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Related papers: Variational processing of multimode squeezed light

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The squeezed photons, as a quantum-correlated light with reduced noise, have emerged as a great resource for sensing the structures of matter. Here we study the transient absorption (TA) scheme using the squeezed photons whose spectral…

Quantum Physics · Physics 2026-05-29 Jiahao Joel Fan , Feihong Liu , Dangyuan Lei , Zhedong Zhang

High-Q optical microresonators combine low losses and high optical energy concentration in a small effective mode volume, making them an attractive platform for optical sensors. While light is confined in the microresonator by total…

Quantum Physics · Physics 2025-11-18 Dariya Salykina , Daniil Shakhbaziants , Igor Bilenko , Farid Khalili

Continuous-variable encoding of quantum information in the optical domain has recently yielded large temporal and spectral entangled states instrumental for quantum computing and quantum communication. We introduce a protocol for the…

Quantum Physics · Physics 2021-01-20 D. Barral , K. Bencheikh , J. A. Levenson , N. Belabas

Scaling-up optical quantum technologies requires to combine highly efficient multi-photon sources and integrated waveguide components. Here, we interface these scalable platforms: a quantum dot based multi-photon source and a reconfigurable…

Controlling and engineering continuous spectral modes of entangled photons represents one of the promising approaches toward secure quantum communications. By using the telecom bandwidth generated from a cascade-emitted biphoton in atomic…

Quantum Physics · Physics 2023-12-15 N. -Y Tsai , H. H. Jen

Quantum sensing is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use optimization…

Programmable unitary photonic networks that interfere hundreds of modes are emerging as a key technology in energy-efficient sensing, machine learning, cryptography, and linear optical quantum computing applications. In this work, we…

Optics · Physics 2022-11-01 Sunil Pai , Olav Solgaard , Shanhui Fan , David A. B. Miller

As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic…

Emerging Technologies · Computer Science 2020-06-25 Liane Bernstein , Alexander Sludds , Ryan Hamerly , Vivienne Sze , Joel Emer , Dirk Englund

Optical unitary converter (OUC) that can convert a set of N mutually orthogonal optical modes into another set of arbitrary N orthogonal modes is expected to be the key device in diverse applications, including the optical communication,…

In this paper, we propose an image re-sampling compression method by learning virtual codec network (VCN) to resolve the non-differentiable problem of quantization function for image compression. Here, the image re-sampling not only refers…

Image and Video Processing · Electrical Eng. & Systems 2018-07-11 Lijun Zhao , Huihui Bai , Anhong Wang , Yao Zhao

Radio Frequency (RF) sensing holds the potential for enabling pervasive monitoring applications. However, modern sensing algorithms imply complex operations, which clash with the energy-constrained nature of edge sensing devices. This calls…

Signal Processing · Electrical Eng. & Systems 2024-01-30 Eleonora Cicciarella , Riccardo Mazzieri , Jacopo Pegoraro , Michele Rossi

Spiking neural networks (SNN) provide a new computational paradigm capable of highly parallelized, real-time processing. Photonic devices are ideal for the design of high-bandwidth, parallel architectures matching the SNN computational…

Neural and Evolutionary Computing · Computer Science 2022-08-30 Luis El Srouji , Yun-Jhu Lee , Mehmet Berkay On , Li Zhang , S. J. Ben Yoo

Transport phenomena on a quantum scale appear in a variety of systems, ranging from photosynthetic complexes to engineered quantum devices. It has been predicted that the efficiency of quantum transport can be enhanced through dynamic…

The rapid growth of artificial intelligence, coupled with the slowing of Moore's law, is straining computing infrastructure, as CMOS electronics face inherent limits in bandwidth, energy efficiency, and parallelism. Integrated photonic…

As computing resource demands continue to escalate in the face of big data, cloud-connectivity and the internet of things, it has become imperative to develop new low-power, scalable architectures. Neuromorphic photonics, or photonic neural…

Network theory has played a dominant role in understanding the structure of complex systems and their dynamics. Recently, quantum complex networks, i.e. collections of quantum systems in a non-regular topology, have been explored leading to…

Integrated photonic technologies have recently shown significant advances, enabling the possibility to implement reconfigurable interferometers with increasing size. One of the main tasks to fully exploit the capabilities of reconfigurable…

Quantum Physics · Physics 2026-05-12 Denis Stanev , Nicolò Spagnolo , Fabio Sciarrino

Optical computing offers ultrafast, energy-efficient alternatives to conventional digital processors, yet most implementations remain confined to single-channel processing, severely underutilizing light's information capacity. Here we…

Optics · Physics 2026-01-13 Fatma Nur Kılınç , Uğur Teğin

The time evolution of quantum many-body systems is one of the most promising applications for near-term quantum computers. However, the utility of current quantum devices is strongly hampered by the proliferation of hardware errors. The…

Quantum Physics · Physics 2024-05-21 Maurits S. J. Tepaske , David J. Luitz , Dominik Hahn

Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic…

Emerging Technologies · Computer Science 2021-06-29 Bryce A. Primavera , Jeffrey M. Shainline
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