A super cool method to set the configuration of emacs, following DistroTube
Emacs requires tweaking the
init.el file in the
.emacs.d dir in home
init.el is written in emacs-lisp and a bit obscure to understand
One can use the wonderful
emacs org-mode to code the
init.el by using an auxiliary
config.org placed in any directory
The steps are described below (generated by emacs org-mode)
In addition to set a proper configuration, it also useful to use emacs as a client, which speeds up running emacs.
This is done, first running the emacs daemon
And then any time we launch emacs we use
emacsclient -c -a 'emacs'
In Linux systems, the emacs daemon can be launched at startup by adding the service to the
systemd as detailed here
Table of Contents
1 How to configure emacs by a emacsconfig.org file
1.1 Set your init.el in .emacs.d
Write the following init.el in .emacs.d
(org-babel-load-file (expand-file-name "~/org/emacsconfig.org" user-emacs-directory))
Here emacsconfig.org is the org configuration file with its path You can set any file
In this file we have various functions
- Org Babel
Org Babel is a wonderful tool to use different languages in a single org file
Loads Emacs Lisp source blocks in the org file
Replace the file name with absolute path
Is the directory where the Emacs-specific files are placed .emacs.d typically, where the search of the file starts
1.2 Write emacsconfig.org
In the .org file for the configuration we will write different parts as pieces of emacs lisp code. For example
* FRAME SIZE #+begin_src emacs-lisp (add-to-list 'default-frame-alist '(width . 180)) (add-to-list 'default-frame-alist '(height . 90)) #+end_src * FONTSIZE #+begin_src emacs-lisp (set-face-attribute 'default (selected-frame) :height 150) #+end_src
Boson sampling solitons by quantum machine learning
We use a neural network variational ansatz to compute Gaussian quantum discrete solitons in an array of waveguides described by the quantum discrete nonlinear Schroedinger equation. By training the quantum machine learning model in the phase space, we find different quantum soliton solutions varying the number of particles and interaction strength. The use of Gaussian states enables measuring the degree of entanglement and the boson sampling patterns. We compute the probability of generating different particle pairs when varying the soliton features and unveil that bound states of discrete solitons emit correlated pairs of photons. These results may have a role in boson sampling experiments with nonlinear systems and in developing quantum processors to generate entangled many-photon nonlinear states.
Quantum machine learning and boson sampling
Training Gaussian boson sampling by quantum machine learning
published in Quantum Machine Intelligence 3, 26 (2021)
We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space. By a pullback mechanism, we model transformations due to unitary operators as linear layers that can be cascaded to simulate complex multi-particle processes. We use the layered neural networks for non-classical light propagation in random interferometers, and compute boson pattern probabilities by automatic differentiation. This is a viable strategy for training Gaussian boson sampling. We demonstrate that multi-particle events in Gaussian boson sampling can be optimized by a proper design and training of the neural network weights. The results are potentially useful to the creation of new sources and complex circuits for quantum technologies.
Code for multilevel quantum gates now available on Github
We made available our Python and TensorFlow code about machine learning design of multilevel quantum gates with reservoir computing
Machine Learning Photonics 2021 on youtube!
Lectures on our Come Lake school on Machine Learning and Photonics are now available on Youtube!
Stay tuned for the new edition next years (hopefully on site and in person!)