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Modularized and Scalable Compilation for Double Quantum Dot Quatum Computing 双量子点量子计算的模块化和可扩展编译
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-10-13 DOI: 10.1088/2058-9565/acfe38
Run-hong He, Xu-Sheng Xu, Mark Byrd, Zhao-Ming Wang
Abstract Any quantum program on a realistic quantum device must be compiled into an executable form while taking into account the underlying hardware constraints. Stringent restrictions on architecture and control imposed by physical platforms make this very challenging. In this paper, based on the quantum variational algorithm, we propose a novel scheme to train an Ansatz circuit and realize high-fidelity compilation of a set of universal quantum gates for singlet-triplet qubits in semiconductor double quantum dots, a fairly heavily constrained system. Furthermore, we propose a scalable architecture for a modular implementation of quantum programs in this constrained systems and validate its performance with two representative demonstrations, the Grover’s algorithm for the database searching (static compilation) and a variant of variational quantum eigensolver for the Max-Cut optimization (dynamic compilation). Our methods are potentially applicable to a wide range of physical devices. This work constitutes an important stepping-stone for exploiting the potential for advanced and complicated quantum algorithms on near-term devices.
在实际的量子设备上,任何量子程序都必须在考虑底层硬件约束的情况下编译成可执行的形式。物理平台对架构和控制的严格限制使得这非常具有挑战性。本文基于量子变分算法,提出了一种新的方案来训练Ansatz电路,并实现了半导体双量子点(一个相当严格约束的系统)中单重态-三重态量子比特的一组通用量子门的高保真编译。此外,我们提出了一种可扩展的架构,用于在此受限系统中实现量子程序的模块化,并通过两个代表性演示验证其性能,即用于数据库搜索(静态编译)的Grover算法和用于最大割优化(动态编译)的变分量子特征解算器的变体。我们的方法可能适用于广泛的物理设备。这项工作为在近期设备上开发先进和复杂量子算法的潜力奠定了重要的基础。
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引用次数: 1
High-dimensional quantum key distribution using energy-time entanglement over 242 km partially deployed fiber 利用242公里部分部署光纤的能量-时间纠缠进行高维量子密钥分配
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-10-12 DOI: 10.1088/2058-9565/acfe37
Jingyuan Liu, Zhihao Lin, Dongning Liu, Xue Feng, Fang Liu, Kaiyu Cui, Yidong Huang, Wei Zhang
Abstract Entanglement-based quantum key distribution (QKD) is an essential ingredient in quantum communication, owing to the property of source-independent security and the potential on constructing large-scale quantum communication networks. However, implementation of entanglement-based QKD over long-distance optical fiber links is still challenging, especially over deployed fibers. In this work, we report an experimental QKD using energy-time entangled photon pairs that transmit over optical fibers of 242 km (including a section of 19 km deployed fibers). The QKD is realized through the protocol of dispersive-optics QKD (DO-QKD) with high-dimensional encoding to utilize coincidence counts more efficiently. A reliable, high-accuracy time synchronization technology for long-distance entanglement-based QKD is developed based on the distribution of optical pulses in quantum channels. Our system operates continuously for more than 7 d without active polarization or phase calibration. We ultimately generate secure keys with secure key rates of 0.22 bps and 0.06 bps in the asymptotic and finite-size regimes, respectively. It shows that entanglement-based DO-QKD is reliable for long-distance realization in the field if its high requirement on time synchronization is satisfied.
