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Nanophotonics for quantum information processing and Artificial Intelligence

Nanophotonics for Quantum Information Processing and Artific...

2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC)

作者： Nicholas C. Harris Lightmatter

ISBN: (数字)9784885523366

ISBN: (纸本)9781665486064

Compute requirements for state-of-the-art artificial intelligence applications are growing at a rate that is unprecedented in the field of high performance computing. New hardware technologies and architectures are being developed towards closing the gap between traditional compute advancement cycles, typically bound by an 18 month cadence, and what is required to power increasingly large parameter count neural networks. Silicon photonics is typically viewed as a communications platform. Here, we will discuss a brief history of optical computing in both the classical and quantum regimes, what's changed, and how silicon photonics can be applied to the problem of accelerating artificial intelligence algorithms. We will also discuss a novel, wafer-scale, switchable photonic fabric implemented in a hybrid CMOS photonics process with reticle-stitched transistors and photonic components and integrated III-V lasers.

关键词： Photonics Artificial intelligence quantum computing information processing Transistors Switching circuits Social networking (online)

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Performance of superconducting quantum computing chips under different architecture designs

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quantum information processing 2022年第7期21卷 1-14页

作者： Hu, Wei Yang, Yang Xia, Weiye Pi, Jiawei Huang, Enyi Zhang, Xin-Ding Xu, Hua Kunfeng Quantum Technol Co Ltd Shanghai Peoples R China Yiwei Quantum Technol Co Ltd Hefei Anhui Peoples R China South China Normal Univ Sch Phys & Telecommun Engn Guangdong Prov Key Lab Quantum Engn & Quantum Mat Guangzhou 510006 Peoples R China

Existing and near-term quantum computers can only perform two-qubit gating operations between physically connected qubits. Research has been done on compilers to rewrite quantum programs to match hardware constraints. However, the quantum processor architecture, in particular the qubit connectivity and topology, still lacks enough discussion, while it potentially has a huge impact on the performance of the quantum algorithms. We perform a quantitative and comprehensive study on the quantum processor performance under different qubit connectivity and topology. We select ten representative design models with different connectivities and topologies from quantum architecture design space and benchmark their performance by running a set of standard quantum algorithms. It is shown that a high-performance architecture almost always comes with a design with large connectivity, while the topology shows a weak influence on the performance in our experiment. Different quantum algorithms show different dependence on quantum chip connectivity and topologies. This work provides quantum computing researchers with a systematic approach to evaluating their processor design.

关键词： quantum computation quantum chip architecture Performance

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Winograd for NTT: A Case Study on Higher-Radix and Low-Latency Implementation of NTT for Post quantum Cryptography on FPGA

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第12期71卷 6396-6409页

作者： Mandal, Suraj Roy, Debapriya Basu Indian Inst Technol Kanpur Dept Comp Sci & Engn Kanpur 208016 India

Number Theoretic Transform (NTT) plays an important role in efficiently implementing lattice-based cryptographic algorithms like CRYSTALS-Kyber, Dilithium, and FALCON. Existing implementations of NTT for these algorithms are mostly based on radix-2 or radix-4 realization of Cooley-Tukey and Gentleman-Sande architectures. In this work, we explore an alternative method of performing NTT known as Winograd's NTT that requires fewer number of modular multipliers than the conventional Coole-Tukey/Gentleman-Sande for higher radix NTT. We have proposed three different low-latency implementations of Winograd's NTT, applicable to CRYSTALS-Dilithium, FALCON, and CRYSTALS-Kyber, respectively. Our first implementation of Winograd NTT focuses on radix- 16 NTT multiplication unit for polynomials of length 256 and can be directly used for CRYSTALS-Dilithium. The NTT of CRYSTALS-Dilithium is also benefited from our proposed K-RED modular multiplication. Our radix-16-based Winograd outperforms existing Cooley-Tukey/Gentleman-Sande based NTT multipliers of CRYSTALS-Dilithium. Our second implementation of NTT is based on radix- 8 Winograd structure with a novel modular multiplication method that targets polynomials of length 512 and can be directly applied for FALCON. For CRYSTALS-Kyber, we have designed a radix-16 Winograd Butterfly Unit (BFU) that can be configured as two parallel radix-8 Winograd BFUs during mixed-radix computation. To the best of our knowledge, this is the first work that applied the Winograd technique for NTT multiplication for post-quantum secure lattice-based cryptographic algorithms.

