Science Week - 2019

Session

Data Science and Quantum Computing Workshop

23 Aug 2019, 08:30

Auditorium (TRIUMF)

Conveners

Data Science and Quantum Computing Workshop: Hands-on Session: Machine Learning

• Wojciech Fedorko (TRIUMF)

Data Science and Quantum Computing Workshop: Hands-on Session: Quantum Computing

• Olivia Di Matteo (TRIUMF)

Data Science and Quantum Computing Workshop

• Olivia Di Matteo (TRIUMF)
• Wojciech Fedorko (TRIUMF)

Data Science and Quantum Computing Workshop

• Olivia Di Matteo (TRIUMF)
• Wojciech Fedorko (TRIUMF)

Presentation materials

slides

participants_handles.txt

42. Hands on Machine Learning session

Dr Wojciech Fedorko (TRIUMF)

23/08/2019, 08:30

Data Science and Quantum Computing Workshop

We will develop a deep learning solution for event classification in a partice physics experiment

43. Hands-on Session: Developing Quantum Applications in Q#

Dr Christopher Granade (Microsoft)

23/08/2019, 11:00

In this workshop, participants will learn to use the Quantum Development Kit and the Q# programming language to develop quantum applications and test them using simulators. The workshop consists of a short lecture and a series of hands-on exercises, covering a wide variety of tasks and concepts. No prior software installation needed to participate, all materials are available online.

41. Deep Learning Applications to Medical Imaging

Dr Ghassan Hamarneh

23/08/2019, 13:30

Medical imaging has revolutionized medicine. Now medical imaging itself is witnessing a deep learning revolution. Clinically-relevant medical image interpretation tasks (e.g. image segmentation and image classification) have been re-formulated under a deep learning framework with impressive results. These early successes have been attributed to three factors: data, learning algorithms, and...

45. Quantum Variational Autoencoders and their applications

Dr Hossein Sadeghi (D-Wave)

23/08/2019, 14:00

Generative models are among the most promising approaches toward understanding unlabelled data. They have a wide range of applications in structured prediction, molecular & material design, image analysis, speech synthesis, and computer vision. They pair with supervised learning models to help perform ML tasks when labelling data is expensive or labels are only available in a different domain....

46. Enhancing quantum simulators with neural networks

Dr Giacomo Torlai (Flatiron Institute)

23/08/2019, 14:30

The recent advances in qubit manufacturing and coherent control of synthetic quantum matter are leading to a new generation of intermediate scale quantum hardware, with promising progress towards scalable simulation of quantum matter and materials. In order to enhance the capabilities of this class of quantum devices, some of the more arduous experimental tasks can be off-loaded to classical...

47. Quantum Algorithms for Solving Dynamic Programming Problems

Dr Pooya Ronagh (1QBit, IQC, UW)

23/08/2019, 15:00

We introduce quantum algorithms for solving finite-horizon and infinite-horizon dynamic programming problems. We visit the query complexity lower bounds for classical randomized algorithms for the same tasks and consequently demonstrate a polynomial separation between the query complexity of our quantum algorithms and best-case query complexity of classical randomized algorithms. Up to...

44. Projects at TRIUMF

Dr Wojciech Fedorko (TRIUMF)

23/08/2019, 16:00

A foreword on Machine Learning projects started at TRIUMF

63. Variational AutoEncoders (VAEs) for water Cherenkov detectors

Mr Abhishek Kajal (TRIUMF/University of Manitoba)

23/08/2019, 16:03

Data Science and Quantum Computing Workshop

A Variational AutoEncoder (VAE) is a generative method used to approximate the probability distribution of processes in very high dimensional spaces. We apply VAEs for generative modelling of Water Cherenkov detectors which are used to perform precision measurements on neutrinos. In this talk, I will discuss the steps and challenges in applying VAEs to simulated neutrino events in the proposed...

64. Extrapolating Nuclear Many-Body Calculations with Constrained Gaussian Processes

Mr Peter Gysbers

23/08/2019, 16:21

The properties of nuclei can be computed from first principles starting from realistic interactions between nucleons. Using suitable basis functions, the many-body wavefunction is found by diagonalizing a Hamiltonian matrix (i.e. solving the Schrödinger equation). Due to limited computational resources only a finite basis size can be used. This is frequently insufficient for complete...

48. Expressive Structured Representations in Deep Generative Models

Dr Andreas Lehrmann

23/08/2019, 16:40

Deep generative models, such as variational autoencoders and generative adversarial networks, are among the most exciting recent developments in machine learning. Variational autoencoders, in particular, have seen a tremendous rise in popularity due to their principled variational framework and powerful neural approximations to previously infeasible inference tasks, including marginal and...

49. Solving Technological Challenges with the Fujitsu Digital Annealer

Mr Jeffrey English (Fujitsu), Mr Tadayoshi Ozaki (Fujitsu)

23/08/2019, 17:05

Data Science and Quantum Computing Workshop

The Fujitsu Digital Annealer is a new technology that is used to solve large-scale combinatorial optimization problems instantly. The Digital Annealer uses a digital circuit design and can solve problems which are intractable for classical computers. In this workshop, we introduce how the Digital Annealer works for solving combinatorial optimization problems with use cases drawn from...