Research at the intersection of quantum physics and artificial intelligence is rapidly growing in academia and industry. This one-week mini-course will introduce a selection of computational methods currently being applied in quantum industry settings and will highlight career opportunities outside of academia for students and researchers with a background in quantum theory and computational physics. The course will consist of two lecture series: one on generative modeling (including restricted Boltzmann machines, neural autoregressive distribution estimators, and recurrent neural networks) by Perimeter researchers Roger Melko and Mohamed Hibat Allah, and another lecture series on tensor networks and quantum algorithms by Martin Ganahl of SandboxAQ. Afternoons will include coding tutorials, workshops, talks from speakers who have transitioned from academia to quantum industry, and career networking opportunities.
Confirmed guests for the Industry networking session are:
- 1QBit
- Agnostiq
- Amazon Web Services
- IBM Quantum
- Nord Quantique
- Quantum Valley Ideas Laboratories
- SandboxAQ
- Xanadu
- YiyaniQ
- ZebraKet
This mini-course will assume that participants have the following prerequisites:
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Graduate-level knowledge of quantum mechanics (including wavefunctions, single-body quantum mechanics, Hamiltonians, density matrices, time evolution, and angular momentum) and statistical mechanics (including partition functions, the Ising model, and phase transitions),
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Knowledge of introductory machine learning methods (see, for instance, Lectures 1-6 of Perimeter’s course on Machine Learning for Many-Body Physics), and
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Basic programming skills in Python.