Past Quantum Systems Seminars
- Date and time: September 28, Wednesday, 3-4 PM (AEST)
Title: Linking many-body physics to many-time physics: Characterising micro and macro features of non-Markovian quantum processes
Speaker: Kavan Modi, Director, Centre for Quantum Technology, Transport for New South Wales, A/Prof, School of Physics and Astronomy, Monash University
Recording: https://webcast.csiro.au/sharevideo/e9989533-6a6c-4eaf-b98e-940ac6403697
Slides:
Abstract: A classical stochastic process is a joint probability distribution of a random variable over time. Its quantum generalisation then turns out to be a multipartite density matrix, which retain exotic quantum features, e.g. quantum entanglement. We refer to the studies of such density matrices as many-time physics in analogy to the well-founded field of many-body physics. Here, we report a set of tools that allows us to characterise both the detailed features of a quantum process, as well as its coarse structures. The former, we show, could be used to tame correlated noise due to a quantum stochastic process. We implement these tools on NISQ devices and display their high efficacy in managing real noise. We also show the complex temporal entanglement structures that noise possesses in real devices. The complex coarse structure of temporal entanglement leads to exotic features such as new dynamical phases for quantum processes.
Biography: Kavan Modi received his PhD in physics from the University of Texas in Austin in 2008. After this, he worked as a research fellow at the Centre for Quantum Technologies in Singapore and Clarendon Labs at Oxford University. His research focuses on many facets of complex dynamics, which includes practical noise management and error correction in quantum computers plagued with complex noise, quantifying the computational complexity of quantum processes, and the resolving foundational questions of what makes a quantum process chaotic and leads to equilibration, Markovianisation, and thermalisation. He continues to lead a programme on this front Monash University, where he has been on the faculty since 2014. As of Aug 2022, he was appointed as the first the Director of Centre for Quantum Technology at Transport for New South Wales. Here, he will look to solve logistical and optimisation problem faced in transport using quantum computers.
- Date and time: August 31, Wednesday, 3-4 PM (AEST)
Title: Applications at the intersection of machine learning and quantum computing
Presenter: Dr Muhammad Usman (Team Leader Quantum Technologies, Data61/CSIRO)
Recording: https://webcast.csiro.au/#/videos/b1d18da8-20d9-4fd3-9347-02b7a2b64671
Slides:
Abstract: Quantum computing is an emerging paradigm of computing for computationally intensive problems which are currently intractable on classical computing platforms. It is believed that within the next decade, quantum computing will have disruptive impact in many areas of development including materials design, data science and machine learning, health, climate science, and combinatorial optimisation. Among these, machine learning is widely considered as the recipient of early quantum advantage. In this talk, I will discuss recent research at the intersection of machine learning and quantum computing. By presenting a few examples from our recent work, I will establish that the integration of quantum computing and machine learning has the potential to benefit both fields with opportunities for novel applications within the next few years.
Bio: Dr Muhammad Usman joined DATA61/CSIRO in 2022 as a Team Leader Quantum Technologies and Principal Research Scientist. Previously working as part of the Center for Quantum Computation and Communication Technology at the University of Melbourne since 2014, his research made ground-breaking contributions towards the design and characterisation of silicon quantum devices and quantum processor architecture. Since 2018, the focus of his work has shifted towards quantum software and algorithms for benchmarking near-term quantum computers. As part of this program at the University of Melbourne, he has been developing applications for near-term quantum devices such as quantum machine learning, quantum data science, and quantum optimisation. Dr Usman received his Ph.D. in Electrical and Computer Engineering from Purdue University, Indiana USA in 2010. He is a recipient of several awards including 2020 Rising Stars List in Computational Material Science from Elsevier and 2019 Best Researcher Award at the University of Melbourne. He has received prestigious research fellowships including USA Fulbright Fellowship in 2005 and German DAAD Fellowship in 2010. He is a member of the executive editorial board of the Institute of Physics journal IOP Nano Futures.