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Mitigated barren plateaus in the time-nonlocal optimization of analog quantum-algorithm protocols

Physical Review Research 2024年第1期6卷 013076-013076页

作者： Lukas Broers Ludwig Mathey Center for Optical Quantum Technologies University of Hamburg 22761 Hamburg Germany Institute for Quantum Physics University of Hamburg 22761 Hamburg Germany The Hamburg Center for Ultrafast Imaging 22761 Hamburg Germany

quantum machine learning has emerged as a promising utilization of near-term quantum computation devices. However, algorithmic classes such as variational quantum algorithms have been shown to suffer from barren plateaus due to vanishing gradients in their parameters spaces. We present an approach to quantum algorithm optimization that is based on trainable Fourier coefficients of Hamiltonian system parameters. Our ansatz is exclusive to the extension of discrete quantum variational algorithms to analog quantum optimal control schemes and is nonlocal in time. We demonstrate the viability of our ansatz on the objectives of compiling the quantum Fourier transform and preparing ground states of random problem Hamiltonians. In comparison to the temporally local discretization ansätze in quantum optimal control and parametrized circuits, our ansatz exhibits faster and more consistent convergence. We uniformly sample objective gradients across the parameter space and find that in our ansatz the variance decays at a nonexponential rate with the number of qubits, while it decays at an exponential rate in the temporally local benchmark ansatz. This indicates the mitigation of barren plateaus in our ansatz. We propose our ansatz as a viable candidate for near-term quantum machine learning.

关键词： quantum algorithms & computation quantum computation quantum control quantum information architectures & platforms quantum information processing with continuous variables

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quantum Entanglement Inspired Correlation Learning for Classification 26th

Quantum Entanglement Inspired Correlation Learning for Class...

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26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Zhang, Junwei Li, Zhao Wang, Juan Wang, Yinghui Hu, Shichang Xiao, Jie Li, Zhaolin Tianjin Univ Coll Intelligence & Comp1 Tianjin Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Hangzhou Yugu Technol Co Ltd Hangzhou Peoples R China Tianjin Xiniu Huaan Technol Co Ltd Tianjin Peoples R China

ISBN: (纸本)9783031059360;9783031059353

Correlation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that between classical systems, quantum information processing methods show superiority that classical methods do not possess. In this paper, we study the virtue of entangled systems and propose a novel classification algorithm called quantum Entanglement inspired the Classification Algorithm (QECA). Particularly, we use the joint probability derived from entangled systems to model correlation between features and categories, that is, quantum Correlation (QC), and leverage it to develop a novel QC-induced Multi-layer Perceptron framework for classification tasks. Experimental results on four datasets from diverse domains show that QECA is significantly better than the baseline methods, which demonstrates that QC revealed by entangled systems can improve the classification performance of traditional algorithms.

关键词： quantum correlation quantum-inspired algorithms Classification algorithm

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quantum Computing's Double-Edged Sword Unravelling the Vulnerabilities in quantum Key Distribution for Enhanced Network Security

Quantum Computing's Double-Edged Sword Unravelling the Vulne...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Anantraj, I. Umarani, B. Karpagavalli, C. Usharani, C. Lakshmi, S. Jothi Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Namakkal India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Rajapalayam India

ISBN: (纸本)9798350319088

quantum computing has emerged as both a boon and a bane in the realm of network security. This paper delves into the intriguing interplay between quantum computing and the vulnerabilities of quantum Key Distribution (QKD) protocols, a cornerstone of secure quantum communication. While QKD offers unmatched security through the principles of quantum mechanics, it is not impervious to attacks in the age of rapidly advancing quantum computing. We examine these vulnerabilities, highlighting the challenges of ensuring the long-term security of quantum networks in a post-quantum world. Through a comprehensive exploration of threat models and the practicality of QKD, we uncover potential weaknesses in quantum network security. In particular, we investigate QKD's susceptibility to evolving quantum algorithms, hardware vulnerabilities, and post-processing security concerns. This work aims to provide a roadmap for understanding and addressing these vulnerabilities, ensuring the continued robustness of quantum communication networks, and fostering the development of post-quantum cryptographic solutions. As quantum computing advances, its implications for quantum network security are far-reaching. This study underlines the critical need for research and innovation in securing quantum communication. By unravelling the vulnerabilities within quantum Key Distribution and exploring post-quantum cryptographic alternatives, this research contributes to the development of more resilient and secure quantum networks, safeguarding sensitive information and communications in an increasingly quantum-enabled world. © 2023 IEEE.

关键词： quantum cryptography

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Distributed quantum Learning with co-Management in a Multi-tenant quantum System

Distributed Quantum Learning with co-Management in a Multi-t...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： D'Onofrio, Anthony Hossain, Amir Santana, Lesther Machlovi, Naseem Stein, Samuel Liu, Jinwei Li, Ang Mao, Ying Fordham University Computer and Information Science Department United States United States Florida A&M University United States

ISBN: (纸本)9798350324457

The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications Given its immense potential, quantum-based computing systems have attracted increasing attention with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting. © 2023 IEEE.

