Quantum Analog-Digital Conversion

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.Comment: 7 page

Formation of optimal Boolean functions for analog-digital conversion

[EN] This paper describes a situation in which the function obtained from an analog-to-digital conversion is considered a partially-certain function. In this case it is possible to neglect the least significant bits and therefore redefine them arbitrarily. A realization of proposed method simplifies the further processing of the received signal, and saves hardware consumption at designing and usage of hardware and software systems that include analog-to-digital converters

Low Power Analog-to-Digital Conversion in Millimeter Wave Systems: Impact of Resolution and Bandwidth on Performance

The wide bandwidth and large number of antennas used in millimeter wave systems put a heavy burden on the power consumption at the receiver. In this paper, using an additive quantization noise model, the effect of analog-digital conversion (ADC) resolution and bandwidth on the achievable rate is investigated for a multi-antenna system under a receiver power constraint. Two receiver architectures, analog and digital combining, are compared in terms of performance. Results demonstrate that: (i) For both analog and digital combining, there is a maximum bandwidth beyond which the achievable rate decreases; (ii) Depending on the operating regime of the system, analog combiner may have higher rate but digital combining uses less bandwidth when only ADC power consumption is considered, (iii) digital combining may have higher rate when power consumption of all the components in the receiver front-end are taken into account.Comment: 8 pages, 6 figures, in Proc. of IEEE Information Theory and Applications Workshop, Feb. 201

Method of analog-digital conversion of high-frequency signals With additional noise signal

У  роботі  представлено  метод  розширення  динамічного  діапазону  аналого­цифрових  перетворювачів  (АЦП)  високочастотних  сигналів  на  базі  додаткового  шумоподібного  вхідного  сигналу  АЦП.  Доведено,  що  даний  метод  характеризується  високою  ефективністю  при  високих  значеннях  нелінійності  характеристики  перетворення АЦП.  The method of analog­digital conversion of high­frequency signals, which unlike existing uses adding to the entrance signal of analog­digital converters (ADC) of additional noise as a multiharmonic noise signal is in­process offered.   Principles of correction of non­linearity of ADC are developed by an additional noise signal.   An additional noise signal is in a free frequency bar, close to maximum for this type ADC. Indemnification of noise signal is in future executed on the output of ADC and his filtration. The offered method enables to reduce the level of non­linearity of description of transformation of ADC, which results in expansion of dynamic range of transformer.   It is well­proven that increase of amplitude of additional noise signal on the entrance of ADC to the values which exceed unit of the least significant bit will allow to execute the improvement of linearness of description of transformation of ADC

Analog to Digital Conversion in Physical Measurements

There exist measuring devices where an analog input is converted into a digital output. Such converters can have a nonlinear internal dynamics. We show how measurements with such converting devices can be understood using concepts from symbolic dynamics. Our approach is based on a nonlinear one-to-one mapping between the analog input and the digital output of the device. We analyze the Bernoulli shift and the tent map which are realized in specific analog/digital converters. Furthermore, we discuss the sources of errors that are inevitable in physical realizations of such systems and suggest methods for error reduction.Comment: 9 pages in LATEX, 4 figures in ps.; submitted to 'Chaos, Solitons & Fractals

Deep photonic reservoir computing recurrent network

Deep neural networks usually process information through multiple hidden layers. However, most hardware reservoir computing recurrent networks only have one hidden reservoir layer, which significantly limits the capability of solving real-world complex tasks. Here we show a deep photonic reservoir computing (PRC) architecture, which is constructed by cascading injection-locked semiconductor lasers. In particular, the connection between successive hidden layers is all optical, without any optical-electrical conversion or analog-digital conversion. The proof of concept is demonstrated on a PRC consisting of 4 hidden layers and 320 interconnected neurons. In addition, we apply the deep PRC in the real-world signal equalization of an optical fiber communication system. It is found that the deep PRC owns strong ability to compensate the nonlinearity of fibers

Enhancing quantum entropy in vacuum-based quantum random number generator

Information-theoretically provable unique true random numbers, which cannot be correlated or controlled by an attacker, can be generated based on quantum measurement of vacuum state and universal-hashing randomness extraction. Quantum entropy in the measurements decides the quality and security of the random number generator. At the same time, it directly determine the extraction ratio of true randomness from the raw data, in other words, it affects quantum random numbers generating rate obviously. In this work, considering the effects of classical noise, the best way to enhance quantum entropy in the vacuum-based quantum random number generator is explored in the optimum dynamical analog-digital converter (ADC) range scenario. The influence of classical noise excursion, which may be intrinsic to a system or deliberately induced by an eavesdropper, on the quantum entropy is derived. We propose enhancing local oscillator intensity rather than electrical gain for noise-independent amplification of quadrature fluctuation of vacuum state. Abundant quantum entropy is extractable from the raw data even when classical noise excursion is large. Experimentally, an extraction ratio of true randomness of 85.3% is achieved by finite enhancement of the local oscillator power when classical noise excursions of the raw data is obvious.Comment: 12 pages,8 figure