关键词： Polynomials Logic gates Signal processing algorithms Pulse width modulation Low latency communication Memory management Adders Pipelines Homomorphic encryption Field programmable gate arrays Number theoretic transformation (NTT) CRYSTALS-Dilithium FALCON CRYSTALS-Kyber Winograd

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quantum Bayesian Optimization 37

Quantum Bayesian Optimization

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37th Conference on Neural information processing Systems (NeurIPS)

作者： Dai, Zhongxiang Lau, Gregory Kang Ruey Verma, Arun Shu, Yao Low, Bryan Kian Hsiang Jaillet, Patrick Natl Univ Singapore Dept Comp Sci Singapore Singapore CNRS CREATE 1 Create Way08-01 Create Tower Singapore 138602 Singapore MIT Dept Elect & Comp Sci Cambridge MA USA

ISBN: (纸本)9781713899921

Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent method for optimizing complicated black-box reward functions. Various BO algorithms have been theoretically shown to enjoy upper bounds on their cumulative regret which are sub-linear in the number T of iterations, and a regret lower bound of Omega(root T) has been derived which represents the unavoidable regrets for any classical BO algorithm. Recent works on quantum bandits have shown that with the aid of quantum computing, it is possible to achieve tighter regret upper bounds better than their corresponding classical lower bounds. However, these works are restricted to either multi-armed or linear bandits, and are hence not able to solve sophisticated real-world problems with non-linear reward functions. To this end, we introduce the quantum-Gaussian process-upper confidence bound (Q-GP-UCB) algorithm. To the best of our knowledge, our Q-GP-UCB is the first BO algorithm able to achieve a regret upper bound of O(poly log T), which is significantly smaller than its regret lower bound of Omega(root T) in the classical setting. Moreover, thanks to our novel analysis of the confidence ellipsoid, our Q-GP-UCB with the linear kernel achieves a smaller regret than the quantum linear UCB algorithm from the previous work. We use simulations, as well as an experiment using a real quantum computer, to verify that the theoretical quantum speedup achieved by our Q-GP-UCB is also potentially relevant in practice.

关键词： quantum computers

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Smart Machine Vision for Universal Spatial-Mode Reconstruction

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Huerta-Morales, Jose D. You, Chenglong Magana-Loaiza, Omar S. Dong, Shi-Hai Leon-Montiel, Roberto de J. Quiroz-Juarez, Mario A. Univ Nacl Autonoma Mexico Inst Ciencias Nucl Mexico City 04510 Mexico Louisiana State Univ Dept Phys & Astron Quantum Photon Lab Baton Rouge LA 70803 USA Huzhou Univ Res Ctr Quantum Phys Huzhou 310003 Peoples R China UPALM Inst Politecn Nacl Lab Cienciasde Informac Cuant CIC Mexico City 07700 Mexico Univ Nacl Autonoma Mexico Ctr Fis Aplicada & Tecnol Avanzada Queretaro 76230 Mexico

Structured light beams, in particular, those carrying orbital angular momentum (OAM), have gained a lot of attention due to their potential for enlarging the transmission capabilities of communication systems. However, the use of OAM-carrying light in communications faces two major problems, namely distortions introduced during propagation in disordered media, such as the atmosphere or optical fibers, and the large divergence that high-order OAM modes experience. While the use of nonorthogonal modes may offer a way to circumvent the divergence of high-order OAM fields, artificial intelligence (AI) algorithms have shown promise for solving the mode-distortion issue. Unfortunately, current AI-based algorithms make use of large-amount data-handling protocols that generally lead to large processing time and high power consumption. Here, we show that a low-power, low-cost image sensor can act as an artificial neural network that simultaneously detects and reconstructs distorted OAM-carrying beams. We demonstrate the capabilities of our device by reconstructing (with a 95% efficiency) individual Vortex, Laguerre-Gaussian (LG), and Bessel modes, as well as hybrid (nonorthogonal) coherent superpositions of such modes. Our work provides a potentially useful basis for the development of low-power-consumption, light-based communication devices.

关键词： Accuracy Laser beams Image reconstruction Optical distortion Optical fiber networks Optical beams Convolutional neural networks Optical fibers Deep learning Crosstalk Artificial intelligence (AI) neural networks smart machine-vision device spatial-mode reconstruction

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quantum k-means algorithm based on Manhattan distance

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quantum information processing 2022年第1期21卷 1-10页

作者： Wu, Zhihao Song, Tingting Zhang, Yanbing Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin 541004 Peoples R China

Traditional k-means algorithm measures the Euclidean distance between any two data points, but it is not applicable in many scenarios, such as the path information between two cities, or when there are some obstacles between two data points. To solve the problems, we propose a quantum k-means algorithm based on Manhattan distance (QKMM). The main two steps of the QKMM algorithm are calculating the distance between each training vector and k cluster centroids, and choosing the closest cluster centroid. The quantum circuit is designed, and the time complexity is O(log(Nd) + 2 n root k), where N is number of training vectors, d is number of features for each training vector, n is number of bits for each feature, and k is the number of clustering classes. Different from other quantum k-means algorithms, our algorithm has wide applications and reduces the complexity. Compared with classical k-means algorithm, our algorithm reaches quadratic speedup.

