Adiabatic evolution on a spatial-photonic Ising machine

Combinatorial optimization problems are crucial for widespread applications but remain difficult to solve on a large scale with conventional hardware. Novel optical platforms, known as coherent or photonic Ising machines, are attracting considerable attention as accelerators on optimization tasks formulable as Ising models. Annealing is a well-known technique based on adiabatic evolution for finding optimal solutions in classical and quantum systems made by atoms, electrons, or photons. Although various Ising machines employ annealing in some form, adiabatic computing on optical settings has been only partially investigated. Here, we realize the adiabatic evolution of frustrated Ising models with 100 spins programmed by spatial light modulation. We use holographic and optical control to change the spin couplings adiabatically, and exploit experimental noise to explore the energy landscape. Annealing enhances the convergence to the Ising ground state and allows to find the problem solution with probability close to unity. Our results demonstrate a photonic scheme for combinatorial optimization in analogy with adiabatic quantum algorithms and enforced by optical vector-matrix multiplications and scalable photonic technology.


See also Super Duper Ising Machine

Direct Observation of Topological Protected Edge States in Slow-Light

We use split-ring resonators to demonstrate topologically protected edge states in the Su-Schieffer-Heeger model experimentally, but in a slow-light wave with the group velocity down to ∼0.1 of light speed in free space. A meta-material formed by an array of complementary split-ring resonators with controllable hopping strength enables the direct observation in transmission and reflection of non-trivial topology eigenstates, including a negative phase velocity regime. By rotating the texture orientation of the diatomic resonators, we can explore all the band structures and unveil the onset of the trivial and non-trivial protected eigenmodes at GHz frequencies, even in the presence of non-negligible loss. Our system realizes a fully tunable and controllable artificial optical system to study the interplay between topology and slow-light towards applications in quantum technologies


Programming multi-level quantum gates in disordered computing reservoirs via machine learning

Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library TensorFlow to design multi-level quantum gates, including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multi-modal fiber. We show that trainable operators at the input and the readout enable one to realize multi-level gates. We study various qudit gates, including the scaling properties of the algorithms with the size of the reservoir. Despite an initial low slop learning stage, TensorFlow turns out to be an extremely versatile resource for designing gates with complex media, including different models that use spatial light modulators with quantized modulation levels.

Optics Express 28, 14018 (2020)

See also Quantum Gates by Tensorflow

The Game of Light

In memoriam: John Horton Conway

In 1970 an article by Martin Gardner appeared in Scientific American disclosing for the first time a “game” invented by John H. Conway: a matrix of ones and zeros changes with time according to simple rules inspired by biology. Cells (ones) survive or die because of overpopulation, or starvation. The simple rules surprisingly generate a variety of binary animals, named gliders, blocks, and spaceships, among others. By pen and paper, Conway demonstrated that complex dynamics spontaneously emerge in the game. Ultimately, Conway’s Game of Life turned out to be a universal Turing machine, and it is the most famous example of Cellular Automaton.

I was deeply inspired by the possibility of generating complexity with simple rules, like many others before me. In more than 50 years, Conway’s Game of Life inspired generations of scientists. “Life” is at the inner core of ideas that pervade nowadays machine learning, evolutionary biology, quantum computing, and many other fields. It also connects to the work of Wolfram and the development of Mathematica.

I was intrigued by the interaction between light and complexity and I wanted to combine the Game of Life with electromagnetic fields. I report below my original post on the topic (dating back to 2008). The article was rejected by many journals and finally published in a book dedicated to the 50 years of the GOL ( Game of Life Cellular Automata, Springer 2010).

The Enlightened Game of Life (EGOL)

The link between light and the development of complex behavior is as subtle as evident. Examples include the moonlight triggered mass spawning of hard corals in the Great Barrier, or the light-switch hypothesis in evolutionary biology, which ascribes the Cambrian explosion of biodiversity to the development of vision. Electromagnetic (EM) radiation drastically alters complex systems, from physics (e.g., climate changes) to biology (e.g., structural colors or bioluminescence). So far the emphasis has been given to bio-physical, or digital, models of the evolution of the eye with the aim of understanding the environmental influence on highly specialized organs. In this manuscript, we consider the way the appearance of photosensitivity affects the dynamics, the emergent properties and the self-organization of a community of interacting agents, specifically, of cellular automata (CA).

Quick and dirty implementation of the EGOL in a Python Notebook