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Artificial General Intelligence and the Future of Physics
Adam Brown - Stanford University
Hunting New Physics in the Dark Universe
Elena Pinetti - Fermi National Accelerator Laboratory (Fermilab)
Spin Ruijsenaars-Schneider models are Coulomb branches
Lukas Hardi - Universität Hamburg
Harnessing information from higher order statistics in cosmology - k-nearest neighbor (kNN) distributions
Arka Banerjee - Indian Institute of Science Education and Research Pune
The reactivity of quantum experiments
Thomas Schuster - California Institute of Technology (Caltech)
Glassy and Correlated Phases of Optimal Quantum Control
Marin Bukov - University of California, Berkeley
Modern Machine Learning (ML) relies on cost function optimization to train model parameters. The non-convexity of cost function landscapes results in the emergence of local minima in which state-of-the-art gradient descent optimizers get stuck. Similarly, in modern Quantum Control (QC), a key to…
Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes
Yehua Liu - University of Sherbrooke
Belief-propagation (BP) decoders are responsible for the success of many modern coding schemes. While many classical coding schemes have been generalized to the quantum setting, the corresponding BP decoders are flawed by design in this setting. Inspired by an exact mapping between BP and deep…
Operational quantum tomography
Olivia Di Matteo - Xanadu Quantum Technologies (Canada)
As quantum processors become increasingly refined, benchmarking them in useful ways becomes a critical topic. Traditional approaches to quantum tomography, such as state tomography, suffer from self-consistency problems, requiring either perfectly pre-calibrated operations or measurements. This…
Machine learning phase discovery in quantum gas microscope images
Ehsan Khatami - San Jose State University
Site resolution in quantum gas microscopes for ultracold atoms in optical lattices have transformed quantum simulations of many-body Hamiltonians. Statistical analysis of atomic snapshots can produce expectation values for various charge and spin correlation functions and have led to new discoveries…
Machine Learning Physics: From Quantum Mechanics to Holographic Geometry
Yi-Zhuang You - University of California, San Diego
Inspired by the "third wave" of artificial intelligence (AI), machine learning has found rapid applications in various topics of physics research. Perhaps one of the most ambitious goals of machine learning physics is to develop novel approaches that ultimately allows AI to discover new concepts and…
Attention is all you get
Paul Ginsparg - Cornell University
For the past decade, there has been a new major architectural fad in deep learning every year or two. One such fad for the past two years has been the transformer model, an implementation of the attention method which has superseded RNNs in most sequence learning applications. I'll give an overview…
Deep learning and density functional theory
Isaac Tamblyn - University of Ottawa
Density functional theory is a widely used electronic structure method for simulating and designing nanoscale systems based on first principles. I will outline our recent efforts to improve density functionals using deep learning. Improvement would mean achieving higher accuracy, better scaling…
Machine learning ground-state energies and many-body wave function
Sebastiano Pilati - University of Camerino
In the first part of this presentation, I will present supervised machine-learning studies of the low-lying energy levels of disordered quantum systems. We address single-particle continuous-space models that describe cold-atoms in speckle disorder, and also 1D quantum Ising glasses. Our results…
Quantum machine learning and the prospect of near-term applications on noisy devices
Kristan Temme - IBM (United States)
Prospective near-term applications of early quantum devices rely on accurate estimates of expectation values to become relevant. Decoherence and gate errors lead to wrong estimates. This problem was, at least in theory, remedied with the advent of quantum error correction. However, the overhead that…
Vulnerability of quantum systems to adversarial perturbations
High-dimensional quantum systems are vital for quantum technologies and are essential in demonstrating practical quantum advantage in quantum computing, simulation and sensing. Since dimensionality grows exponentially with the number of qubits, the potential power of noisy intermediate-scale quantum…
Optimizing Quantum Optimization
Stefan Leichenauer - Alphabet (United States)
Variational algorithms for a gate-based quantum computer, like the QAOA, prescribe a fixed circuit ansatz --- up to a set of continuous parameters --- that is designed to find a low-energy state of a given target Hamiltonian. After reviewing the relevant aspects of the QAOA, I will describe attempts…