Thursday & Friday, September 5 - 6, 2019
Hall for Discovery and Learning Research (HDLR)
Purdue University, West Lafayette, Indiana
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
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.
Registration deadline: August 15
Abstract deadline: August 1
Please contact Melissa Gulick (magulick@purdue.edu) for more information about this event.
Now accepting applications for contributed talks and posters
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 |
Arnab Banerjee (Purdue) |
Quantum Annealing the Ising Shastry-Sutherland Hamiltonian |
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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