Physics

Isometric tensor networks (isoTNS) form a subclass of tensor network states that have an additional isometric condition, which implies that they can be efficiently prepared with a linear-depth quantum circuit. In this work, we introduce a procedure to construct isoTNS encoding of certain 2D classical partition functions. By continuously tuning a parameter in the isoTNS, the many-body ground state undergoes quantum phase transitions, exhibiting distinct 2D topological order. We illustrate this by constructing an isoTNS path with bond dimension $D = 2$ interpolating between distinct symmetry-enriched topological (SET) phases. At the transition point, the isoTNS wavefunction is related to a gapless point in the classical six-vertex model. Furthermore, the critical wavefunction supports a power-law correlation along one spatial direction while remains long-range ordered in the other spatial direction. We provide an exact linear-depth parametrized local quantum circuit that realizes the path. The above features can therefore be efficiently realized on a programmable quantum device. In the second part of my talk, I will show how to discover efficiently measurable order parameters for quantum phases using model-independent training of quantum circuit classifiers. The possibility of the efficient realization of phase transition path is useful for benchmarking quantum phase recognition methods in higher than one dimension.

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Zoom link https://pitp.zoom.us/j/93183360141?pwd=RVdYeUxUbE1aZ1dUbzRSL3lBb0lHZz09

Despite their simple intuition, convolutions are more tedious to analyze than dense layers, which complicates the transfer of theoretical and algorithmic ideas. We provide a simplifying perspective onto convolutions through tensor networks (TNs) which allow reasoning about the underlying tensor multiplications by drawing diagrams, and manipulating them to perform function transformations and sub-tensor access. We demonstrate this expressive power by deriving the diagrams of various autodiff operations and popular approximations of second-order information with full hyper-parameter support, batching, channel groups, and generalization to arbitrary convolution dimensions. Further, we provide convolution-specific transformations based on the connectivity pattern which allow to re-wire and simplify diagrams before evaluation. Finally, we probe computational performance, relying on established machinery for efficient TN contraction. Our TN implementation speeds up a recently-proposed KFAC variant up to 4.5x and enables new hardware-efficient tensor dropout for approximate backpropagation.

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Zoom link https://pitp.zoom.us/j/99090845943?pwd=NHBNVTNnbDNSOGNSVzNGS21xcllFdz09

Abstract TBA

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Zoom link https://pitp.zoom.us/j/98123499206?pwd=QWlHcFBJWE8zRTlyT1A5WUVsQS9NUT09

Atmospheric characterisation of habitable-zone exoplanets is a major frontier of exoplanet science. The detection of atmospheric signatures of habitable Earth-like exoplanets is challenging due to their small planet-star size contrast and thin atmospheres with high mean molecular weight. Recently, a new class of habitable sub-Neptune exoplanets, called Hycean worlds, have been proposed, which are expected to be temperate ocean-covered worlds with H2-rich atmospheres. Their large sizes and extended atmospheres, compared to rocky planets of the same mass, make Hycean worlds significantly more accessible to atmospheric spectroscopy. Several temperate Sub-Neptunes have been identified in recent studies as candidate Hycean worlds orbiting nearby M dwarfs that make them highly conducive for transmission spectroscopy with JWST. Recently, we reported the first JWST spectrum of a possible Hycean world, K2-18 b, with detections of multiple carbon-bearing molecules in its atmosphere. In this talk, we will present constraints on the atmospheric composition of K2-18 b and on the temperature structure, clouds/hazes, atmospheric extent, chemical disequilibrium and the possibility of a habitable ocean underneath the atmosphere. We will discuss new observational and theoretical developments in the characterisation of candidate Hycean worlds, and their potential for habitability. Our findings demonstrate the unprecedented potential of JWST for characterising Hycean worlds, and temperate sub-Neptunes in general, and open a new era of atmospheric characterisation of habitable-zone exoplanets with JWST.

