QCE20 Workshop on
Applied Quantum Artificial Intelligence (AQAI)

Alejandro Perdomo-Ortiz, Zapata Computing
Keynote Speaker, AQAI Workshop

Alejandro Perdomo-Ortiz did his graduate studies, M.A and Ph.D. in Chemical Physics, at Harvard University. Over the past 12+ years, he has explored the computational limits and opportunities of quantum computing for real-world applications. Before joining Zapata Computing as a Senior Quantum Scientist and Quantum Applications Lead, Alejandro was the lead scientist of the Quantum Machine Learning effort at NASA’s Quantum Artificial Intelligence Laboratory (NASA QuAIL) where he worked for 5+ years. He was also the Co-Founder of Qubitera LLC, a consulting company acquired by Rigetti Computing where he worked after NASA and before his current appointment with Zapata Computing. His latest research involves the design of hybrid quantum-classical algorithms to solve hard optimization problems and intractable machine learning subroutines.

Nathan Killoran, Xanadu Quantum Computing
Panel Discussion Speaker, AQAI Workshop

Nathan Killoran is the Head of Software & Algorithms at Xanadu, and one of the founding developers of PennyLane, the world’s leading quantum machine learning software library. Nathan steers Xanadu’s open-source quantum software products and leads algorithm research in photonics and quantum machine learning. Nathan holds a PhD in Physics from the University of Waterloo, with expertise in quantum computing, deep learning, and quantum optics.

Davide Venturelli, NASA
Panel Discussion Speaker, AQAI Workshop

Davide Venturelli is currently Associate Director for Quantum Computing of the Research Institute of Advanced Computer Science at USRA. He works in the NASA Intelligent System Division (TI) at the Quantum AI Laboratory (QuAIL) as a senior research scientist under the NASA Academic Mission Service contract, invested in research projects dealing with quantum optimization applications and their near-term implementation in real hardware, in a hardware-software co-design approach. Recently, he is PI of a DARPA Optimization with Noisy Intermediate-Scale Quantum devices (ONISQ) project on Scheduling applications, co-PI in an NSF Expeditions project on Coherent Ising Machines, and co-lead of the quantum information ecosystem task force of the DOE Superconducting Quantum Materials and System Center, led by Fermilab. Prior to joining USRA and QuAIL, he obtained is Ph.D. at SISSA in Trieste and University of Grenoble and worked as a postdoc at the Normale School in Pisa.

Patrick Coles, Los Alamos National Laboratory (LANL)
Panel Discussion Speaker, AQAI Workshop

Patrick Coles joined Los Alamos National Laboratory (LANL) in 2017 as a staff scientist. He currently leads the near-term quantum computing group at LANL, where he focuses on fundamental issues related to quantum machine learning and noisy quantum devices. He also co-organizes LANL’s quantum computing summer school. Previously, he did postdocs at University of Waterloo, National University of Singapore, and Carnegie Mellon University. He has a broad background that includes prior work in quantum cryptography, quantum information theory, quantum foundations, and condensed matter physics. He received his PhD from University of California, Berkeley, his Master’s degree from University of Cambridge as a Churchill Fellow, and his undergraduate degree from Case Western Reserve University.

Maria Schuld, University of KwaZulu-Natal and Xanadu Quantum Computing
Panel Discussion Speaker, AQAI Workshop

Maria Schuld works as a senior researcher for the Toronto-based quantum computing start-up Xanadu, as well as for the Big Data and Informatics Flagship of the University of KwaZulu-Natal in Durban, South Africa, from which she received her PhD in theoretical physics in 2017. She co-authored the book “Supervised Learning with Quantum Computers” (Springer 2018) and is a lead developer of the PennyLane software framework for quantum differentiable programming. Besides her research on the intersection of quantum computing and machine learning, Maria has a postgraduate degree in political science, and a keen interest in the interplay between emerging technologies and society.

AQAI Workshop Agenda (Download)

Program Committee

• Prasanna Date, Oak Ridge National Laboratory – Organizing Committee Chair
• Kathleen Hamilton, Oak Ridge National Laboratory – Organizing Committee Chair
• Kadir Amasyali, Oak Ridge National Laboratory
• Kubra Aydeniz, Oak Ridge National Laboratory
• Arnab Banerjee, Purdue University
• Lukasz Cincio, Los Alamos National Laboratory
• Samudra Dasgupta, Oak Ridge National Laboratory
• Eugene Dumitrescu, Oak Ridge National Laboratory
• Pranav Gokhale, University of Chicago
• Luke Govia, Raytheon BBN Technologies
• Dmitry Liakh, Oak Ridge National Laboratory
• Susan Mniszewski, Los Alamos National Laboratory
• Gabriel Perdue, Fermilab
• Thomas Potok, Oak Ridge National Laboratory
• Sarah Powers, Oak Ridge National Laboratory

Important Dates

• Paper Submission Deadline extended: August 15, 2020
• Notification of Acceptance extended: September 15, 2020
• Workshop Date: October 12, 2020

Submission Instructions

• Format your manuscript according to the IEEE conference template. Both LaTeX and Microsoft Word templates are available.
• Make sure your manuscript is 4-6 pages in length (including references) and follows the IEEE conference template.
• Submit your manuscript using the EasyChair portal for AQAI Workshop.

Call for Papers

Current research pertaining to Quantum Artificial Intelligence (QAI) is mainly comprised of designing novel QAI algorithms or formulating existing machine learning algorithms for quantum computers. Researchers are also exploring real-world applications and use cases for NISQ-era quantum computers within the AI and machine learning space. Physicists have used AI and machine learning models for enhancing and augmenting the simulation of quantum systems. Accordingly, we have the following three sessions:

• Theory and Algorithms: Development of QAI theory, development of novel QAI algorithms, and formulation of classical AI and machine learning techniques for quantum computers.
• Applications: Near-term real-world QAI applications on NISQ-era quantum computers.
• Quantum Simulations: Use of QAI for simulating quantum systems.

Contact Us

For more information about the Applied Quantum Artificial Intelligence (AQAI) workshop, contact Prasanna Date (datepa@ornl.gov) or Kathleen Hamilton (hamiltonke@ornl.gov).