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 classical annealing methods but enforced by optical vector-matrix multiplications and scalable photonic technology.
Non-abelian gauge fields emerge naturally in the description of adiabatically evolving quantum systems having degenerate levels. Here we show that they also play a role in Thouless pumping in the presence of degenerate bands. To this end we consider a photonic Lieb lattice having two degenerate non-dispersive modes and we show that, when the lattice parameters are slowly modulated, the propagation of the photons bear the fingerprints of the underlying non-abelian gauge structure. The non-dispersive character of the bands enables a high degree of control on photon propagation. Our work paves the way to the generation and detection of non-abelian gauge fields in photonic and optical lattices.
Non-abelian gauge fields lie at the very heart of many modern physical theories. We need new experimental routes and observables to disclose the importance of the Wilczek and Zee holonomy. We have shown that properly designed photonic lattices enable the control of the beam evolution by non-commutative fields. These lattices may lead to the direct observation of the quantization of the displacement due to a non-abelian Chern number. This work can be extended in several directions, including nonlinear effects or considering the propagation of non-classical light in non-abelian lattices. Both these possibilities are unexplored so far and open several new questions concerning – for example – the effect of the non-abelian holonomy on entanglement or the impact of nonlinearity in breaking the hidden symmetries. Non-abelian topological photonics may stimulate further developments and applications for classical and quantum information and tests of fundamental physics.
Controlling directional emission of nanophotonic radiation sources is fundamental to tailor radiation-matter interaction and to conceive highly efficient nanophotonic devices for on-chip wireless communication and information processing. Nanoantennas coupled to quantum emitters have proven to be very efficient radiation routers, while electrical control of unidirectional emission has been achieved through inelastic tunneling of electrons. Here we prove that the radiation emitted from the interaction of a high-energy electron beam with a graphene-nanoparticle composite has beaming directions which can be made to continuously span the full circle even through small variations of the graphene Fermi energy. Emission directionality stems from the interference between the double cone shaped electron transition radiation and the nanoparticle dipolar diffraction radiation. Tunability is enabled since the interference is ruled by the nanoparticle dipole moment whose amplitude and phase are driven by the hybrid plasmonic resonances of the composite and the absolute phase of the graphene plasmonic polariton launched by the electron, respectively. The flexibility of our method provides a way to exploit graphene plasmon physics to conceive improved nanosources with ultrafast reconfigurable radiation patterns.
Physical unclonable functions (PUFs) are complex physical objects that aim at overcoming the vulnerabilities of traditional cryptographic keys, promising a robust class of security primitives for different applications. Optical PUFs present advantages over traditional electronic realizations, namely, a stronger unclonability, but suffer from problems of reliability and weak unpredictability of the key. We here develop a two-step PUF generation strategy based on deep learning, which associates reliable keys verified against the National Institute of Standards and Technology (NIST) certification standards of true random generators for cryptography. The idea explored in this work is to decouple the design of the PUFs from the key generation and train a neural architecture to learn the mapping algorithm between the key and the PUF. We report experimental results with all-optical PUFs realized in silica aerogels and analyzed a population of 100 generated keys, each of 10,000 bit length. The key generated passed all tests required by the NIST standard, with proportion outcomes well beyond the NIST’s recommended threshold. The two-step key generation strategy studied in this work can be generalized to any PUF based on either optical or electronic implementations. It can help the design of robust PUFs for both secure authentications and encrypted communications.
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