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Zoom link https://pitp.zoom.us/j/98012554989?pwd=b0pCYkIvYmd2Y2hueUExQXBNVG8vZz09

In quantum metrology, one of the major applications of quantum technologies, the ultimate precision of estimating an unknown parameter is often stated in terms of the Cramér-Rao bound. Yet, the latter is no longer guaranteed to carry an operational meaning in the regime where few measurement samples are obtained. We instead propose to quantify the quality of a metrology protocol by the probability of obtaining an estimate with a given accuracy. This approach, which we refer to as probably approximately correct (PAC) metrology, ensures operational significance in the finite-sample regime. The accuracy guarantees hold for any value of the unknown parameter, unlike the Cramér-Rao bound which assumes it is approximately known. We establish a strong connection to multi-hypothesis testing with quantum states, which allows us to derive an analogue of the Cramér-Rao bound which contains explicit corrections relevant to the finite-sample regime. We further study the asymptotic behavior of the success probability of the estimation procedure for many copies of the state and apply our framework to the example task of phase estimation with an ensemble of spin-1/2 particles. Overall, our operational approach allows the study of quantum metrology in the finite-sample regime and opens up a plethora of new avenues for research at the interface of quantum information theory and quantum metrology. TL;DR: In this talk, I will motivate why the Cramér-Rao bound might not always be the tool of choice to quantify the ultimate precision attainable in a quantum metrology task and give a (hopefully) intuitive introduction of how we propose to instead quantify it in a way that is valid in the single- and few-shot settings. We will together unearth a strong connection to quantum multi-hypothesis testing and conclude that there are many exiting and fundamental open questions in single-shot metrology!

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Zoom link https://pitp.zoom.us/j/92247273192?pwd=ZkprOFZ0eEdQYjJDY1hneFNLckFDZz09

Quantum state tomography (QST) is the art of reconstructing an unknown quantum state through measurements. It is a key primitive for developing quantum technologies. Neural network quantum state tomography (NNQST), which aims to reconstruct the quantum state via a neural network ansatz, is often implemented via a basis-dependent cross-entropy loss function. State-of-the-art implementations of NNQST are often restricted to characterizing a particular subclass of states, to avoid an exponential growth in the number of required measurement settings. In this talk, I will discuss an alternative neural-network-based QST protocol that uses shadow-estimated infidelity as the loss function, named “neural-shadow quantum state tomography” (NSQST). After introducing NNQST and the classical shadow formalism, I will present numerical results on the advantage of NSQST over NNQST at learning the relative phases, NSQST’s noise robustness, and NSQST’s advantage over direct shadow estimation. I will also briefly discuss the future prospects of the protocol with different variational ansatz and randomized measurements, as well as its experimental feasibility.

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Zoom link https://pitp.zoom.us/j/94167105773?pwd=TXR3TUtwNjV4VFB4SEpvTkhqd29SUT09

Long-range entangled quantum matter encompasses a wealth of fascinating phenomena including fractionalization and criticality.  I will show how quantum dynamics involving measurements can both enable new kinds of long-range entangled states and facilitate their realization on quantum simulators.  In the first part, I will illustrate how competing measurements along with unitary time evolution can give rise to distinct universality classes of non-equilibrium criticality.  In the second part, I will show how measurements and unitary evolution conditioned on the measurement outcomes (“adaptive quantum circuits”) enable efficient preparation of long-range entangled matter.  Finally, I will demonstrate how environmental measurement (decoherence) can remarkably enrich quantum critical pure states, giving rise to renormalization group flows between quantum channels with important implications on the entanglement structure of the resulting critical mixed states.  

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Zoom link https://pitp.zoom.us/j/97087115629?pwd=NHdlWG9oM0xHUDIzU05sUWRKdWlFdz09

Learn more about the benefits of creating an accessible environment and how you play a part. Dr. Mahadeo Sukhai and Ainsley Latour are scientists, researchers, educators and IDEA professionals, who are passionate about and committed to inclusion in the scientific research and training enterprise. 

• Learning Objective 1 – provide an understanding of the “leaky funnel” in STEM training for persons with disabilities
• Learning Objective 2 – provide an overview of the principles underpinning a culture of accessibility and inclusion in the sciences
• Learning Objective 3 – provide an appreciation for the importance of accessibility in scientific conferences and publications

Dr. Mahadeo A. Sukhai (He/Him), Ph.D. is the world’s first congenitally blind geneticist. Dr. Sukhai is Vice-President Research & International Affairs and Chief Accessibility Officer for the CNIB (Canadian National Institute for the Blind), having previously served as a researcher in cancer genomics at the University Health Network in Toronto. Dr. Sukhai also holds an adjunct faculty appointment in the Department of Ophthalmology, School of Medicine, Queens University (Kingston, ON, Canada), as well as additional Adjunct roles in the Faculty of Business and Information Technology at Ontario Tech University and in the Inclusive Design Program at OCAD University. In his role at CNIB, Dr. Sukhai is responsible for organizational employee culture-building strategy related to inclusion, accessibility and employee wellness. Dr. Sukhai is the Principal Investigator for "Creating a Culture of Accessibility in the Sciences," a book based on his ground-breaking work on access to science within higher education, and serves as the principal investigator for national projects to examine accessibility and inclusion within science education and healthcare. Dr. Sukhai co-founded IDEA-STEM, and INOVA, the international Network of researchers with Visual impairments and their Allies, a new professional society with the mission to improve accessibility and inclusion in the biomedical sciences for researchers with vision loss. Dr. Sukhai is the External Co-Chair for the Canadian Institutes of Health Research External Advisory Committee on Accessibility and Systemic Ableism and the Chair of the Employment Technical Committee for Accessibility Standards Canada.

