Emerging Opportunities in Quantum Machine Learning & Quantum Algorithms

America/Los_Angeles
48-1-112 - Redwood A/B (SLAC)

48-1-112 - Redwood A/B

SLAC

2575 Sand Hill Rd. Menlo Park, CA 94025
    • 13:15 15:00
      Talks
      • 13:15
        Opening Remarks 15m
      • 13:30
        Quantum Machine Learning: The landscape from an industry perspective. 45m

        Quantum Machine Learning (QML) is emerging as an application for quantum computers with the potential to be the killer app. With the successful application of machine learning methods In industry applications and the emerging deep learning renaissance is it natural to extrapolate that quantum computers, once fully capable, will play a transformative role in the way we think of computation and communication with learning and automation driven methods at their core. This exploratory talk will briefly introduce the basics of QML and survey the landscape of existing methods. The talk will then, showcase a few example quantum machine learning projects done by the INQNET group at the AT&T Foundry, while highlighting how such projects could lead to real world applications in industry. In conclusion the talk will raise some questions on the current status of the field, in the hope of spurring constructive discussion on future progress.

        Speaker: Rishiraj Pravahan (AT&T Foundry)
      • 14:15
        Quantum AI at Fermilab 45m

        Machine learning has the potential to be an important early application area for quantum computers. In this presentation I will discuss the Quantum Science Program at Fermilab at a high level, and then zoom in on a pair of recently awarded DOE grants that include machine learning applications as part of their portfolios. I will discuss the applications we're exploring and chart a course for some NISQ-era projects in the space.

        Speaker: Gabriel Perdue (Fermilab)
    • 15:00 15:30
      Discussion/Coffee
    • 15:30 17:00
      Talks
      • 15:30
        Quantum Algorithms for Quantum Field Theory 45m

        Inherently quantum calculators may be able to empower quantum field theory calculations. After discussing existing results for scattering amplitudes and lattice methods, I will motivate a new direction where interference and entanglement effects are largely ignored in current high energy simulations.

        Speaker: Ben Nachman (LBNL)
      • 16:15
        Quantum Machine Learning:Use Cases, Challenges, and Potential 45m

        We survey the field of quantum machine learning, in particular the algorithmic tools which are used in modern quantum ML algorithms. We review both heuristic approaches, such as quantum neural and tensor networks, as well as provable approaches based on the HHL algorithm and quantum linear algebra. We also discuss the varying applications, and amenability to near-term quantum computers.

        Speakers: Adam Bouland (QCWare), Matthew Coudron (QCWare)
    • 17:00 19:00
      Wine & Cheese