### Conveners

#### TS-2 Quantum Machine Learning (DTP) / Apprentissage automatique quantique (DPT)

- Aida Ahmadzadegan (Perimeter Institute)
- Achim Kempf (University of Waterloo)

In recent years the prospects of quantum machine learning and quantum deep neural network have gained notoriety in the scientific community. By combining ideas from quantum computing with machine learning methodology, quantum neural networks (QNNs) promise new ways to interpret classical and quantum data sets. However, many of the proposed quantum neural network architectures exhibit a...

Since many concepts in theoretical physics are well known to scientists in the form of equations, it is possible to identify such concepts in non-conventional applications of neural networks to physics.

In this talk we examine what is learned by convolutional neural networks, autoencoders or siamese networks in various physical domains. We find that these networks intrinsically learn physical...

In this talk, I will introduce a generalization of the earth mover's distance to the set of quantum states. The proposed distance recovers the Hamming distance for the vectors of the canonical basis, and more generally the classical earth mover's distance for quantum states diagonal in the canonical basis. I will discuss some desirable properties of this distance, including a continuity bound...

In this presentation you'll see how to use TensorFlow Quantum to conduct large scale research in QML. The presentation will be broken down into two major sections: First you will follow along as we implement and scale up (beyond the authors original size) some existing QML works from the literature in TensorFlow Quantum. We will focus on how to write effective TensorFlow Quantum code,...

Despite an undeserved reputation for being hard to understand, the mathematics behind quantum computing is based on relatively straightforward linear algebra. This means that the equations governing quantum computing are intrinsically differentiable. This simple observation has remarkable consequences. In particular, many of the tools developed over the past decades for deep learning, such as...

In the distant future we expect to be using large-scale, nearly perfect quantum computers that aid in drug discovery, break RSA encryption, and outperform supercomputers in certain machine learning tasks. Today we have access to small quantum computers afflicted by noise and error. Somewhere between these two extremes lies a momentous event for the field known as quantum advantage: solving a...

Many important challenges in science and technology can be cast as optimization problems. When viewed in a statistical physics framework, these can be tackled by simulated annealing, where a gradual cooling procedure helps search for ground state solutions of a target Hamiltonian. While powerful, simulated annealing is known to have prohibitively slow sampling dynamics when the optimization...

Control systems are vital in engineering, and machine learning is transforming data science; however, their basic constructs are expressed in terms of classical physics, which impedes generalizing to quantum control and quantum machine learning in a consistent way. We incorporate classical and quantum control and learning and their dependencies into a single conceptual framework. Then we...

Generating high-quality data (e.g. images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such tasks to potentially enhance conventional machine learning algorithms has emerged as a promising application, but poses big challenges due to the limited number of qubits and the level of gate noise in available devices....