关键词： quantum machine learning quantum algorithm quantum computation

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Multi-parameter optimization of polarization gradient cooling for 87 Rb atoms based on reinforcement learning

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OPTICS EXPRESS 2024年第23期32卷 40364-40374页

作者： Liang, Changwen Gao, Shaojun Liu, Jixun Liu, Jixun Yan, Shuhua Yang, Jun Zhu, Lingxiao Ma, Xiaoxiao Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China Natl Univ Def Technol Interdisciplinary Ctr Quantum Informat Changsha 410073 Peoples R China

Polarization gradient cooling (PGC) plays an important role in many cold atom applications including the formation of Bose-Einstein condensates (BECs) and cooling of single atoms. Traditional parameter optimization of PGC usually relies on subjective expertise, faces challenges in fine manipulation, and exhibits low optimization efficiency. Here, we propose a segmented control method that differs from the traditional PGC process by expanding the experiment parameters from 3 to 30. Subsequently, the conventional timing optimization problem is reformulated as a Markov decision process (MDP), and the experiment parameters are optimized using a reinforcement learning model. With proper settings of hyper parameters, the learning process exhibits good convergence and powerful parameter exploration capabilities. Finally, we capture similar to 4.3 x 108 cold atoms, with a phase space density of similar to 7.1 x 10-4 at a temperature of similar to 3.7 mu K in similar to 18.8 min. Our work paves the way for the intelligent preparation of degenerate quantum gas. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Bose Einstein condensates Genetic algorithms information processing Machine learning Phase space analysis methods Tunable diode lasers

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Tensor Networkquantum Virtual Machine for Simulating quantum Circuits at Exascale

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ACM TRANSACTIONS ON quantum COMPUTING 2023年第1期4卷 1-21页

作者： Thien Nguyen Lyakh, Dmitry Dumitrescu, Eugene Clark, David Larkin, Jeff McCaskey, Alexander Oak Ridge Natl Lab Quantum Comp Inst Oak Ridge TN 37830 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37830 USA Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN USA NVIDIA Corp Santa Clara CA USA

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. We present a modernized version of the Tensor Network quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google's Sycamore superconducting architecture.

关键词： quantum computing quantum simulation high performance computing

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Boosting for quantum weak learners

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quantum information processing 2022年第6期21卷 1-22页

作者： Ohno, Hiroshi Toyota Cent Res & Dev Labs Inc 41-1 Yokomichi Nagakute Aichi 4801192 Japan

In this study, the use of boosting techniques is empirically examined to determine to what extent quantum weak learners can be improved for binary classification tasks. In classical or quantum boosting, weak learners are treated as hybrid quantum-classical learners when realized on quantum simulators or NISQ devices. For hybrid quantum-classical learners, classical algorithms are used to train the learners. In the quantum boosting method proposed in this study, quantum mean estimation using quantum amplitude estimation is applied and conditions for ensuring a strong learner are presented. In addition, a quantum perceptron using quantum phase estimation is proposed as an easy-to-implement weak learner. Experiments were conducted on two binary classification tasks (binary and continuous feature spaces), the results of which showed that the resultant learners behaved as strong learners.

关键词： Boosting Weak learner Perceptron Phase estimation Amplitude estimation NISQ

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QBFO-BOMP Based Channel Estimation Algorithm for mmWave Massive MIMO Systems

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1789-1804页

作者： Xiaoli Jing Xianpeng Wang Xiang Lan Ting Su State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan UniversityHaikou570228China

At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high *** bacterial foraging optimization(BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization *** the BFO-based algorithm is easy to fall into local ***,this paper proposes the quantum bacterial foraging optimization(QBFO)-binary orthogonal matching pursuit(BOMP)channel estimation algorithm to the problem of local ***,the binary matrix is constructed according to whether atoms are selected or *** the support set of the sparse signal is recovered according to the BOMP-based ***,the QBFO-based algorithm is used to obtain the estimated channel *** optimization function of the least squares method is taken as the fitness *** on the communication between the quantum bacteria and the fitness function value,chemotaxis,reproduction and dispersion operations are carried out to update the bacteria *** results showthat compared with other algorithms,the estimationmechanism based onQBFOBOMP algorithm can effectively improve the channel estimation performance of millimeter wave(mmWave)massive multiple input multiple output(MIMO)***,the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.

关键词： Channel estimation bacterial foraging optimization quantum bacterial foraging optimization binary orthogonal matching pursuit massive MIMO

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