The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Hongming Zhang - One of the best experts on this subject based on the ideXlab platform.
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Optical Analog-to-Digital Conversion System Based on Compressive Sampling
IEEE Photonics Technology Letters, 2011Co-Authors: Hao Nan, Hongming ZhangAbstract:A novel scheme of optical analog-to-Digital Conversion system is proposed based on compressive sampling and the recent electrical modulated wideband converter. The proposed optical solution is more stable and implementable in practice. Because of the ultranarrow optical pulseswidth, the proposed solution can provide a uniform signal-to-noise attenuate of all frequency bins, compared to its electrical counterpart.
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Adaptive thresholding scheme in photonic analog-to-Digital Conversion.
Optics letters, 2009Co-Authors: Yue Peng, Hongming Zhang, Minyu YaoAbstract:An adaptive thresholding scheme in photonic analog-to-Digital Conversion is proposed and experimentally demonstrated. Instead of a single-ended electronic comparator, differential detection technology is used to reduce the quantization noise induced by thresholding error. The main advantages of this scheme are the elimination of the dynamic setting of thresholding level and better tolerance to common-mode noise.
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Phase-shifted photonic analog-to-Digital Conversion system with 40GS/s sampling rate
2009 IEEE LEOS Annual Meeting Conference Proceedings, 2009Co-Authors: Yue Peng, Hongming Zhang, Minyu YaoAbstract:A phase-shifted photonic analog-to-Digital Conversion system is experimentally demonstrated with 40GS/S sampling rate. A 2.5GHz sinusoid signal is quantized and the effective number of bits of 3.41 is obtained.
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Photonic Analog-to-Digital Conversion using LiNbO3 Asymmetric Mach-Zehnder Interferometer
Optical Transmission Switching and Subsystems VI, 2008Co-Authors: Hongming Zhang, Minyu YaoAbstract:Asymmetric lithium niobate Mach-Zehnder interferometer and its applications in photonic analog-to-Digital Conversion will be discussed. Two schemes based on the asymmetric interferometer will be proposed and analyzed. The first scheme is the phase shift photonic analog-to-Digital Conversion using asymmetric interferometer and synchronized multiwavelength optical sampling pulses. Because of the dispersion effect of the lithium niobate crystal, when multiwavelength optical pulses enter into the interferometer, at the output port, different wavelengths will have different phase differences between two arms. As a result, after interference, the transmission characteristics of different wavelengths will have a phase shift between each other, and this is just the key issue of phase shift photonic analog-to-Digital Conversion. The other scheme we will propose in this paper is a spectral encoded photonic analog-to-Digital Conversion. The spectral transmission characteristic of the asymmetric interferometer will shift with the voltage change of the analog signal, and this shift has an ideal linear relation with the analog voltage change. The peak wavelength of the transmission spectrum can be detected to realize quantization of the applied analog signal. Using both schemes presented in this paper, high sampling rate and high resolution optical analog-to-Digital Conversion can be realized.
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All-Optical Analog-to-Digital Conversion Using Inherent Multiwavelength Phase Shift in LiNbO
2008Co-Authors: Hongming Zhang, Minyu Yao, Wei ZhouAbstract:All-optical analog-to-Digital Conversion utilizing inherent multiwavelength phase shift in lithium niobate phase modulator is proposed. In the experimental demonstration, a wavelength-tunable continuous-wave laser diode and a lithium niobate phase modulator are used to quantize the sinusoidal tone electrical analog signal. Using 16 different wavelengths, an effective number of bits of 4.3-bit has been obtained after software sampling measurement. Benefits of the presented approach in this letter are its simple realization of the phase shift and high stability. Index Terms—Analog-to-Digital Conversion, phase modulation, photonic analog-to-Digital converter (ADC), photonic sampling.
Minyu Yao - One of the best experts on this subject based on the ideXlab platform.
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Adaptive thresholding scheme in photonic analog-to-Digital Conversion.
Optics letters, 2009Co-Authors: Yue Peng, Hongming Zhang, Minyu YaoAbstract:An adaptive thresholding scheme in photonic analog-to-Digital Conversion is proposed and experimentally demonstrated. Instead of a single-ended electronic comparator, differential detection technology is used to reduce the quantization noise induced by thresholding error. The main advantages of this scheme are the elimination of the dynamic setting of thresholding level and better tolerance to common-mode noise.
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Phase-shifted photonic analog-to-Digital Conversion system with 40GS/s sampling rate
2009 IEEE LEOS Annual Meeting Conference Proceedings, 2009Co-Authors: Yue Peng, Hongming Zhang, Minyu YaoAbstract:A phase-shifted photonic analog-to-Digital Conversion system is experimentally demonstrated with 40GS/S sampling rate. A 2.5GHz sinusoid signal is quantized and the effective number of bits of 3.41 is obtained.
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Photonic Analog-to-Digital Conversion using LiNbO3 Asymmetric Mach-Zehnder Interferometer
Optical Transmission Switching and Subsystems VI, 2008Co-Authors: Hongming Zhang, Minyu YaoAbstract:Asymmetric lithium niobate Mach-Zehnder interferometer and its applications in photonic analog-to-Digital Conversion will be discussed. Two schemes based on the asymmetric interferometer will be proposed and analyzed. The first scheme is the phase shift photonic analog-to-Digital Conversion using asymmetric interferometer and synchronized multiwavelength optical sampling pulses. Because of the dispersion effect of the lithium niobate crystal, when multiwavelength optical pulses enter into the interferometer, at the output port, different wavelengths will have different phase differences between two arms. As a result, after interference, the transmission characteristics of different wavelengths will have a phase shift between each other, and this is just the key issue of phase shift photonic analog-to-Digital Conversion. The other scheme we will propose in this paper is a spectral encoded photonic analog-to-Digital Conversion. The spectral transmission characteristic of the asymmetric interferometer will shift with the voltage change of the analog signal, and this shift has an ideal linear relation with the analog voltage change. The peak wavelength of the transmission spectrum can be detected to realize quantization of the applied analog signal. Using both schemes presented in this paper, high sampling rate and high resolution optical analog-to-Digital Conversion can be realized.
