All-Optical Scalable Spatial Coherent Ising Machine

Networks of optical oscillators simulating coupled Ising spins have been recently proposed as a heuristic platform to solve hard optimization problems. These networks, called coherent Ising machines (CIMs), exploit the fact that the collective nonlinear dynamics of coupled oscillators can drive the system close to the global minimum of the classical Ising Hamiltonian, encoded in the coupling matrix of the network. To date, realizations of large-scale CIMs have been demonstrated using hybrid optical-electronic setups, where optical oscillators simulating different spins are subject to electronic feedback mechanisms emulating their mutual interaction. While the optical evolution ensures an ultrafast computation, the electronic coupling represents a bottleneck that causes the computational time to severely depend on the system size. Here, we propose an all-optical scalable CIM with fully programmable coupling. Our setup consists of an optical parametric amplifier with a spatial light modulator (SLM) within the parametric cavity. The spin variables are encoded in the binary phases of the optical wave front of the signal beam at different spatial points, defined by the pixels of the SLM. We first discuss how different coupling topologies can be achieved by different configurations of the SLM, and then benchmark our setup with a numerical simulation that mimics the dynamics of the proposed machine. In our proposal, both the spin dynamics and the coupling are fully performed in parallel, paving the way towards the realization of size-independent ultrafast optical hardware for large-scale computation purposes.

Ph.D. course Quantum Machine Learning

Duration 20h (3CFU)
Scheduled at February or March 2022

1) introduction to phase space methods in quantum optics
2) introduction to quantum machine learning

1) Methods in the phase space, characteristic function
2) Gaussian states and their transformations
3) Neural network representation of Gaussian states
4) Training of quantum machine learning models
5) Examples
Gaussian Boson sampling
Neural networks variational ansatz for quantum many-body

Exam (two options)
1) Colloquium on theoretical aspects
2) Coding examples

Barnett and Radmore, Methods in Theoretical Quantum Optics

Configuring Emacs, the cool way

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 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

/usr/bin/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

1 How to configure emacs by a file

1.1 Set your init.el in .emacs.d

Write the following init.el in .emacs.d


Here 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

  • org-babel-load-file

Loads Emacs Lisp source blocks in the org file

  • expand-file-name

Replace the file name with absolute path

  • user-emacs-directory

Is the directory where the Emacs-specific files are placed .emacs.d typically, where the search of the file starts

1.2 Write

In the .org file for the configuration we will write different parts as pieces of emacs lisp code. For example

#+begin_src emacs-lisp
(add-to-list 'default-frame-alist '(width . 180))
(add-to-list 'default-frame-alist '(height . 90))

#+begin_src emacs-lisp
(set-face-attribute 'default (selected-frame) :height 150)

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.