关键词： Distributed quantum Computing Multi-node quantum quantum Resource Management

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qLEET: visualizing loss landscapes, expressibility, entangling power and training trajectories for parameterized quantum circuits

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quantum information processing 2023年第6期22卷 256-256页

作者： Azad, Utkarsh Sinha, Animesh Int Inst Informat Technol Ctr Computat Nat Sci & Bioinformat Hyderabad India Int Inst Informat Technol Ctr Quantum Sci & Technol Hyderabad India

We present qLEET, an open-source Python package for studying parameterized quantum circuits (PQCs), which are widely used in various variational quantum algorithms (VQAs) and quantum machine learning (QML) algorithms. qLEET enables computation of properties such as expressibility and entangling power of a PQC by studying its entanglement spectrum and the distribution of parameterized states produced by it. Furthermore, it allows users to visualize the training trajectories of PQCs along with high-dimensional loss landscapes generated by them for different objective functions. It supports quantum circuits and noise models built using popular quantum computing libraries such as Qiskit, Cirq, and PyQuil. In our work, we demonstrate how qLEET provides opportunities to design and improve hybrid quantum-classical algorithms by utilizing intuitive insights from the ansatz capability and structure of the loss landscape.

关键词： quantum computing quantum software Parameterized quantum circuits Hybrid quantum-classical algorithms

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Photoacoustic identification of blood authenticity based on quantum-behaved particle swarm optimized wavelet neural network

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JOURNAL OF BIOPHOTONICS 2022年第5期15卷 e202100309-e202100309页

作者： Liu, Tao Ren, Zhong Wu, Junli Xiong, Chengxin Peng, Wenping Jiangxi Sci & Technol Normal Univ Key Lab Opt Elect & Commun Nanchang 330038 Jiangxi Peoples R China Jiangxi Sci & Technol Normal Univ Key Lab Opt Elect Detect & Informat Proc Nanchang Nanchang Jiangxi Peoples R China

To accurately identify the blood authenticity, a set of photoacoustic detection system was established. In experiments, five kinds of blood in total of 125 groups were used, the time-resolved photoacoustic signals and peak-to-peak spectra were obtained in 700 to 1064 nm. Experimental results showed the accurate identification of blood authenticity was limited due to overlap of signals and spectra. To solve the problem, wavelet neural network (WNN) was employed to supervised train peak-to-peak spectra of 100 samples. The correct rate was 72% for 25 test samples. To improve correct rate, the parameters of WNN were optimized by quantum-behaved particle swarm optimization (QPSO) algorithm. Meanwhile, the effects of neurons number, learning rate factors, iteration times and training times on correct rate were studied and compared with WNN and WNN-PSO algorithms. Results showed the correct rate of WNN-QPSO was increased to 96%. Then, three kinds of dynamic contraction-expansion coefficients were used. Under the optimal dynamic coefficient, the correct rate reached 100%. Moreover, the truncated mean stabilization strategy (TMSS) was coupled to improve the convergent speed. Finally, 10 algorithms were compared. Results demonstrated that photoacoustic spectroscopy combined with WNN-QPSO coupled with TMSS and dynamic contraction-expansion coefficient had an excellent performance in the identification of blood authenticity.

关键词： blood authenticity correct rate photoacoustic spectroscopy quantum-behaved particle swarm optimization wavelet neural network

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Q-BEEP: quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum 23

Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson ...

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50th Annual International Symposium on Computer Architecture (ISCA)

作者： Stein, Samuel A. Wiebe, Nathan Ding, Yufei Ang, James Li, Ang Pacific Northwest Natl Lab Richland WA 99352 USA Univ Toronto Toronto ON Canada Univ Calif Santa Barbara Santa Barbara CA USA

ISBN: (纸本)9798400700958

quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processors' qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. quantum Error Correction (in-situ error mitigation) and quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors. IBM recently published an article stating that quantum Error Mitigation is the path to quantum computing usefulness. A recent work, namely HAMMER, demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they assumed that errors occur solely in local clusters, whereas we observe that at higher average Hamming distances this structure falls away. In this work, we show that such a correlated structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the proposed iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, a fidelity improvement of up to 17.8% is attained. Q-Beep is a light-weight post-processing technique that can be performed offline and rem

关键词： quantum Computing quantum Error Mitigation Bayesian Error Mitigation State Graphs Noisy Intermediate Scale quantum Computing quantum algorithms

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Performance analysis of a 448 Gbps PDM/WDM/16-QAM hybrid SMF/FSO system for last mile connectivity

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OPTICAL AND quantum ELECTRONICS 2023年第3期55卷 1-23页