基于纠缠态的量子密钥分发(QKD)具有源无关的安全性和构建大规模量子通信网络的潜力,是量子通信的重要组成部分。然而,在长距离光纤链路上实现基于纠缠的QKD仍然具有挑战性,特别是在部署的光纤上。在这项工作中,我们报告了一个使用能量时间纠缠光子对的实验性量子密钥分配,该光子对通过242公里的光纤传输(包括一段19公里的部署光纤)。QKD是通过高维编码的色散光学QKD (DO-QKD)协议实现的,以更有效地利用符合计数。基于光脉冲在量子信道中的分布,提出了一种可靠、高精度的远距离纠缠量子密钥分配时间同步技术。我们的系统在没有主动极化或相位校准的情况下连续运行超过7天。我们最终在渐近和有限大小的情况下分别生成了安全密钥率为0.22 bps和0.06 bps的安全密钥。研究表明,在满足高时间同步要求的前提下,基于纠缠的DO-QKD在现场远距离实现是可靠的。
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引用次数: 2
Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions 用于加载任意函数傅里叶近似的线性深度量子电路
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-10-11 DOI: 10.1088/2058-9565/acfc62
Mudassir Moosa, Thomas Watts, Yiyou Chen, Abhijat Sarma, Peter L. McMahon
Abstract The ability to efficiently load functions on quantum computers with high fidelity is essential for many quantum algorithms, including those for solving partial differential equations and Monte Carlo estimation. In this work, we introduce the Fourier series loader (FSL) method for preparing quantum states that exactly encode multi-dimensional Fourier series using linear-depth quantum circuits. Specifically, the FSL method prepares a ( Dn )-qubit state encoding the 2 Dn -point uniform discretization of a D -dimensional function specified by a D -dimensional Fourier series. A free parameter, m , which must be less than n , determines the number of Fourier coefficients, <?CDATA $2^{D(m+1)}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mi>D</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>m</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msup> </mml:math> , used to represent the function. The FSL method uses a quantum circuit of depth at most <?CDATA $2(n-2)+lceil log_{2}(n-m) rceil + 2^{D(m+1)+2} -2D(m+1)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo stretchy="false">(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mo>+</mml:mo> <mml:mrow> <mml:mo>⌈</mml:mo> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>log</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mi>m</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>⌉</mml:mo> </mml:mrow> <mml:mo>+</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mi>D</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>m</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mo>+</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> <mml:mi>D</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>m</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> , which is linear in the number of Fourier coefficients, and linear in the number of qubits ( Dn ) despite the fact that the loaded function’s discretization is over exponentially many (2 Dn ) points. The FSL circuit consists of at most <?CDATA $Dn+2^{D(m+1)+1}-1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mi>D</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>m</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:math> single-qubit and <?CDATA $Dn(n+1)/2 + 2^{D(m+1)+1} - 3D(m+1) - 2$?> <mml:math xmlns:mml="http://www.w
在量子计算机上高效、高保真地加载函数的能力对于许多量子算法来说是必不可少的,包括求解偏微分方程和蒙特卡罗估计的量子算法。在这项工作中,我们介绍了傅立叶级数加载器(FSL)方法,用于使用线性深度量子电路制备精确编码多维傅立叶级数的量子态。具体而言,FSL方法制备了一个(Dn)-量子比特状态,编码由D维傅里叶级数指定的D维函数的2dn点均匀离散化。一个必须小于n的自由参数m决定了用来表示函数的傅里叶系数2d (m + 1)的数目。目前方法使用量子电路的深度最多2 (n−2)+⌈日志2 (n−m)⌉+ 2 D (m + 1) + 2−2 D (m + 1),这是傅里叶系数的线性数量,数量和线性量子比特(Dn)尽管加载函数的离散化是许多(Dn) 2点在成倍增长。FSL电路最多由dn + 2d (m + 1) + 1−1个单量子比特和dn (n + 1) / 2 + 2d (m + 1) + 1−3 D (m + 1)−2个双量子比特门组成;我们提出了一种经典的编译算法,在运行时间为0 (2 3 D (m + 1))的情况下确定给定傅里叶级数的FSL电路。FSL方法允许用有限多项的傅立叶级数很好地近似的复值函数的高精度加载。我们报告了无噪声量子电路模拟的结果,说明了FSL方法在20个量子比特上加载各种连续1D函数和不连续1D函数的能力,不忠实度分别小于10−6和10−3。我们还通过在量子H1-1和H1-2捕获离子量子计算机上进行的实验证明了FSL方法在近期量子计算机上的实用性:我们在3个量子位上加载了一个复值函数,保真度超过95%,以及在多达6个量子位上加载了各种一维实值函数,经典保真度≈99%,在10个量子位上加载了一个二维函数,经典保真度≈94%。
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引用次数: 4
Production of Fock mixtures in trapped ions for motional metrology 动态计量用捕获离子的Fock混合物的生产
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-10-10 DOI: 10.1088/2058-9565/ad01d7
Antonis Delakouras, Daniel Rodríguez, Javier Cerrillo
Abstract We present a protocol to produce a class of non-thermal Fock state mixtures in trapped ions. This class of states features a clear metrological advantage with respect to the ground state, thus overcoming the standard quantum limit without the need for full sideband cooling and Fock-state preparation on a narrow electronic transition. The protocol consists in the cyclic repetition of red-sideband, measurement and preparation laser pulses. By means of the Kraus map representation of the protocol, it is possible to relate the length of the red sideband pulses to the specific class of states that can be generated. With the help of numerical simulations, we analyze the parametric regime where these states can be reliably reproduced.
