Events

Math/CS Seminar - Vahid Asadi from IQC

In this talk, we study the problem of designing worst-case to average-case reductions for quantum algorithms. For all linear problems, we provide an explicit and efficient transformation of quantum algorithms that are only correct on a small (even sub-constant) fraction of their inputs into ones that are correct on all inputs. This stands in contrast to the classical setting, where such results are only known for a small number of specific problems or restricted computational models. En route, we obtain a tight Ω(n^2) lower bound on the average-case quantum query complexity of the Matrix-Vector Multiplication problem...

So you want to build a satellite?

Are you curious about the Quantum Encryption and Science Satellite mission, also known as QEYSSat? Are you wondering "Why put quantum in space"? Or perhaps you are curious to know what it takes to put quantum hardware in space? In this talk, we will discuss why it is advantageous to have quantum in space. We will also explore the various design challenges that need to be considered for space hardware. Finally, we will discuss the history of quantum space activities at the Institute for Quantum Computing, particularly QEYSSat, which is a joint project between the Canadian Space Agency (CSA) and the University of Waterloo.

CS/Math Seminar - Luowen Qian, Boston University in person and on ZOOM

Pseudorandom states (PRS) are efficiently constructible states that are effectively Haar random against efficient observers. Recently, this notion has found its influence in many fields like quantum information, quantum gravity, and quantum complexity. ...

Roger Melko: Language models for quantum simulation

Abstract: As the frontiers of artificial intelligence advance more rapidly than ever before, generative language models like ChatGPT are poised to unleash vast economic and social transformation. In addition to their remarkable performance on typical language tasks (such as writing undergraduate research papers), language models are being rapidly adopted as powerful ansatze states for quantum many-body systems.  In this talk, I will discuss the use of language models for learning quantum states realized in experimental Rydberg atom arrays. By combining variational optimization with data-driven learning using qubit projective measurements, I will show how language models are poised to become one of the most powerful computational tools in our arsenal for the design and characterization of quantum simulators and computers.

The Quantum Shorts festival celebrates independent short films inspired by quantum mechanics. Using ideas like the uncertainty principle and many-worlds interpretation, filmmakers from around the world have created five-minute wonders.

CS/Math Seminar - Daochen Wang QuICS, UMD

The divide-and-conquer framework, used extensively in classical algorithm design, recursively breaks a problem into smaller subproblems, along with some auxiliary work, to give a recurrence relation for the classical complexity. We describe a quantum divide-and-conquer framework that, in certain cases, yields quantum speedup through an analogous recurrence relation for the quantum query complexity....

Fabien Lefebvre, University of Ottawa

Modern machine learning tools have become a great method for simulating quantum systems. The recurrent neural network (RNN) has proven to be a powerful tool able to simulate quantum many-body systems. Numerical tools such as the RNN can provide insight on quantum systems using approximative simulation methods that run efficiently on conventional computers.

Join us for Quantum Today, where we sit down with researchers from the University of Waterloo’s Institute for Quantum Computing (IQC) to talk about their work, its impact and where their research may lead.

Learning in the Quantum Universe

Abstract: I will present recent progress in building a rigorous theory to understand how scientists, machines, and future quantum computers could learn models of our quantum universe. The talk will begin with an experimentally feasible procedure for converting a quantum many-body system into a succinct classical description of the system, its classical shadow. Classical shadows can be applied to efficiently predict many properties of interest, including expectation values of local observables and few-body correlation functions.

I will then build on the classical shadow formalism to answer two fundamental questions at the intersection of machine learning and quantum physics: Can classical machines learn to solve challenging problems in quantum physics? And can quantum machines learn exponentially faster than classical machines?

Bio: Hsin-Yuan (Robert) Huang is a Ph.D. student at Caltech, advised by John Preskill and Thomas Vidick. His research focuses on understanding how the theory of learning can provide new insights into physics, information, and quantum computing. His notable works include classical shadow tomography for learning large-scale quantum systems, provably efficient machine learning algorithms for solving quantum many-body problems, and quantum advantages in learning from experiments.

He has been awarded a Google Ph.D. fellowship, the Quantum Creator Prize, MediaTek research young scholarship, and the Kortschak scholarship.

Follow the link to attend this seminar on Zoom.

Please note: for the passcode, please email Joe Petrik no later than 10 a.m. day of.

Improved diagnostics and implementation for quantum error correction