作者： Singh, Mehtab Aly, Moustafa H. H. El-Mottaleb, Somia A. Abd Chandigarh Univ Univ Inst Engn Dept Elect & Commun Engn Mohali Punjab India Arab Acad Sci Technol & Maritime Transport Alexandria 1029 Egypt Alexandria Higher Inst Engn & Technol Alexandria Egypt

A combined wavelength division multiplexing (WDM)-polarization division multiplexing dual polarization (DP) in-phase quadrature modulation is proposed in hybrid single mode fiber (SMF)/free space optics (FSO) communication system to be used for last mile connectivity. Four WDM channels are used;each carries 112 Gbps information data. Additionally, 16-quadrature amplitude modulation signals are used in a DP in-phase quadrature modulation. Also, for SMF nonlinear losses and dispersion loss, digital signal processing algorithms are used to compensate nonlinear fiber losses, dispersion, and FSO channel losses. Furthermore, we assumed constant SMF length of 50 km with various ranges for the FSO link. External climate changes like clear air (CA), hazy, rainy, and foggy weather conditions are considered for evaluating the system performance. Both the bit error rate (BER) and percentage of error vector magnitude (EVM%) are used as performance metrics. The simulation results assure the longest transmission distance of 50 km SMF + 3.5 km FSO under CA and the shortest transmission distance of 50 km SMF + 0.675 km FSO under heavy fog with an overall capacity of 448 Gbps. At BER < 3.8 x 10(-3) and EVM% < 15%, these transmission ranges are conducted. Consequently, the proposed SMF/FSO system is suggested to be used in the fifth generation last-mile wide-area networks.

关键词： Free space optics Polarization division multiplexing Wavelength division multiplexing Quadrature amplitude modulation Single mode fiber Digital signal processing

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Feedback-Driven quantum Reservoir Computing for Time-Series Analysis

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PRX quantum 2024年第4期5卷 040325-040325页

作者： Kaito Kobayashi Keisuke Fujii Naoki Yamamoto Department of Applied Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan RIKEN Center for Quantum Computing (RQC) Hirosawa 2-1 Wako Saitama 351-0198 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan Quantum Computing Center Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan

quantum reservoir computing (QRC) is a highly promising computational paradigm that leverages quantum systems as a computational resource for nonlinear information processing. While its application to time-series analysis is eagerly anticipated, prevailing approaches suffer from the collapse of the quantum state upon measurement, resulting in the erasure of temporal input memories. Neither repeated initializations nor weak measurements offer a fundamental solution, as the former escalates the time complexity while the latter restricts the information extraction from the Hilbert space. To address this issue, we propose the feedback-driven QRC framework. This methodology employs projective measurements on all qubits for unrestricted access to the quantum state, with the measurement outcomes subsequently fed back into the reservoir to restore the memory of prior inputs. We demonstrate that our QRC successfully acquires the fading-memory property through the feedback connections, a critical aspect in time-series processing. Notably, analysis of measurement trajectories reveals three distinct phases depending on the feedback strength, with the memory performance maximized at the edge of chaos. We also evaluate the predictive capabilities of our QRC, demonstrating its suitability for forecasting signals originating from quantum spin systems.

关键词： Artificial intelligence quantum algorithms quantum circuits quantum computation quantum computing models quantum simulation

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Theoretical Analysis of the Memory-Efficient Matrix Storage Method for quantum Emulation Accelerators with Gate Fusion on FPGAs 17

Theoretical Analysis of the Memory-Efficient Matrix Storage ...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Xuan Hieu, Le Tran Luan, Pham Hoai Hai, Vu Tuan Trung Duong, Le Vu Nakashima, Yasuhiko University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Nara Institute of Science and Technology Ikoma Nara630-0192 Japan

ISBN: (纸本)9798331530471

quantum emulators play an important role in the development and testing of quantum algorithms, especially given the limitations of the current FTQC era. Developing high-speed, memory-optimized quantum emulators is a growing research trend, with gate fusion being a promising technique. However, existing gate fusion implementations often struggle to efficiently support large-scale quantum systems with a high number of qubits due to a lack of optimizations for the exponential growth in memory requirements. Therefore, this study proposes the MEMS (Memory-Efficient Matrix Storage) method for storing quantum operators and states, along with a MEMS-based quantum Emulator Accelerator (QEA) architecture that incorporates multiple processing elements (PEs) to accelerate tensor product and matrix multiplication computations in quantum emulation with gate fusion. The theoretical analysis of the QEA on the Xilinx ZCU102 FPGA, using varying numbers of PEs and different depths of unitary and local data memory, reveals a linear increase in memory depth with the number of qubits. This scaling highlights the potential of the MEMS-based QEA to accommodate larger quantum circuits, providing insights into selecting appropriate memory sizes and FPGA devices. Furthermore, the estimated performance of the QEA with PE counts ranging from 22 to 25 on the Xilinx ZCU102 FPGA demonstrates that increasing the number of PEs significantly reduces the computation cycle count for circuits with fewer than 18 qubits, making it substantially faster than previous works. © 2024 IEEE.

关键词： Qubits

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