Super-Duper Ising Machine featured in Physics!

New hardware for solving NP-complete problems is of paramount importance in the modern theory of complexity and computation. In the new era of machine learning and quantum computing, many groups are working for realizing “annealing devices.” Ising machines are a special class that finds the minima of spin-glass Hamiltonians, as Sherrington-Kirkpatrick and Mattis models. Our recent work on a new simple and scalable Ising machine [Phys.Rev.Lett. 122, 213902(2019) and arXiv:1905.11548] has been featured in Physics.

Photonic Ising Machines Go Big: A new optical processor for solving hard optimization problems breaks previous size records and is based on a highly scalable technology”

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Super-Duper Ising Machine by a Single SLM

Quantum and classical physics can be used for mathematical computations that are hard to tackle by conventional electronics. Very recently, optical Ising machines have been demonstrated for computing the minima of spin Hamiltonians, paving the way to new ultra-fast hardware for machine learning. However, the proposed systems are either tricky to scale or involve a limited number of spins. We design and experimentally demonstrate a large-scale optical Ising machine based on a simple setup with a spatial light modulator. By encoding the spin variables in a binary phase modulation of the field, we show that light propagation can be tailored to minimize an Ising Hamiltonian with spin couplings set by input amplitude modulation and a feedback scheme. We realize configurations with thousands of spins that settle in the ground state in a low-temperature ferromagnetic-like phase with all-to-all and tunable pairwise interactions. Our results open the route to classical and quantum photonic Ising machines that exploit light spatial degrees of freedom for parallel processing of a vast number of spins with programmable couplings.

D. Pierangeli, G. Marcucci, C. Conti in ArXiv:1905.11548 and Phys. Rev. Lett. 122, 213902 (2019)

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Deep learning, living, random, optical, and – maybe – useful

In a recent paper, we demonstrated an optical deep neural network with a real living piece of brain tumor (a 3D “tumour model”). We think this is the first example of a hybrid living/photonic hardware: a sort of artificially intelligent device performing optical functions and detecting tumour morphodynamics (including the effect of chemotherapy)

Deep optical neural network by living tumour brain cells

Abstract: The new era of artificial intelligence demands large-scale ultrafast hardware for machine learning. Optical artificial neural networks process classical and quantum information at the speed of light, 
and are compatible with silicon technology, but lack scalability and need expensive manufacturing of many computational layers. New paradigms, as reservoir computing and the extreme learning machine, suggest that disordered and biological materials may realize artificial neural networks with thousands of computational nodes trained only at the input and at the readout. Here we employ biological complex systems, i.e., living three-dimensional tumour brain models, and demonstrate a random neural network (RNN) trained to detect tumour morphodynamics via
image transmission. The RNN, with the tumour spheroid 19 as a three-dimensional deep computational reservoir, performs programmed optical functions and detects cancer morphodynamics from laser-induced hyperthermia inaccessible by optical imaging. Moreover, the RNN quantifies the effect of chemotherapy inhibiting tumour growth. We realize a non-invasive smart probe for cytotoxicity assay, which is at least one order of magnitude more sensitive with respect to conventional imaging. Our random and hybrid photonic/living system is a novel artificial machine for computing and for the real-time investigation of tumour dynamics.

Authors: D. Pierangeli, V. Palmieri, G. Marcucci, C. Moriconi, G. Perini, M. De Spirito, M. Papi, C. Conti

https://arxiv.org/abs/1812.09311

The Bathynomus propinquus

Testing the new Gutenberg editor of WordPress : the visit at the Harvard Museum of Natural History

One of the most surprising animals of the last century :  the Bathynomus propinquus

Why this nice fluffy has such big eyes if he leaves in the soil of the dark deep ocean?

I first read about the giant isopod in the wonderful book “In the Blink of an Eye” by Andrew Parker, a surprising tour in evolution and the Cambrian explosion , see the enlightened game of life.

Recently, I was able to visit the Harvard Museum of Natural Hystory, where you find the footprints of writers and scientists as Stephen Jay Could, and you can meet the fluffy puppy above.

With a primitive fish (the one smiling but without glasses) at the Harvard Museum

Read many books for many years ! (and visit the museums)

Observation of replica symmetry breaking in disordered nonlinear wave propagation

A landmark of statistical mechanics, spin-glass theory describes critical phenomena in disordered systems that range from condensed matter to biophysics and social dynamics. The most fascinating concept is the breaking of replica symmetry: identical copies of the randomly interacting system that manifest completely different dynamics. Replica symmetry breaking has been predicted in nonlinear wave propagation, including Bose-Einstein condensates and optics, but it has never been observed. Here, we report the experimental evidence of replica symmetry breaking in optical wave propagation, a phenomenon that emerges from the interplay of disorder and nonlinearity. When mode interaction dominates light dynamics in a disordered optical waveguide, different experimental realizations are found to have an anomalous overlap intensity distribution that signals a transition to an optical glassy phase. The findings demonstrate that nonlinear propagation can manifest features typical of spin-glasses and provide a novel platform for testing so-far unexplored fundamental physical theories for complex systems.

Davide Pierangeli, Andrea Tavani, Fabrizio Di Mei, Aharon J. Agranat, Claudio Conti, Eugenio Del Re, Nature Communications 8:1501 (2017)