Purdue University: 150 years of giant leaps; 1869-2019

Quantum Machine Learning

Quantum Machine Learning and Data Analytics Workshop

Thursday & Friday, September 5 - 6, 2019

Hall for Discovery and Learning Research (HDLR)
Purdue University, West Lafayette, Indiana

Sponsors

Integrative Data Science Initiative
Purdue Quantum Science Engineering Institute
The Chemistry, Physics, and Computer Science Departments at Purdue University
Electrical and Computer Engineering at Purdue
Discovery Park
Entanglement Institute

Organizing committee

  • Sabre Kais (Purdue)
  • Travis S. Humble (ORNL)
  • Jason Turner (Entanglement Institute)

Local committee

  • Sunil Prabhakar (CS and IDSI)
  • Pankaj Sharma (IDSI)
  • Ashraf Alam (ECE)
  • Alex Pothen (CS)
  • Yong Chen (PHYS)
  • Sabre Kais (CHM)

About

With the rapid development of quantum computers, a number of quantum algorithms have been developed and tested on both superconducting qubits based machines and ion trap hardware. Quantum machine learning is expected to be a potential application of quantum computer in the near future. Many quantum machine learning algorithms have been proposed to speed up classical machine learning by quantum computers. At the same time, deep learning has shown great power in solving real world problems. The aim of the workshop is to bring together world leading experts in this new field of quantum machine learning to discuss the recent development of quantum algorithms to perform machine learning tasks on large-scale scientific datasets for various industrial and technological applications and in solving challenging problems in science and engineering.

Important dates

Registration deadline: August 15

Abstract deadline: August 1

Invited speakers

  • Chad Rigetti (Rigetti Computing)
  • Nathan Wiebe (University of Washington)
  • Barry Sanders (University of Calgary)
  • Jacob Biamonte (Skolkovo Institute of Science and Technology)
  • Masoud Mohseni (Google)
  • Nathan Killoran (Machine Learning & Software Lead Xanadu)
  • Yudong Cao (Zapata)
  • Stephen Gray (ANL)
  • Richard Li/Daniel Lidar (USC)
  • Kathleen Hamilton (ORNL)
  • Karol Kowalski (PNNL)
  • Antonio Mezzacapo (IBM)
  • Rolando Somma (LANL)
  • Kristan Temme (IBM)

Registration

Register now

Contact

Please contact Melissa Gulick (magulick@purdue.edu) for more information about this event.

Submit

Now accepting applications for contributed talks and posters

Agenda

Thursday, September 5

Time Session Title

7:00 – 8:00am

Registration and Breakfast

 

8:00 – 8:15am

Welcome Remarks

Sabre Kais and Tomas Diaz de la Rubia

Morning Session: Travis Humble (ORNL)

8:15 – 9:15am

Jacob Biamonte (Skolkovo Inst.)

Universal Variational Quantum Computation

9:15 – 10:00am

Kristan Temme (IBM)

Quantum Machine Learning & the Prospect of Near-term Applications on Noisy Devices

10:00 – 10:30am

Coffee Break

 

10:30 – 11:15am

Nathan Weibe (Univ. of Washington)

Training Quantum Boltzmann Machines

11:15 – 12:00pm

Yudong Cao (Zapata)

Quantum Boltzmann Machine Using Eigenstate Thermalization

12:00 – 1:30pm

Lunch

 

Afternoon Session: Jason Turner (Chair-Entanglement Institute)

1:30 – 2:15pm

Kathleen Hamilton (ORNL)

Training with Gradient Estimation on NISQ Devices for Quantum Machine Learning Applications

2:15 – 3:00pm

Stephen Gray (ANL)

Supervised Quantum Machine Learning with Photonic Qudits

3:00 – 3:30pm

Coffee Break

 

3:30 – 4:15pm

Karol Kowalski

Coupled Cluster Downfolding Techniques for Quantum Computing: Dimensionality Reduction of Electronic Hamiltonians in Studies of Correlated Molecular Systems

4:15 – 5:00pm

Richard Li (USC)

Case Studies in Machine Learning Via Quantum Annealing

5:30 – 9:00pm

Dinner for invited speakers

Friday, September 6

Time Session Title

7:00 – 8:00am

Breakfast

 

Morning Session: Alex Pothen (Chair-Purdue)

8:00 – 8:15am

Sunil Prabhakar (Purdue)

Integrative Data Science Initiative

8:15 – 8:30 am

Yong Chen (Purdue)

Purdue Quantum Science Engineering Institute

8:30 – 9:30am

Masoud Mohseni (Google)

Challenges and opportunities for hybrid quantum-classical machine learning and optimization

9:30 – 10:15am

Chad Rigetti (Rigetti Computing)

TBA

10:15 – 10:45am

Coffee Break

 

10:45 – 11:30am

Barry Sanders (Univ. of Calgary)

Machine Learning for Quantum Control

11:30 – 12:15pm

Nathan Kiloran (Xanadu)

Penny Lane – Automatic Differentiation and Machine Learning of Quantum Computations

12:15 – 1:30pm

Lunch

 

Afternoon Session: Ashraf Alam (Chair-Purdue)

1:30 – 2:15pm

Antonio Mezzacapo (IBM)

Quantum Computing and Artificial Intelligence

2:15 – 3:00pm

Rolando Somma (LANL)

Quantum Algorithms for Systems of Linear Equations

3:00 - 3:15pm

Coffee Break

 

3:15 – 4:00pm

Supriyo Datta (Purdue)

p-Bits for Quantum-inspired Algorithms

4:00- 4:15pm

Sabre Kais (Purdue)

Closing remarks

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