☆ 4.8 Article

Heavy tails and pruning in programmable photonic circuits for universal unitaries

NATURE COMMUNICATIONS (2023)

Related references

Note: Only part of the references are listed.

Article Computer Science, Interdisciplinary Applications

Evolving scattering networks for engineering disorder

Sunkyu Yu

Summary: Network science is a powerful tool for understanding complex systems. Using wave phenomena to construct networks is important for machine learning hardware, as seen in optical neural networks. However, most wave-based networks use static models, and incorporating evolving models from network science into wave physics has great potential.

NATURE COMPUTATIONAL SCIENCE (2023)

Add to Collection

Article Multidisciplinary Sciences

Deep physical neural networks trained with backpropagation

Logan G. Wright et al.

Summary: The study introduced a hybrid in situ-in silico algorithm called physics-aware training, which applies backpropagation to train controllable physical systems for deep physical neural networks. By demonstrating the training of diverse physical neural networks in areas like optics, mechanics, and electronics to perform audio and image classification tasks, the research showcased the universality and effectiveness of the approach.

NATURE (2022)

Add to Collection

Review Optics

Photonic matrix multiplication lights up photonic accelerator and beyond

Hailong Zhou et al.

Summary: Photonic matrix multiplication is a crucial part of information processing in science and technology, offering faster and more efficient computations in the optical domain. Recent research has shown its potential for applications in telecommunications and artificial intelligence that are beyond the capabilities of conventional processors.

LIGHT-SCIENCE & APPLICATIONS (2022)

Add to Collection

Article Nanoscience & Nanotechnology

Resonance for Analog Recurrent Neural Network

Yurui Qu et al.

Summary: There is a strong interest in using physical waves for artificial neural computing due to their fast speed and intrinsic parallelism. Resonance, a common feature in many wave systems, is a natural choice for analog computing in temporal signals. This study demonstrates that resonance can be utilized to construct stable and scalable recurrent neural networks. By incorporating resonators with different lifetimes, the computing system can develop both short-term and long-term memories simultaneously.

ACS PHOTONICS (2022)

Add to Collection

Article Physics, Applied

Stability of Self-Configuring Large Multiport Interferometers

Ryan Hamerly et al.

Summary: This paper highlights the importance of algorithmic stability in self-configuration and proposes a self-configuration scheme for both triangular and rectangular meshes.

PHYSICAL REVIEW APPLIED (2022)

Add to Collection

Article Multidisciplinary Sciences

Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice

G. W. K. Moore et al.

Summary: Ice arches at the northern and southern ends of Nares Strait, a key passage in the Arctic, are forming for shorter durations, leading to increased ice transport and accelerating the export of multi-year ice.

NATURE COMMUNICATIONS (2021)

Add to Collection

Article Multidisciplinary Sciences

An optical neural chip for implementing complex-valued neural network

H. Zhang et al.

Summary: Complex-valued neural networks have advantages over real-valued networks, but optical computing platforms have not fully utilized these benefits. This study introduces an optical neural chip that implements complex-valued operations and demonstrates superior performance compared to its real-valued counterpart in various tasks.

NATURE COMMUNICATIONS (2021)

Add to Collection

Review Nanoscience & Nanotechnology

Engineered disorder in photonics

Sunkyu Yu et al.

Summary: In photonics, the introduction of order and disorder for device applications has traditionally been treated separately. Recent developments in nanofabrication and design strategies enable the use of materials between order and disorder, leading to innovative optical phenomena. Engineered disorder has the potential to greatly enhance design freedom in photonics and transform the traditional landscape of materials design.

NATURE REVIEWS MATERIALS (2021)

Add to Collection

Article Multidisciplinary Sciences

Quantum circuits with many photons on a programmable nanophotonic chip

J. M. Arrazola et al.

Summary: The newly introduced photon quantum computing system is capable of executing multiple quantum algorithms, surpassing the limitations of existing photon quantum computers, with significant breakthroughs in detecting the quantity and rate of multi-photon events. The platform validates the application prospects of photon technologies in quantum information processing, particularly in the breakthroughs brought by high squeezing and sampling rates.

NATURE (2021)

Add to Collection

Review Optics

Photonics for artificial intelligence and neuromorphic computing

Bhavin J. Shastri et al.

Summary: Research in photonic computing is thriving due to the proliferation of optoelectronic components in photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, with algorithms running on such hardware having the potential to address the growing demand for machine learning and artificial intelligence. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence.

NATURE PHOTONICS (2021)

Add to Collection

Article Optics

Parity time symmetric optical neural networks

Haoqin Deng et al.

