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Laser-Seeding Attack in quantum Key Distribution

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Physical Review Applied 2019年第6期12卷 064043-064043页

作者： Anqi Huang Álvaro Navarrete Shi-Hai Sun Poompong Chaiwongkhot Marcos Curty Vadim Makarov Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People’s Republic of China Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada N2L 3G1 EI Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo E-36310 Spain School of Physics and Astronomy Sun Yat-Sen University Zhuhai 519082 People’s Republic of China Department of Physics and Astronomy University of Waterloo Waterloo Ontario Canada N2L 3G1 Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia

quantum key distribution (QKD) based on the laws of quantum physics allows the secure distribution of secret keys over an insecure channel. Unfortunately, imperfect implementations of QKD compromise its information-theoretical security. Measurement-device-independent quantum key distribution (MDI QKD) is a promising approach to remove all side channels from the measurement unit, which is regarded as the “Achilles’ heel” of QKD. An essential assumption in MDI QKD is, however, that the sources are trusted. Here we experimentally demonstrate that a practical source based on a semiconductor laser diode is vulnerable to a laser-seeding attack, in which light injected from the communication line into the laser results in an increase of the intensities of the prepared states. The unnoticed increase of intensity may compromise the security of QKD, as we show theoretically for the prepare-and-measure decoy-state BB84 and MDI QKD protocols. Our theoretical security analysis is general and can be applied to any vulnerability that increases the intensity of the emitted pulses. Moreover, a laser-seeding attack might be launched as well against decoy-state-based quantum cryptographic protocols beyond QKD.

关键词： Optical quantum information processing Optoelectronics Photonics quantum algorithms quantum communication quantum cryptography Semiconductor quantum optics Photodiodes

quantum architecture search with meta-learning

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arXiv 2021年

作者： He, Zhimin Chen, Chuangtao Li, Lvzhou Zheng, Shenggen Situ, Haozhen School of Electronic and Information Engineering Foshan University Foshan528000 China School of Mechatronic Engineering and Automation Foshan University Foshan528000 China Institute of Quantum Computing and Computer Science Theory School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Peng Cheng Laboratory Shenzhen518055 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

Variational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling and quantum machine learning models. The performances of VQAs largely depend on the architecture of parameterized quantum circuits (PQCs). quantum architecture search (QAS) aims to automate the design of PQCs in different VQAs with classical optimization algorithms. However, current QAS algorithms do not use prior experiences and search the quantum architecture from scratch for each new task, which is inefficient and time consuming. In this paper, we propose a meta quantum architecture search (MetaQAS) algorithm, which learns good initialization heuristics of the architecture (i.e., meta-architecture), along with the meta-parameters of quantum gates from a number of training tasks such that they can adapt to new tasks with a small number of gradient updates, which leads to fast learning on new tasks. The proposed MetaQAS can be used with arbitrary gradient-based QAS algorithms. Simulation results of variational quantum compiling on three- and four-qubit circuits show that the architectures optimized by MetaQAS converge much faster than a state-of-the-art gradient-based QAS algorithm, namely DQAS. MetaQAS also achieves a better solution than DQAS after fine-tuning of gate parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning algorithms

Variational quantum compiling with double Q-learning

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arXiv 2021年

作者： He, Zhimin Li, Lvzhou Zheng, Shenggen Li, Yongyao Situ, Haozhen School of Electronic and Information Engineering Foshan University Foshan528000 China Peng Cheng Laboratory Shenzhen518055 China Institute of Quantum Computing and Computer Science Theory School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China School of Physics and Optoelectronic Engineering Foshan University Foshan528000 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U. It is a crucial stage for the running of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. However, the space for structure exploration of quantum circuit is enormous, resulting in the requirement of human expertise, hundreds of experimentations or modifications from existing quantum circuits. In this paper, we propose a variational quantum compiling (VQC) algorithm based on reinforcement learning (RL), in order to automatically design the structure of quantum circuit for VQC with no human intervention. An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q-learning with Ε-greedy exploration strategy and experience replay. At first, the agent randomly explores a number of quantum circuits with different structures, and then iteratively discovers structures with higher performance on the learning task. Simulation results show that the proposed method can make exact compilations with less quantum gates compared to previous VQC algorithms. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices, and enable quantum algorithms especially for complex algorithms to be executed within coherence time. © 2021, CC BY.

