This is an online journal club that discusses topics at the interface of machine learning and (quantum) physics.
To subscribe to the mailing list, follow this link.
The format is informal, and based on:
Oct 24
Quantum Engineering of Qudits with Interpretable Machine Learning
Presenter: Martina / Devil’s advocate: Mo, Oleksii
Oct 31 No journal club; Happy Halloween!
Nov 7
Learning interactions between Rydberg atoms
Presenter: Yifan / Devil’s advocate: Mykola
Nov 14
Accurate Computation of Quantum Excited States with Neural Networks
Presenter: Attila / Devil’s advocates: Adrien, Douglas
Nov 21
Variational subspace methods and application to improving variational Monte Carlo dynamics
Presenter: Adrien
Nov 28
Grassmann Variational Monte Carlo with neural wave functions
Presenter: Douglas
Dec 5
Adaptive Neural Quantum States: A Recurrent Neural Network Perspective
Presenter: Kedir / Devil’s advocate: Wladi, Noe
Dec 12
Neural Network-Augmented Pfaffian Wave-functions for Scalable Simulations of Interacting Fermions
Presenter: Antoine / Devil’s advocate: Adrien
Dec 19 No journal club; Merry Christmas!
Jan 9 No journal club
Jan 16
Adversarial Policies Beat Superhuman Go AIs
Presenter: Moksh / Devil’s advocate: Giovanni
Jan 23
Breaking Through Barren Plateaus: Reinforcement Learning Initializations for Deep Variational Quantum Circuits
Presenter: Giovanni / Devil’s advocate: Moksh
Jan 30
Dynamics of disordered quantum systems with two- and three-dimensional tensor networks
Presenter: Wladi / Devil’s advocate: Attila
Feb 6
Fourier Neural Operators for Time-Periodic Quantum Systems: Learning Floquet Hamiltonians, Observable Dynamics, and Operator Growth
Presenter: Noe / Devil’s advocate: Oleksii
Apr 25
Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning
Presenter: Hans / Devil’s advocate: Oleksii
May 2
Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
Presenter: Paul / Devil’s advocate: Giovanni
May 9
Artificially intelligent Maxwell’s demon for optimal control of open quantum systems
Presenter: Mo / Devil’s advocate: Patrick
May 16
Scaling the Automated Discovery of Quantum Circuits via Reinforcement Learning with Gadgets
Presenter: Giovanni / Devil’s advocate: Antoine
May 23
Graph NNs, A General Introduction 1: https://distill.pub/2021/gnn-intro/, https://gnn.seas.upenn.edu/, https://arxiv.org/abs/1806.01261
Presenter: Martina
May 30
Graph NNs applications: Gauging tensor networks with belief propagation
Presenter: Wladi / Devil’s advocate: Hans
Jun 6
Local minima in quantum systems
Presenter: Marc / Devil’s advocate: Martina
Jun 13
Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powers
Presenter: Patrick / Devil’s advocate: Wladi
Jun 20
Provably efficient machine learning for quantum many-body problems
Presenter: Oleksii / Devil’s advocate: tbd
Jun 27
A learning-based approach to characterization of open quantum systems
Presenter: Noe / Devil’s advocate: Paul
Jul 4
Foundation Neural-Network Quantum States
Presenter: Adrien / Devil’s advocate: Noe
Jul 11
Many-body dynamics with explicitly time-dependent neural quantum states
Presenter: Kadir / Devil’s advocate: Adrien
Jul 18
Efficient Optimization of Variational Autoregressive Networks with Natural Gradient
Presenter: Antoine / Devil’s advocate: Kadir
Jul 25
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation
Presenter: Rajah / Devil’s advocate: Mo
Oct 18: Learning eigenstates of quantum many-body Hamiltonians within the symmetric subspaces using neural network quantum states - Presenter: Adrien / Devil’s advocate: Ahmedeo, Ege
Oct 25: Accurate neural quantum states for interacting lattice bosons - Presenter: Viktor / Devil’s advocate: Giovanni
Nov 8: Tensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States Presenter: Hans / Devil’s advocate: Rajah
Nov 15: Approximately-symmetric neural networks for quantum spin liquids - Presenter: Rajah / Devil’s advocate: Viktor, Onurcan
