Journal Club for Quantum Physics and Machine Learning



Next Journal Club Meeting: 13th December 2022, 6:00 pm CET.  

Speaker: Michael Goerz

Affiliation: Stanford University

Title: Quantum Optimal Control via Semi-Automatic Differentiation

Reference: arXiv:2205.15044


Abstract: We develop a framework of "semi-automatic differentiation" that combines existing gradient-based methods of quantum optimal control with automatic differentiation. The approach allows to optimize practically any computable functional and is implemented in two open source Julia packages, GRAPE.jl and Krotov.jl, part of the QuantumControl.jl framework. Our method is based on formally rewriting the optimization functional in terms of propagated states, overlaps with target states, or quantum gates. An analytical application of the chain rule then allows to separate the time propagation and the evaluation of the functional when calculating the gradient. The former can be evaluated with great efficiency via a modified GRAPE scheme. The latter is evaluated with automatic differentiation, but with a profoundly reduced complexity compared to the time propagation. Thus, our approach eliminates the prohibitive memory and runtime overhead normally associated with automatic differentiation and facilitates further advancement in quantum control by enabling the direct optimization of non-analytic functionals for quantum information and quantum metrology, especially in open quantum systems. We illustrate and benchmark the use of semi-automatic differentiation for the optimization of perfectly entangling quantum gates on superconducting qubits coupled via a shared transmission line. This includes the first direct optimization of the non-analytic gate concurrence.


Upcoming Journal Club meetings:

Date Time Speaker Affiliation Reference
13th December 2022  6:00pm CET  Michael Goerz  Stanford University Quantum Optimal Control via Semi-Automatic Differentiation


Past Journal Club Meetings:

Date Time Speaker Affiliation Reference
29th November 2022 5:30pm CET Abigail McClain Gomez Harvard University Reconstructing quantum states using basis-enhanced Born machines
15th November 2022 5:30pm CET Nicolas Sadoune University of Munich / MCQST Unsupervised Interpretable Learning of Phases From Many-Qubit
 1st November 2022  5:30pm CET Flavia Alejandra Gómez Albarracín University of La Plata  Machine learning techniques to construct detailed phase diagrams for
skyrmion system
18th October 2022 5:30pm CET Riccardo Fabbricatore Skolkovo Institute of Science and Technology Gradient
dynamics in reinforcement learning
4th October 2022   5:30pm CET  Bogdan Zviazhynski   University of Cambridge  Unveil the unseen: Exploit information hidden in noise
21st June 2022  5:30pm CET Javier Robledo Moreno Flatiron Institute / NYU

Fermionic wave functions from neural-network constrained hidden states


 7th June 2022 5:30pm CET   Francesco Carnazza   Universität Tübingen

 Machine learning Markovian quantum master equations of few-body observables in interacting spin chains

24th May 2022 5:30pm CET Niklas Käming  Universität Hamburg

 Unsupervised machine learning of topological phase transitions from experimental data


10th May 2022 5:30pm CET  Julian Arnold University of Basel

Replacing neural networks by optimal analytical predictors for the detection of phase transitions

26th April 2022 5:30pm CET Katerina Gratsea ICFO

Storage properties of a quantum perceptron 

12th April 2022 5:30pm CET  Anna Dawid  University of Warsaw / ICFO

Hessian-based toolbox for reliable and interpretable machine learning in physics


12th December 2021 5:30pm CET Jonathon Brown Queen's University Belfast Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems
7th December 2021 5:30pm CET M. Michael Denner University of Zürich Efficient learning of a one-dimensional density functional theory
23rd November 2021  5:30pm CET Robert Huang California Institute of Technology Provably efficient machine learning for quantum many-body problems
9th November 2021  5:30pm CET Paolo Andrea Erdman Freie Universität Berlin  Identifying optimal cycles in quantum thermal machines with reinforcement-learning
26th October 2021  5:30pm CET Maciej Koch-Janusz  University of Zurich /  University of Chicago Relevant operators and symmetries through the lens of real-space mutual information
12th October 2021 5:30pm CET Moritz Reh Heidelberg University Time-dependent variational principle for open quantum systems with artificial neural networks
15th June 2021 4:30pm CET Nishad Maskara Harvard University A learning algorithm with emergent scaling behavior for classifying phase transitions
1st June 2021 4:30pm CET Rouven Koch Aalto University Neural network enhanced hybrid quantum many-body dynamical distributions
18th May 2021 4:30pm CET Wei Chen PUC Rio de Janeiro A supervised learning algorithm for interacting topological insulators based on local curvature
4th May 2021 4:30pm CET Agnes Valenti ETH Zürich  Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
20th April 2021 4:30pm CET Rui Lin ETH Zürich 

Minimal model of permutation symmetry in unsupervised learning


6th April 2021 4:30pm CET Miriam Büttner University of Freiburg

Learning Potentials of Quantum Systems using Deep Neural Networks


 23rd March 2021 4:30pm CET Luuk Coopmans Trinity College Dublin/Dublin Institute for Advanced Studies Protocol Discovery for the Quantum Control of Majoranas by Differential Programming and Natural Evolution Strategies
 9th March 2021 4:30pm CET Axel Lode University of Freiburg

Interpretable Phase Detection and Classification with Persistent Homology


 23rd February 2021  4:30pm CET Evert van Nieuwenburg University of Copenhagen A NEAT Quantum Error Decoder
 9th February 2021  3pm CET Shahnawaz Ahmed  Chalmers University of  Technology Classification and reconstruction of optical quantum states with deep neural networks 
 26th January 2021  3pm CET Julian Arnold  University of Basel Interpretable and unsupervised phase classification
 12th January 2021  3pm CET Paolo Molignini  University of Cambridge

Scientific intuition inspired by machine learning generated hypotheses


Hits: 6478