摘要提出了一种在俘获离子中产生一类非热Fock态混合物的方法。这类状态相对于基态具有明显的计量优势,从而克服了标准量子极限,而不需要在狭窄的电子跃迁上进行完整的边带冷却和fock态制备。该方案包括红边带、测量和制备激光脉冲的循环重复。通过协议的克劳斯映射表示,可以将红色边带脉冲的长度与可以生成的特定状态类联系起来。在数值模拟的帮助下,我们分析了这些状态可以可靠地再现的参数区。
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引用次数: 0
Quantum kernel evaluation via Hong-Ou-Mandel interference 基于Hong-Ou-Mandel干涉的量子核评估
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-10-09 DOI: 10.1088/2058-9565/acfba9
Cassandra Bowie, Sally Shrapnel, Michael Kewming
Abstract One of the fastest growing areas of interest in quantum computing is its use within machine learning methods, in particular through the application of quantum kernels. Despite this large interest, there exist very few proposals for relevant physical platforms to evaluate quantum kernels. In this article, we propose and simulate a protocol capable of evaluating quantum kernels using Hong–Ou–Mandel interference, an experimental technique that is widely accessible to optics researchers. Our proposal utilises the orthogonal temporal modes of a single photon, allowing one to encode multi-dimensional feature vectors. As a result, interfering two photons and using the detected coincidence counts, we can perform a direct measurement and binary classification. This physical platform confers an exponential quantum advantage also described theoretically in other works. We present a complete description of this method and perform a numerical experiment to demonstrate a sample application for binary classification of classical data.
量子计算中增长最快的领域之一是它在机器学习方法中的应用,特别是通过量子核的应用。尽管有这么大的兴趣,但很少有关于评估量子核的相关物理平台的建议。在本文中,我们提出并模拟了一种能够使用Hong-Ou-Mandel干涉(一种广泛用于光学研究人员的实验技术)评估量子核的协议。我们的方案利用单光子的正交时间模式,允许对多维特征向量进行编码。因此,干涉两个光子并利用检测到的符合计数,我们可以进行直接测量和二元分类。这种物理平台赋予了指数量子优势,在其他作品中也有理论上的描述。我们给出了该方法的完整描述,并进行了数值实验,以演示经典数据二值分类的示例应用。
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引用次数: 0
Diabatic quantum annealing for the frustrated ring model 受挫环模型的非绝热量子退火
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-10-01 DOI: 10.1088/2058-9565/acfbaa
Jeremy Côté, Frédéric Sauvage, Martín Larocca, Matías Jonsson, Lukasz Cincio, Tameem Albash
Abstract Quantum annealing (QA) is a continuous-time heuristic quantum algorithm for solving or approximately solving classical optimization problems. The algorithm uses a schedule to interpolate between a driver Hamiltonian with an easy-to-prepare ground state and a problem Hamiltonian whose ground state encodes solutions to an optimization problem. The standard implementation relies on the evolution being adiabatic: keeping the system in the instantaneous ground state with high probability and requiring a time scale inversely related to the minimum energy gap between the instantaneous ground and excited states. However, adiabatic evolution can lead to evolution times that scale exponentially with the system size, even for computationally simple problems. Here, we study whether non-adiabatic evolutions with optimized annealing schedules can bypass this exponential slowdown for one such class of problems called the frustrated ring model. For sufficiently optimized annealing schedules and system sizes of up to 39 qubits, we provide numerical evidence that we can avoid the exponential slowdown. Our work highlights the potential of highly-controllable QA to circumvent bottlenecks associated with the standard implementation of QA.