Summary: A new architecture of optical neural networks (ONNs) utilizing parity-time (PT) symmetric couplers as building blocks has been proposed, with gain-loss contrasts adjusted for training the network to avoid problems associated with changing phase. The PT symmetric ONNs show adequate expressiveness in tasks like digit recognition and achieve comparable accuracy to conventional ONNs. This approach may lead to new avenues for fast training in chip-scale ONNs.

OPTICA (2021)

Add to Collection

Article Physics, Multidisciplinary

Robust Architecture for Programmable Universal Unitaries

M. Yu Saygin et al.

PHYSICAL REVIEW LETTERS (2020)

Add to Collection

Article Optics

Optimal design of error-tolerant reprogrammable multiport interferometers

S. A. Fldzhyan et al.

OPTICS LETTERS (2020)

Add to Collection

Article Engineering, Electrical & Electronic

Graph Representations for Programmable Photonic Circuits

Xiangfeng Chen et al.

JOURNAL OF LIGHTWAVE TECHNOLOGY (2020)

Add to Collection

Review Multidisciplinary Sciences

Programmable photonic circuits

Wim Bogaerts et al.

NATURE (2020)

Add to Collection

Article Multidisciplinary Sciences

Machine learning identifies scale-free properties in disordered materials

Sunkyu Yu et al.

NATURE COMMUNICATIONS (2020)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks

Charles H. Martin et al.

PROCEEDINGS OF THE 2020 SIAM INTERNATIONAL CONFERENCE ON DATA MINING (SDM) (2020)

Add to Collection

Article Optics

Universal programmable photonic architecture for quantum information processing

Ben Bartlett et al.

PHYSICAL REVIEW A (2020)

Add to Collection

Article Multidisciplinary Sciences

Scale-free networks are rare

Anna D. Broido et al.

NATURE COMMUNICATIONS (2019)

Add to Collection

Article Quantum Science & Technology

Quantum optical neural networks

Gregory R. Steinbrecher et al.

NPJ QUANTUM INFORMATION (2019)

Add to Collection

Article Multidisciplinary Sciences

Quantum supremacy using a programmable superconducting processor

Frank Arute et al.

NATURE (2019)

Add to Collection

Article Physics, Multidisciplinary

Scale-free networks well done

Ivan Voitalov et al.

PHYSICAL REVIEW RESEARCH (2019)

Add to Collection

Article Multidisciplinary Sciences

Multidimensional quantum entanglement with large-scale integrated optics

Jianwei Wang et al.

SCIENCE (2018)

Add to Collection

Article Materials Science, Multidisciplinary

All-optical nonlinear activation function for photonic neural networks

Mario Miscuglio et al.

OPTICAL MATERIALS EXPRESS (2018)

Add to Collection

Review Optics

Linear programmable nanophotonic processors

Nicholas C. Harris et al.

OPTICA (2018)

Add to Collection

Article Quantum Science & Technology

Quantum Computing in the NISQ era and beyond

John Preskill

QUANTUM (2018)

Add to Collection

Article Optics

Deep learning with coherent nanophotonic circuits

Yichen Shen et al.

NATURE PHOTONICS (2017)

Add to Collection

Article Optics

Unscrambling light-automatically undoing strong mixing between modes

Andrea Annoni et al.

LIGHT-SCIENCE & APPLICATIONS (2017)

Add to Collection

Article Optics

Optimal design for universal multiport interferometers

William R. Clements et al.

OPTICA (2016)

Add to Collection

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Add to Collection

Article Multidisciplinary Sciences

Universal linear optics

Jacques Carolan et al.

SCIENCE (2015)

Add to Collection

Article Optics

Generation of universal linear optics by any beam splitter

Adam Bouland et al.

PHYSICAL REVIEW A (2014)

Add to Collection

Article Physics, Applied

Thermo-optic coefficient of silicon at 1550nm and cryogenic temperatures

J. Komma et al.

APPLIED PHYSICS LETTERS (2012)

Add to Collection

Article Physics, Multidisciplinary

Fidelity between unitary operators and the generation of robust gates against off-resonance perturbations

Renan Cabrera et al.

JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL (2011)

Add to Collection

Article Mathematics, Applied

Power-Law Distributions in Empirical Data

Aaron Clauset et al.

SIAM REVIEW (2009)

Add to Collection

Review Physics, Multidisciplinary

Linear optical quantum computing with photonic qubits

Pieter Kok et al.

REVIEWS OF MODERN PHYSICS (2007)

Add to Collection

Article Multidisciplinary Sciences

Scale-free networks

AL Barabási et al.

SCIENTIFIC AMERICAN (2003)

Add to Collection

© Peeref 2019-2024. All rights reserved.