Ms. Ainsley R. Latour (She/Her), B.Ed., MLT, M.Sc. is the president and co-founder of IDEA-STEM, an organization created to enhance the participation and inclusion for people with disabilities in STEM.  She identifies as hard of hearing and neurodiverse.  She also serves on the Government of Ontario’s AODA Post-Secondary Education Standards Development Committee. Ainsley's work on the experience of students with disabilities in Canada has been presented at national and international conferences on science and disability, including SciAccess 2019 and 2020, the ISLAND 2020 conference, and the American Association for the Advancement of Science (2018, 2019 and 2021).  She also maintains a practice as a licensed cytogenetic and molecular genetic technologist (MLT).  She holds two undergraduate degrees, two graduate diplomas and will graduate soon with a masters in marine environmental genetics from the Memorial University of Newfoundland.

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Zoom link:  https://pitp.zoom.us/j/99867497693?pwd=ZDRkdE44dVBWRDdtS3J3ZzFOMFlHZz09

This session will introduce the grad student seminar series, include an interactive session focusing on thoughts and ideas for effective preparation and presentation, and will gauge students' interest about upcoming science outreach activities.

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Zoom link https://pitp.zoom.us/j/95397824623?pwd=LzViSVBpTXJzcjZXbjlhdzZKMk9Ndz09

Numerical simulations of collision events within the ATLAS experiment have played a pivotal role in shaping the design of future experiments and analyzing ongoing ones. However, the quest for accuracy in describing Large Hadron Collider (LHC) collisions comes at an imposing computational cost, with projections estimating the need for millions of CPU-years annually during the High Luminosity LHC (HL-LHC) run. Simulating a single LHC event with Geant4 currently devours around 1000 CPU seconds, with calorimeter simulations imposing substantial computational demands. To address this challenge, we propose a Quantum-Assisted deep generative model. Our model marries a variational autoencoder (VAE) on the exterior with a Restricted Boltzmann Machine (RBM) in the latent space, delivering enhanced expressiveness compared to conventional VAEs. The RBM nodes and connections are meticulously engineered to enable the use of qubits and couplers on D-Wave's Pegasus Quantum Annealer. We also provide preliminary insights into the requisite infrastructure for large-scale deployment.

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Zoom link https://pitp.zoom.us/j/97724484247?pwd=Witua1lKcHlrc3JDNHNDWXpHYkVvQT09

How did the universe begin? How did it evolve to what we see now?

There was a time when few people believed such questions could even be posed in scientific terms. Now, as increasingly precise instruments deliver their treasure trove of data, the answers may be within reach.

On Wednesday, October 25, Perimeter Director Emeritus Neil Turok will tackle this intriguing topic in a Perimeter Institute Public Lecture, “Secrets of the Universe: Hiding in Plain Sight?”

The performance of neural networks like large language models (LLMs) is governed by "scaling laws": the error of the network, averaged across the whole dataset, drops as a power law in the number of network parameters and the amount of data the network was trained on. While the mean error drops smoothly and predictably, scaled up LLMs seem to have qualitatively different (emergent) capabilities than smaller versions when one evaluates them at specific tasks. So how does scaling change what neural networks learn? We propose the "quantization model" of neural scaling, where smooth power laws in mean loss are understood as averaging over many small discrete jumps in network performance. Inspired by Max Planck's assumption in 1900 that energy is quantized, we make the assumption that the knowledge or skills that networks must learn are quantized, coming in discrete chunks which we call "quanta". In our model, neural networks can be understand as being implicitly a large number of modules, and scaling simply adds modules to the network. In this talk, I will discuss evidence for and against this hypothesis, its implications for interpretability and for further scaling, and how it fits in with a broader vision for a "science of deep learning".

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Zoom link https://pitp.zoom.us/j/93886741739?pwd=NzJrcTBNS2xEUUhXajgyak94LzVvdz09