Nov 22: Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function - Presenter: Ege / Devil’s advocate: Patrick
Nov 29: Vision Transformer Neural Quantum States for Impurity Models - Presenter: Ahmedeo / Devil’s advocate: Francesca, Jonas
Dec 6: Tutorial 1 Kolmogorov-Arnold networks - Presenter: Attila
Dec 13: Tutorial 2 Kolmogorov-Arnold networks - Presenter: Aleksei
Jan 10: Accurate Learning of Equivariant Quantum Systems from a Single Ground State - Presenter: Paul / Devil’s advocate: Aleksei, Adrien
Jan 17: Attention to Quantum Complexity - Presenter: Francesco / Devil’s advocate: Martina, Mo
Jan 24: Measurement-based quantum machine learning - Presenter: Fabian / Devil’s advocate: Francesco
Jan 31: The statistical mechanics and machine learning of the α-Rényi ensemble - Presenter: Francesca / Devil’s advocate: Fabian
Feb 7: Discovering Local Hidden-Variable Models for Arbitrary Multipartite Entangled States and Arbitrary Measurements - Presenter: Onurcan / Devil’s advocate: Patrick
New component this term: Besides the presenter, a devil’s advocate is assigned to each paper. The job is to pose 2-3 critical questions at the end of the presentation, in order to kick off the discussion. The advocates are listed in parenthesis below.
We meet on Fridays at 4pm (CEST; CET from November on) via zoom. Connection details are distributed via the mailing list.
Apr 26: Model-Free Quantum Control with Reinforcement Learning - Giovanni Cemin (Felix Motzoi)
May 3: Reinforcement learning decoders for fault-tolerant quantum computation - Chris Hooley (Marin Bukov)
May 10: Real-time quantum error correction beyond break-even - Manuel Guatto (??)
May 17: Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network - Adrien Kahn (Markus Schmitt)
May 24: Neural Decoder for Topological Codes - Rajah Nutakki (Giovanni Cemin)
May 31: Retrieving non-stabilizerness with Neural Networks - Wladi Krinitsin (Chris Hooley)
June 7: Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning - Francesco Preti
June 14: Neural Ordinary Differential Equations - Martina Jung (Marc Machaczek)
June 21: Fourier Neural Operator for Parametric Partial Differential Equations - Aleksei Malyshev (Fabian Ballar)
June 28: Operator Learning Renormalization Group (I) - Patrick Lenggenhager
July 5: Operator Learning Renormalization Group (II) - Jonas Rigo (Carlos Benavides)
July 12: Flow-based generative models for Markov chain Monte Carlo in lattice field theory - Ao Chen (Martina Jung)
July 19: Spin-1/2 kagome Heisenberg antiferromagnet: Machine learning discovery of the spinon pair density wave ground state - Roeland Wiersema (Rajah Nutakki)
We meet on Fridays at 4pm (CEST; CET from November on) via zoom. Connection details are distributed via the mailing list.
Oct 20: “Highly resolved spectral functions of two-dimensional systems with neural quantum states” - Markus Schmitt
Oct 27: Solving Schrödinger Equation with a Language Model - Mo Abedi
Nov 3: Backflow Transformations via Neural Networks for Quantum Many-Body Wave-Functions - Ankit Mahajan
Nov 10: Autoregressive Neural TensorNet: Bridging Neural Networks and Tensor Networks for Quantum Many-Body Simulation - Friederike Metz
Nov 17: Variational dynamics of open quantum systems in phase space - Zala Lenarcic
Nov 24: Efficient optimization of deep neural quantum states toward machine precision - Ao Chen
Dec 1: Mean-field theories are simple for neural quantum states - Fabian Ballar Trigueros
Dec 8: Diffusion models tutorial 1 - Viktor Oganesyan
Dec 15: Diffusion models tutorial 2 - Viktor Oganesyan
Jan 12: skipped
Jan 19: Quantum-Noise-driven Generative Diffusion Models - Gianluca Lagnese
Jan 26: Mutual Information, Neural Networks and the Renormalization Group - Jonas Rigo
Feb 2: Compression theory for inhomogeneous systems - Carlos
Feb 9: Mutual information of spin systems from autoregressive neural networks - Wladi Krinitisin
Feb 16: t.b.d. - Paolo Erdman