature to answer specific queries. We will also discuss on how MatSciBERT, the first materials-aware language model, can be used to extract information regarding composition-property from the glass literature. Second, we focus on developing interpretable ML models for predicting properties based on the information extracted from the literature. This work covers nearly the entire periodic table for glass forming elements. Third, we will discuss on how to accelerate simulations using physics-informed ML (PIML). Here, we will discuss how interaction laws in nature can be discovered directly from the trajectory of physical systems using PIML. Altogether, the talk will cover various aspects of AI and ML that have been used to accelerate materials discovery. Finally, a brief outlook on the future prospects will be discussed.
Anoop completed his Ph.D. in Civil Engineering from Indian Institute of Science Bangalore in 2015, after which, he worked as a postdoctoral researcher in University of California Los Angeles from 2015 to 2017. Prior to this, he completed his B.Tech in Civil Engineering from National Institute of Technology Calicut in 2009. In October 2017, he joined IIT Delhi in the Department of Civil Engineering, where he is currently serving as an Associate Professor and heads the M3RG. He also holds a joint position as an Assistant Professor in the School of Artificial Intelligence, IIT Delhi. He has published more than 80 international peer-reviewed journal publications and has filed 3 patents. He has founded a start-up Substantial AI Pvt. Ltd., incubated at IIT Delhi, for AI-driven materials discovery and process optimisation. He has won several awards including Google scholar research award (2023), W. A. Weyl International Glass Science Award by ICG and Penn State University (2022), Indian National Academy of Engineering Young Engineer Award (INAE YAE 2020), BRNS-DAE Young Scientist Award (2021), and National Academy of Science India Young Scientist Award (NASI YSA 2021).
Moderator: Shadab Hussain, co-organizer of Quantum Computing India
- Compiling Resource-Efficient Programs with Numerical InstantiationLink visible for attendees
Quantum hardware is experiencing a boon leading to more chip variety and configurations with higher fidelities. While ultimately, this will translate to a boon for the entire field of quantum computing, it presents a software design problem by placing more of the overall burden of realizing end-to-end quantum applications on the software stacks, specifically the quantum compiler. The Berkeley Quantum Synthesis Toolkit (BQSKit) is a powerful and portable quantum compiler framework with a proven ability to alleviate this issue and translate recent hardware successes up to the algorithm level. BQSKit achieves superior portability and optimization potential by utilizing a parameterized quantum circuit intermediate representation to facilitate numerical instantiation. In this talk and demonstration, I first introduce the idea of numerical instantiation and BQSKit compilation, including algorithms and workflows for transpiling circuits to any hardware, even ones with heterogeneous gate sets or higher-level qudits (qutrits). I then detail several further practical use cases, such as error mitigation techniques with approximations and algorithm-hardware design exploration.
Ed Younis is a computer systems engineer at Lawrence Berkeley National Laboratory with extensive experience developing and implementing advanced algorithms for quantum compilation, such as QFAST and Qfactor. He is currently the principal engineer on the BQSKit project and has research interests in quantum synthesis, compilation, and software systems.