1,141 research outputs found
An On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase-encoding and Intensity Detection
Photonic Ising machine is a new paradigm of optical computing, which is based
on the characteristics of light wave propagation, parallel processing and low
loss transmission. Thus, the process of solving the combinatorial optimization
problems can be accelerated through photonic/optoelectronic devices. In this
work, we have proposed and demonstrated the so-called Phase-Encoding and
Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems
on demand. The PEIDIA is based on the simulated annealing algorithm and
requires only one step of optical linear transformation with simplified
Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase
term of the optical field and only intensity detection is required during the
solving process. As a proof of principle, several 20 and 30-dimensional Ising
problems have been solved with high ground state probability
The physics of optical computing
There has been a resurgence of interest in optical computing over the past
decade, both in academia and in industry, with much of the excitement centered
around special-purpose optical computers for neural-network processing. Optical
computing has been a topic of periodic study for over 50 years, including for
neural networks three decades ago, and a wide variety of optical-computing
schemes and architectures have been proposed. In this paper we provide a
systematic explanation of why and how optics might be able to give speed or
energy-efficiency benefits over electronics for computing, enumerating 11
features of optics that can be harnessed when designing an optical computer.
One often-mentioned motivation for optical computing -- that the speed of light
is fast -- is not a key differentiating physical property of optics for
computing; understanding where an advantage could come from is more subtle. We
discuss how gaining an advantage over state-of-the-art electronic processors
will likely only be achievable by careful design that harnesses more than one
of the 11 features, while avoiding a number of pitfalls that we describe.Comment: 31 pages; 11 figure
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
General Spatial Photonic Ising Machine Based on Interaction Matrix Eigendecomposition Method
The spatial photonic Ising machine has achieved remarkable advancements in
solving combinatorial optimization problems. However, it still remains a huge
challenge to flexibly mapping an arbitrary problem to Ising model. In this
paper, we propose a general spatial photonic Ising machine based on interaction
matrix eigendecomposition method. Arbitrary interaction matrix can be
configured in the two-dimensional Fourier transformation based spatial photonic
Ising model by using values generated by matrix eigendecomposition. The error
in the structural representation of the Hamiltonian decreases substantially
with the growing number of eigenvalues utilized to form the Ising machine. In
combination with the optimization algorithm, as low as 65% of the eigenvalues
is required by intensity modulation to guarantee the best probability of
optimal solution for a 20-vertex graph Max-cut problem, and this probability
decreases to below 20% for zero best chance. Our work provides a viable
approach for spatial photonic Ising machines to solve arbitrary combinatorial
optimization problems with the help of multi-dimensional optical property
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, knownas 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
A large scale photonic matrix processor enabled by charge accumulation
This is the final version. Available on open access from De Gruyter via the DOI in this recordIntegrated neuromorphic photonic circuits aim to power complex artificial neural networks (ANNs) in an energy and time efficient way by exploiting the large bandwidth and the low loss of photonic structures. However, scaling photonic circuits to match the requirements of modern ANNs still remains challenging. In this perspective, we give an overview over the usual sizes of matrices processed in ANNs and compare them with the capability of existing photonic matrix processors. To address shortcomings of existing architectures, we propose a time multiplexed matrix processing scheme which virtually increases the size of a physical photonic crossbar array without requiring any additional electrical post-processing. We investigate the underlying process of time multiplexed incoherent optical accumulation and achieve accumulation accuracy of 98.9% with 1 ns pulses. Assuming state of the art active components and a reasonable crossbar array size, this processor architecture would enable matrix vector multiplications with 16,000 × 64 matrices all optically on an estimated area of 51.2 mm2, while performing more than 110 trillion multiply and accumulate operations per second.Deutsche ForschungsgemeinschaftEuropean CommissionBundesministerium für Bildung und Forschun
In-memory computing with emerging memory devices: Status and outlook
Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning
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