Optical NP problem solver on laser-written waveguide platform

Artificial intelligence for photonics and photonic materials

Artificial intelligence (AI) is the most important new methodology in scientific research since the adoption of quantum mechanics and it is providing exciting results in numerous fields of science and technology. In this review we summarize research and discuss future opportunities for AI in the domains of photonics, nanophotonics, plasmonics and photonic materials discovery, including metamaterials.

Heuristic recurrent algorithms for photonic Ising machines

The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engineering, such as integrated circuits, memristors, and photonics. However, unleashing the potential of such architectures requires the development of algorithms which optimally exploit their fundamental properties. Here, we present the Photonic Recurrent Ising Sampler (PRIS), a heuristic method tailored for parallel architectures allowing fast and efficient sampling from distributions of arbitrary Ising problems. Since the PRIS relies on vector-to-fixed matrix multiplications, we suggest the implementation of the PRIS in photonic parallel networks, which realize these operations at an unprecedented speed. The PRIS provides sample solutions to the ground state of Ising models, by converging in probability to their associated Gibbs distribution. The PRIS also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently. Our work suggests speedups in heuristic methods via photonic implementations of the PRIS.

A scalable photonic computer solving the subset sum problem

The subset sum problem (SSP) is a typical nondeterministic-polynomial-time (NP)-complete problem that is hard to solve efficiently in time with conventional computers. Photons have the unique features of high propagation speed, strong robustness, and low detectable energy level and therefore can be promising candidates to meet the challenge. Here, we present a scalable chip built-in photonic computer to efficiently solve the SSP. We map the problem into a three-dimensional waveguide network through a femtosecond laser direct writing technique. We show that the photons sufficiently dissipate into the networks and search for solutions in parallel. In the case of successive primes, our approach exhibits a dominant superiority in time consumption even compared with supercomputers. Our results confirm the ability of light to realize computations intractable for conventional computers, and suggest the SSP as a good benchmarking platform for the race between photonic and conventional computers on the way toward "photonic supremacy."

Spatially resolved cross-sectional refractive index profile of fs laser-written waveguides using a genetic algorithm

Laser-written waveguides in glass have many potential applications as photonic devices. However, there is little knowledge of the actual profile of the usually asymmetric refractive index (RI) change across the femtosecond (fs) laser-written waveguides. We show, here, a new nondestructive method to measure any symmetric or asymmetric two-dimensional RI profile of fs laser-written waveguides in transparent materials. The method is also suitable for the measurement of the RI profile of any other type of waveguide. A Mach-Zehnder interferometer is used to obtain the phase shift of light propagating transversely through the RI-modified region. A genetic algorithm is then used to determine the matching cross-sectional RI profile based on the known waveguide shape and dimensions. A validation of the method with the comparison to a RNF measurement of the industry-standard SMF-28 is presented, as well as a demonstration of its versatility with measurements on fs laser-written waveguides.