Integrated Reconfigurable Unitary Optical Mode Converter Using MMI Couplers

Tang, Rui; Tanemura, Takuo; Nakano, Yoshiaki

doi:10.1109/lpt.2017.2700619

Cited by 47 publications

(22 citation statements)

References 28 publications

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“…Similar ideas of configuring the transformation were reported in [21][22][23][24]. The universality theorem was proven for a multiport circuit of similar topology [25], however the proof relies on the ability to construct specific multiport mixing gates to fit the overall transformation to the desired unitary matrix, which is not the case considered in this work.…”

Section: The Unitary Composermentioning

confidence: 66%

Robust Architecture for Programmable Universal Unitaries

et al. 2020

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Experimental implementation of a quantum computing algorithm strongly relies on the ability to construct required unitary transformations applied to the input quantum states. In particular, near-term linear optical computing requires universal programmable interferometers, capable of implementing an arbitrary transformation of input optical modes. So far these devices were composed as a circuit with well defined building blocks, such as balanced beamsplitters. This approach is vulnerable to manufacturing imperfections inevitable in any realistic experimental implementation, and the larger the circuit size grows, the more strict the tolerances become. In this work we demonstrate a new methodology for the design of the high-dimensional mode transformations, which overcomes this problem, and carefully investigate its features. The circuit in our architecture is composed of interchanging mode mixing layers, which may be almost arbitrary, and layers of variable phaseshifters, allowing to program the device to approximate any desired unitary transformation. arXiv:1906.06748v1 [quant-ph]

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Section: The Unitary Composermentioning

confidence: 66%

Robust Architecture for Programmable Universal Unitaries

et al. 2020

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“…A mini-sized and universal MVM is more practical, especially in integrated photonic applications. In 2017, Tang et al first proposed a novel integrated reconfigurable unitary optical mode converter using multimode interference couplers, which shared a similar principle with MPLC 59 . Then, it was used for all-optical on-chip multi-input-multi-output (MIMO) mode demultiplexing 60 .…”

Section: Matrix-vector Multiplicationmentioning

confidence: 99%

“…Its scale is very large, input vector sizes of 357 for SPLC-MVM 48 and 490000 ( N = 700) for MPLC-MVM 58 were reported, easily up to 10 3 for SPLC-MVM and 10 6 for MPLC-MVM with SLMs 58 . However, the device size is quite large, and hence, the integrated counterpart was pursued 59 – 61 . The MZI-MVM method is also coherent, but its scale is far smaller than that of the PLC-MVM method ( N = 64 was reported by Lightmatter 95 ).…”

Section: Matrix-vector Multiplicationmentioning

confidence: 99%

Photonic matrix multiplication lights up photonic accelerator and beyond

et al. 2022

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Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerators are designed to accelerate specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for computing resources and capacity. Photonic matrix multiplication has much potential to expand the domain of telecommunication, and artificial intelligence benefiting from its superior performance. Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors. In this review, we first introduce the methods of photonic matrix multiplication, mainly including the plane light conversion method, Mach–Zehnder interferometer method and wavelength division multiplexing method. We also summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years. Finally, we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

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“…The unitary diffractive layers describe the interaction between channels, which can be implemented by multimode interference (MMI) couplers or a region of coupled waveguides; therefore, the transfer matrices of unitary diffractive layers are static because the structure of MMI couplers and coupled waveguides cannot be changed once fabricated. In 2017, Tang et al proposed a novel integrated architecture consisting of cascaded MMI couplers and phase shifter arrays [24] to realize an n × n unitary transfer matrix. The n × n unitary transfer matrix can be decomposed as M stages of unitary matrices with only diagonal elements (i.e., the transfer matrices of phase shifter arrays) and another M − 1 stage of unitary matrices (i.e., the transfer matrices of MMI) when the path-dependent coupling loss is not considered.…”

Section: Figurementioning

confidence: 99%

“…In 2017, Tang et al proposed a novel integrated architecture consisting of cascaded MMI couplers and phase shifter arrays [ 24 ] to realize an n × n unitary transfer matrix. The n × n unitary transfer matrix can be decomposed as M stages of unitary matrices with only diagonal elements (i.e., the transfer matrices of phase shifter arrays) and another M − 1 stage of unitary matrices (i.e., the transfer matrices of MMI) when the path-dependent coupling loss is not considered.…”

Section: Mplc Matrix Corementioning

confidence: 99%

Photonic Matrix Computing: From Fundamentals to Applications

2021

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In emerging artificial intelligence applications, massive matrix operations require high computing speed and energy efficiency. Optical computing can realize high-speed parallel information processing with ultra-low energy consumption on photonic integrated platforms or in free space, which can well meet these domain-specific demands. In this review, we firstly introduce the principles of photonic matrix computing implemented by three mainstream schemes, and then review the research progress of optical neural networks (ONNs) based on photonic matrix computing. In addition, we discuss the advantages of optical computing architectures over electronic processors as well as current challenges of optical computing and highlight some promising prospects for the future development.

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Integrated Reconfigurable Unitary Optical Mode Converter Using MMI Couplers

Cited by 47 publications

References 28 publications

Robust Architecture for Programmable Universal Unitaries

Robust Architecture for Programmable Universal Unitaries

Photonic matrix multiplication lights up photonic accelerator and beyond

Photonic Matrix Computing: From Fundamentals to Applications

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