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

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

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

Quantum Machine Learning

Quantum Machine Learning and Data Analytics Workshop

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