1 Missing project proposals

Several students have not submitted project proposals. It will be difficult to pass the course w/o a project. Projects will be due on the last class day; there will be no extensions.

There are many missing homeworks. I can take late homeworks up through the end of this week - email me to let you submit.

2 Quantum machine learning

1. Quantum Machine Learning (1:14:13), Jun 30, 2016.

   A special lecture entitled " Quantum Machine Learning " by Seth Lloyd from the Massachusetts Institute of Technology, Cambridge, USA.

2. SymCorrel2021 Quantum Machine-Learning for Electronic Structure Calculations , (37:55), Oct 8, 2021.

   Sabre Kais (Purdue University) - Quantum Machine-Learning for Electronic Structure Calculations

   Quantum machine learning algorithms have emerged to be a promising alternative to their classical counterparts as they leverage the power of quantum computers. In this talk, I will present our developed quantum algorithm that can be used to obtain accurate ground and excited states for molecules and two-dimensional materials. Our technique is based on a shallow neural network encoding the desired state of the system with the amplitude computed by sampling the Gibbs- Boltzmann distribution using a quantum circuit with the resource requirements of our algorithm is strictly quadratic. Then, I will show our implementation of the developed algorithm on the actual IBM-Q quantum devices. The results of the quantum simulations for simple molecules and two-dimensional materials such as graphene and transition metal- dichalcogenides are in good agreement with the results procured from conventional electronic structure calculations. The quantum machine learning approach is general and can be used to calculate band structure different materials.

3. The Future of Quantum Machine Learning, (1:33:54) Jul 23, 2021

   To commence our second annual Qiskit Global Summer School attended by over 5,000 students, in over 110 countries, we will be hosting a panel discussion regarding the future of quantum machine learning.

   00:00 Starting Soon 05:18 Commencement 14:55 Panel

   Our panelists will discuss what the current state of quantum machine learning is and where they believe the most promising applications will arise. They will also address some of the major concerns surrounding quantum machine learning, such as whether or not its potential will be reached by painting a picture of the field's landscape, and elaborating on what still needs to be achieved.

   The panel will include quantum experts such as: Ewin Tang, Quantum Algorithms, University of Washington; Maria Schuld, Senior Researcher and Quantum Software Developer, Xanadu; Aram Harrow, Associate Professor of Physics, MIT; Kristan Temme, QC Researcher, IBM Quantum

   Moderated by Amira Abbas, IBM Quantum