Random media with tailored optical properties are attracting burgeoning interest for applications in imaging, biophysics, energy, nanomedicine, spectroscopy, cryptography, and telecommunications. A key paradigm for devices based on this class of materials is the transmission matrix, the tensorial link between the input and the output signals, that describes in full their optical behavior. The transmission matrix has specific statistical properties, such as the existence of lossless channels, that can be used to transmit information, and are determined by the disorder distribution. In nonlinear materials, these channels may be modulated and the transmission matrix tuned accordingly. Here, the direct measurement of the nonlinear transmission matrix of complex materials is reported, exploiting the strong optothermal nonlinearity of scattering silica aerogel (SA). It is shown that the dephasing effects due to nonlinearity are both controllable and reversible, opening the road to applications based on the nonlinear response of random media.
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”
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.
Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library TensorFlowTM 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 multimodal fiber. We show that trainable operators at the input and the readout enable to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir.
The successful exfoliation of graphite initiated new science in any research field and is employing a huge number of scientists in the world investigating chemical, structural, mechanical and optoelectrical; properties of the atomic-thick sheets of graphene and graphene oxide.
Similarly to other carbon-based materials, graphene family have shown exceptional optical responses; and nowadays it is engineered to produce efficient photonic components. In this review we aim to summarize the main results in nonlinear optical response and fluorescence of graphene oxide; moreover, its laser printing is reviewed as a novel promising lithographic technique.
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