关键词： Reinforcement learning

Gsqas: Graph Self-Supervised quantum Architecture Search

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SSRN

SSRN 2023年

作者： He, Zhimin Deng, Maijie Zheng, Shenggen Li, Lvzhou Situ, Haozhen School of Electronic and Information Engineering Foshan University Foshan528000 China School of Mechatronic Engineering and Automation Foshan University Foshan528000 China Peng Cheng Laboratory Shenzhen518055 China Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

quantum Architecture Search (QAS) is a promising approach for designing quantum circuits specifically tailored for variational quantum algorithms (VQAs). However, existing QAS algorithms require calculating the ground-truth performances of a substantial number of quantum circuits during the search process, rendering them computationally demanding and limiting their applicability to large-scale quantum circuits. Recently, a predictor-based QAS has been proposed to address this challenge by estimating circuit performance directly based on their structures using a predictor trained on a set of labeled quantum circuits. However, the predictor is trained by purely supervised learning, which suffers from poor generalization ability when labeled training circuits are scarce. It is highly time-consuming to obtain a substantial number of labeled quantum circuits because the gate parameters of quantum circuits need to be optimized until convergence to obtain their ground-truth performances. To overcome these limitations, we propose GSQAS, a graph self-supervised QAS that trains a predictor by self-supervised learning. Specifically, we first pre-train a graph encoder using a well-designed pretext task on a large number of unlabeled quantum circuits, aiming to generate meaningful representations of quantum circuits. Subsequently, the downstream predictor is trained on a small set of quantum circuits' representations and their corresponding labels. Once the encoder is trained, it becomes applicable to various downstream tasks. To effectively encode spatial topology information and avoid huge dimensions of feature vectors for large-scale quantum circuits, we propose a graph-based encoding scheme for quantum circuits. Simulation results on QAS for variational quantum eigensolver and quantum state classification demonstrate that GSQAS outperforms the state-of-the-art predictor-based QAS, yielding superior performance with fewer labeled circuits. © 2023, The Authors. All rights rese

关键词： Timing circuits

Improved rate-distance trade-offs for quantum codes with restricted connectivity

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arXiv 2023年

作者： Baspin, Nouédyn Guruswami, Venkatesan Krishna, Anirudh Li, Ray Centre for Engineered Quantum Systems School of Physics University of Sydney NSW2006 Australia Departments of EECS and Mathematics The Simons Institute for the Theory of Computing UC Berkeley BerkeleyCA94709 United States Department of Computer Science Stanford Institute for Theoretical Physics Stanford University StanfordCA94305 United States Department of EECS UC Berkeley BerkeleyCA94709 United States

For quantum error-correcting codes to be realizable, it is important that the qubits subject to the code constraints exhibit some form of limited connectivity. The works of Bravyi & Terhal [6] (BT) and Bravyi, Poulin & Terhal [5] (BPT) established that geometric locality constrains code properties—for instance Jn, k, dK quantum codes defined by local checks on the D-dimensional lattice must obey kd2/(D−1) ≤ O(n). Baspin and Krishna [3] studied the more general question of how the connectivity graph associated with a quantum code constrains the code parameters. These trade-offs apply to a richer class of codes compared to the BPT and BT bounds, which only capture geometrically-local codes. We extend and improve this work, establishing a tighter dimension-distance trade-off as a function of the size of separators in the connectivity graph. We also obtain a distance bound that covers all stabilizer codes with a particular separation profile, rather than only LDPC codes. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

GSQAS: Graph Self-supervised quantum Architecture Search

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arXiv 2023年

quantum Architecture Search (QAS) is a promising approach to designing quantum circuits for variational quantum algorithms (VQAs). However, existing QAS algorithms require to evaluate a large number of quantum circuits during the search process, which makes them computationally demanding and limits their applications to large-scale quantum circuits. Recently, predictor-based QAS has been proposed to alleviate this problem by directly estimating the performances of circuits according to their structures with a predictor trained on a set of labeled quantum circuits. However, the predictor is trained by purely supervised learning, which suffers from poor generalization ability when labeled training circuits are scarce. It is very time-consuming to obtain a large number of labeled quantum circuits because the gate parameters of quantum circuits need to be optimized until convergence to obtain their ground-truth performances. To overcome these limitations, we propose GSQAS, a graph self-supervised QAS, which trains a predictor based on self-supervised learning. Specifically, we first pre-train a graph encoder on a large number of unlabeled quantum circuits using a well-designed pretext task in order to generate meaningful representations of circuits. Then the downstream predictor is trained on a small number of quantum circuits’ representations and their labels. Once the encoder is trained, it can apply to different downstream tasks. In order to better encode the spatial topology information and avoid the huge dimension of feature vectors for large-scale quantum circuits, we design a scheme to encode quantum circuits as graphs. Simulation results on searching circuit structures for variational quantum eigensolver and quantum state classification show that GSQAS outperforms the state-of-the-art predictor-based QAS, achieving better performance with fewer labeled circuits. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