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All-Optical Analog-to-Digital Conversion Using Inherent Multiwavelength Phase Shift in LiNbO
2008Co-Authors: Hongming Zhang, Minyu Yao, Wei ZhouAbstract:All-optical analog-to-Digital Conversion utilizing inherent multiwavelength phase shift in lithium niobate phase modulator is proposed. In the experimental demonstration, a wavelength-tunable continuous-wave laser diode and a lithium niobate phase modulator are used to quantize the sinusoidal tone electrical analog signal. Using 16 different wavelengths, an effective number of bits of 4.3-bit has been obtained after software sampling measurement. Benefits of the presented approach in this letter are its simple realization of the phase shift and high stability. Index Terms—Analog-to-Digital Conversion, phase modulation, photonic analog-to-Digital converter (ADC), photonic sampling.
Keisuke Fujii - One of the best experts on this subject based on the ideXlab platform.
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Quantum analog-Digital Conversion
Physical Review A, 2019Co-Authors: Kosuke Mitarai, Masahiro Kitagawa, Keisuke FujiiAbstract:Many quantum algorithms, such as Harrow-Hassidim-Lloyd (HHL) algorithm, depend on oracles that efficiently encode classical data into a quantum state. The encoding of the data can be categorized into two types; analog-encoding where the data are stored as amplitudes of a state, and Digital-encoding where they are stored as qubit-strings. The former has been utilized to process classical data in an exponentially large space of a quantum system, where as the latter is required to perform arithmetics on a quantum computer. Quantum algorithms like HHL achieve quantum speedups with a sophisticated use of these two encodings. In this work, we present algorithms that converts these two encodings to one another. While quantum Digital-to-analog Conversions have implicitly been used in existing quantum algorithms, we reformulate it and give a generalized protocol that works probabilistically. On the other hand, we propose an deterministic algorithm that performs a quantum analog-to-Digital Conversion. These algorithms can be utilized to realize high-level quantum algorithms such as a nonlinear transformation of amplitude of a quantum state. As an example, we construct a "quantum amplitude perceptron", a quantum version of neural network, and hence has a possible application in the area of quantum machine learning.
Andreas O. J. Wiberg - One of the best experts on this subject based on the ideXlab platform.
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OFC - Progress in photonic sampled analog-to-Digital Conversion
Optical Fiber Communication Conference, 2015Co-Authors: Andreas O. J. WibergAbstract:Recent advances in photonic sampled analog-to-Digital Conversion of wideband signals are reviewed in context of the parametric polychromatic sampling and cavity-less pulse source driven subsampling. Latest results and a new figure of merit are presented.
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Demonstration of parallel polychromatic sampling based analog-to-Digital Conversion at 8 GS/s
Conference on Lasers and Electro-Optics 2012, 2012Co-Authors: Andreas O. J. Wiberg, Evgeny Myslivets, Zhi Tong, Lan Liu, Joseph Ponsetto, Vahid Ataie, Nikola Alic, Stojan RadicAbstract:A scalable photonic sampled analog-to-Digital Conversion (ADC) is presented utilizing four-wave mixing processes to multicast and sample a signal. By expanding the architecture to multiple parallel gates, the effective sampling rate is increased.
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Linearized parametric gate for real-time photonic-sampled analog-to-Digital Conversion
Optical Fiber Communication Conference National Fiber Optic Engineers Conference 2011, 2011Co-Authors: Andreas O. J. Wiberg, Evgeny Myslivets, Ron R. Nissim, A. Danicic, Daniel Blessing, B. P.-p. Kuo, Stojan RadicAbstract:A linearized parametric sampling gate was constructed to explore its performance limits in an analog-to-Digital Conversion (ADC) architecture. Linear response of the sampling gate can be achieved both in high-resolution and high-rate ADC.
Kosuke Mitarai - One of the best experts on this subject based on the ideXlab platform.
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Quantum analog-Digital Conversion
Physical Review A, 2019Co-Authors: Kosuke Mitarai, Masahiro Kitagawa, Keisuke FujiiAbstract:Many quantum algorithms, such as Harrow-Hassidim-Lloyd (HHL) algorithm, depend on oracles that efficiently encode classical data into a quantum state. The encoding of the data can be categorized into two types; analog-encoding where the data are stored as amplitudes of a state, and Digital-encoding where they are stored as qubit-strings. The former has been utilized to process classical data in an exponentially large space of a quantum system, where as the latter is required to perform arithmetics on a quantum computer. Quantum algorithms like HHL achieve quantum speedups with a sophisticated use of these two encodings. In this work, we present algorithms that converts these two encodings to one another. While quantum Digital-to-analog Conversions have implicitly been used in existing quantum algorithms, we reformulate it and give a generalized protocol that works probabilistically. On the other hand, we propose an deterministic algorithm that performs a quantum analog-to-Digital Conversion. These algorithms can be utilized to realize high-level quantum algorithms such as a nonlinear transformation of amplitude of a quantum state. As an example, we construct a "quantum amplitude perceptron", a quantum version of neural network, and hence has a possible application in the area of quantum machine learning.
