QTFT Webinars

 
Laws, Artificial Intelligence & Quantum Computing
01:34:38
QTFT

Laws, Artificial Intelligence & Quantum Computing

No prior knowledge is required. # Speakers Peerapat Chokesuwattanaskul, PhD Chulalongkorn University, Thailand Theeraphot Sriarunothai, PhD University of Siegen, Germany # Abstract In the first part, the talk will cover the bi-directional relationship between artificial intelligence and law. From the legal point of view, artificial intelligence will influence the nature and dynamic of societal structure and interaction, hence the law. Some laws will become obsolete or virtually useless. For example, most traffic laws will become totally unnecessary if the self-driving vehicles eventually become the new normal. On the contrary, existing laws will need to be revised and new laws will need to be legislated to deal with unprecedented problems. For example, considering again the self-driving vehicles, we might need to reconsider the liability and insurance system when it comes to accidental cases, let alone the ethical issues like the trolley problem. Also, an increasing influence of on human decision-making has caused a greater tension between free-will and deterministic natures of human decision. One obvious example is the Cambridge Analytica scandal in the previous US election. On the other hand, from the AI point of view, especially Machine Learning, AI can potentially change how the legal “ecosystem” works. It has increasingly been adopted by firms to analyse legal documents in various ways. Mainly, AI has been widely used to accommodate the due diligence and compliance and the case strategy and planning. We will explore how AI could be used to stimulate the accessibility to legal services of wider public and potentially help judges and officers tackle inconsistencies and biasedness in the judicial process. Certainly, some contingencies will need to be discussed such as the bias of AI. In the second part, we will explore quantum computers and how they may affect Laws. Quantum computers have great potentials to leap forward AI by their computing power and memory. These capacities will not be achievable by any classical supercomputer. We have been discussing a lot about we can do many things when we have a quantum computer. However, do we actually have a quantum computer yet? The essential criteria to build quantum computers will be discussed in this talk. Then, we will give some examples of physical quantum bits. We will further discuss two promising platforms to be a large-scale quantum computer. Lastly, we will see the current perspectives of a large-scale quantum computer.
Quantum Convolutional Neural Network
01:41:52
Integrability in Classical Mechanics
01:53:45
Modern Applications of Path Integration in Machine Learning and Quantum Control
02:09:46
QTFT

Modern Applications of Path Integration in Machine Learning and Quantum Control

In this specialized seminar, introductory talks will be given by the speakers, followed by an open discussion. The discussion will range from the basic ideas to advanced concepts related to the topics, fostering research collaboration among QTFT members. # Prerequisite A solid background in undergraduate physics. # Speakers Thiparat Chotibut, PhD Singapore University of Technology and Design Areeya Chantasri, PhD Centre for Quantum Dynamics, Australia # Abstract Feynman’s Path Integral (PI) is the celebrated mathematical reformulation of operator Quantum Mechanics. Such reformulation offers both an intuitive classical interpretation of Quantum Mechanics, and a powerful computational approach to investigate Quantum fluctuations. However, less is known about its stochastic counterparts, whose PI representations enable alternative routes to investigate stochastic phenomena. In this special QTFT seminar, we will begin by reviewing the less familiar PI representations of stochastic processes, such as the Doi-Peliti PI and the Onsager-Machlup PI, and draw connections to standard Quantum Physics. We will then discuss how these alternative views of stochastic processes may offer new tools to tackle modern Machine Learning/Theoretical Neuroscience problems, as well as Quantum control problems. In particular, through stochastic PI, we will first discuss why the spike-timing statistics in experimental neural spike-train data are typically and successfully described by an effective Poisson-like neuron model, despite the sophisticated underlying neural network architecture. The connection between our biologically plausible model of spiking neural networks and the well-known Hopfield neural network originated from statistical physics community will also be discussed. In addition, turning back to quantum problems, we will discuss how the stochastic PI can be applied to quantum systems, but now including fluctuation from measurements and decoherence. The PI provides us a convenient way to derive optimal paths for qubit evolution, which could be useful for problems in quantum control.