Variational learning algorithms for quantum query complexity

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arXiv 2022年

作者： Wu, Zipeng Hou, Shi-Yao Zhang, Chao Li, Lvzhou Zeng, Bei Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu610068 China Shenzhen SpinQ Technology Co. Ltd. Shenzhen China Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

quantum query complexity is pivotal in the analysis of quantum algorithms, encompassing wellknown examples like search and period-finding algorithms. These algorithms typically involve a sequence of unitary operations and oracle calls dependent on an input variable. In this study, we introduce a variational learning approach to explore quantum query complexity. Our method employs an efficient parameterization of the unitary operations and utilizes a loss function derived from the algorithm's error probability. We apply this technique to various quantum query complexities, notably devising a new algorithm that resolves the 5-bit Hamming modulo problem with four queries, addressing an open question from arXiv:2112.14682. This finding is corroborated by a Semidefinite Programming (SDP) approach. Our numerical method exhibits superior memory efficiency compared to SDP and can identify quantum query algorithms that require a smaller workspace register dimension, an aspect not optimized by SDP. These advancements present a significant step forward in the practical application and understanding of quantum query algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

quantum gravity in the lab: Teleportation by size and traversable wormholes

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arXiv 2019年

作者： Brown, Adam R. Gharibyan, Hrant Leichenauer, Stefan Lin, Henry W. Nezami, Sepehr Salton, Grant Susskind, Leonard Swingle, Brian Walter, Michael Google Mountain ViewCA94043 United States Department of Physics Stanford University StanfordCA94305 United States Institute for Quantum Information and Matter Caltech PasadenaCA91125 United States Physics Department Princeton University PrincetonNJ08540 United States Institute for Quantum Information and Matter Caltech PasadenaCA91125 United States Amazon Quantum Solutions Lab SeattleWA98170 United States AWS Center for Quantum Computing PasadenaCA91125 United States Condensed Matter Theory Center Joint Center for Quantum Information and Computer Science Maryland Center for Fundamental Physics Department of Physics University of Maryland College ParkMD20742 United States Korteweg-de Vries Institute for Mathematics Institute for Theoretical Physics Institute for Logic Language and Computation & QuSoft University of Amsterdam Netherlands

With the long-term goal of studying models of quantum gravity in the lab, we propose holographic teleportation protocols that can be readily executed in table-top experiments. These protocols exhibit similar behavior to that seen in the recent traversable wormhole constructions of [1, 2]: information that is scrambled into one half of an entangled system will, following a weak coupling between the two halves, unscramble into the other half. We introduce the concept of teleportation by size to capture how the physics of operator-size growth naturally leads to information transmission. The transmission of a signal through a semi-classical holographic wormhole corresponds to a rather special property of the operator-size distribution we call size winding. For more general systems (which may not have a clean emergent geometry), we argue that imperfect size winding is a generalization of the traversable wormhole phenomenon. In addition, a form of signaling continues to function at high temperature and at large times for generic chaotic systems, even though it does not correspond to a signal going through a geometrical wormhole, but rather to an interference effect involving macroscopically different emergent geometries. Finally, we outline implementations feasible with current technology in two experimental platforms: Rydberg atom arrays and trapped ions. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

Distributed Shor’s algorithm

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arXiv 2022年

作者： Xiao, Ligang Qiu, Daowen Luo, Le Mateus, Paulo Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Physics and Astronomy Sun Yat-sen University Zhuhai519082 China QUDOOR Technologies Inc. Guangzhou China Instituto de Telecomunicações Departamento de Matemática Instituto Superior Técnico Av. Rovisco Pais Lisbon1049-001 Portugal

Shor’s algorithm is one of the most important quantum algorithm proposed by Peter Shor [Proceedings of the 35th Annual Symposium on Foundations of computer Science, 1994, pp. 124–134]. Shor’s algorithm can factor a large integer with certain probability and costs polynomial time in the length of the input integer. The key step of Shor’s algorithm is the order-finding algorithm. Specifically, given an L-bit integer N, we first randomly pick an integer a with gcd(a,N) = 1, the order of a modulo N is the smallest positive integer r such that ar ≡ 1(modN). The order-finding algorithm in Shor’s algorithm first uses quantum operations to obtain an estimation of s/r for some s ∈ {0,1,···,r − 1}, then r is obtained by means of classical algorithms. In this paper, we propose a distributed Shor’s algorithm. The difference between our distributed algorithm and the traditional order-finding algorithm is that we use two quantum computers separately to estimate partial bits of s/r for some s ∈ {0,1,···,r − 1}. To ensure their measuring results correspond to the same , we need employ quantum teleportation. We integrate the measuring results via classical post-processing. After that, we get an estimation of s/r with high precision. Compared with the r traditional Shor’s algorithm that uses multiple controlling qubits, our algorithm reduces nearly L/2 qubits and reduces the circuit depth of each computer. Copyright © 2022, The Authors. All rights reserved.

关键词： Qubits