量子退火(QA)是一种求解或近似求解经典优化问题的连续时间启发式量子算法。该算法使用调度在具有易于准备的基态的驱动器哈密顿量和其基态编码优化问题的解的问题哈密顿量之间进行插值。标准的实现依赖于演化是绝热的:使系统高概率地保持在瞬时基态,并要求时间尺度与瞬时基态和激发态之间的最小能量间隙成反比。然而,绝热演化可能导致演化时间随系统大小呈指数级增长,即使对于计算简单的问题也是如此。在这里,我们研究了非绝热演化与优化退火计划是否可以绕过这种指数减速的一类问题,称为受挫环模型。对于充分优化的退火计划和高达39个量子位的系统大小,我们提供了数值证据,证明我们可以避免指数减速。我们的工作强调了高度可控的QA规避与QA标准实现相关的瓶颈的潜力。
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引用次数: 3
Federated quanvolutional neural network: a new paradigm for collaborative quantum learning 联合量子神经网络:协作量子学习的新范式
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-10-01 DOI: 10.1088/2058-9565/acfc61
Amandeep Singh Singh Bhatia, Sabre Kais, Muhammad A Alam
Abstract In recent years, the concept of federated machine learning has been actively driven by scientists to ease the privacy concerns of data owners. Currently, the combination of machine learning and quantum computing technologies is a hot industry topic and is positioned to be a major disruptor. It has become an effective new tool for reshaping several industries ranging from healthcare to finance. Data sharing poses a significant hurdle for large-scale machine learning in numerous industries. It is a natural goal to study the advanced quantum computing ecosystem, which will be comprised of heterogeneous federated resources. In this work, the problem of data governance and privacy is handled by developing a quantum federated learning approach, that can be efficiently executed on quantum hardware in the noisy intermediate-scale quantum era. We present the federated hybrid quantum–classical algorithm called a quanvolutional neural network with distributed training on different sites without exchanging data. The hybrid algorithm requires small quantum circuits to produce meaningful features for image classification tasks, which makes it ideal for near-term quantum computing. The primary goal of this work is to evaluate the potential benefits of hybrid quantum–classical and classical-quantum convolutional neural networks on non-independently and non-identically partitioned (Non-IID) and real-world data partitioned datasets among several healthcare institutions/clients. We investigated the performance of a collaborative quanvolutional neural network on two medical machine learning datasets, COVID-19 and MedNIST. Extensive experiments are carried out to validate the robustness and feasibility of the proposed quantum federated learning framework. Our findings demonstrate a decrease of 2%–39% times in necessary communication rounds compared to the federated stochastic gradient descent approach. The hybrid federated framework maintained a high classification testing accuracy and generalizability, even in scenarios where the medical data is unevenly distributed among clients.
近年来,为了缓解数据所有者对隐私的担忧,科学家们积极推动了联邦机器学习的概念。目前,机器学习和量子计算技术的结合是一个热门的行业话题,并被定位为一个主要的颠覆者。它已成为重塑从医疗保健到金融等多个行业的有效新工具。数据共享对许多行业的大规模机器学习构成了重大障碍。研究由异构联合资源组成的先进量子计算生态系统是一个自然的目标。在这项工作中,通过开发一种量子联邦学习方法来处理数据治理和隐私问题,该方法可以在嘈杂的中等规模量子时代的量子硬件上有效地执行。我们提出了一种联邦混合量子经典算法,称为量子神经网络,它在不同的地点进行分布式训练,而不交换数据。混合算法需要小的量子电路来产生有意义的特征,用于图像分类任务,这使其成为近期量子计算的理想选择。这项工作的主要目标是评估混合量子-经典和经典-量子卷积神经网络在几个医疗机构/客户之间的非独立和非相同分区(Non-IID)和现实世界数据分区数据集上的潜在优势。我们研究了协同量化神经网络在两个医疗机器学习数据集(COVID-19和MedNIST)上的性能。大量的实验验证了所提出的量子联邦学习框架的鲁棒性和可行性。我们的研究结果表明,与联邦随机梯度下降方法相比,必要的通信回合减少了2%-39%。混合联邦框架保持了较高的分类测试准确性和泛化性,即使在医疗数据在客户端之间分布不均匀的情况下也是如此。
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引用次数: 1
Efficient parallelization of quantum basis state shift 量子基态位移的高效并行化
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-09-28 DOI: 10.1088/2058-9565/acfab7
Ljubomir Budinski, Ossi Niemimäki, Roberto Zamora-Zamora, Valtteri Lahtinen
Abstract Basis state shift is central to many quantum algorithms, most notably the quantum walk. Efficient implementations are of major importance for achieving a quantum speedup for computational applications. We optimize the state shift algorithm by incorporating the shift in different directions in parallel. This provides a significant reduction in the depth of the quantum circuit in comparison to the currently known methods, giving a linear scaling in the number of gates versus working qubits in contrast to the quadratic scaling of the state-of-the-art method based on the quantum Fourier transform. For a one-dimensional array of size 2 n for n > 4, we derive the total number of 15 n + 74 two-qubit CX gates in the parallel circuit, using a total of 2 n 2 qubits including an ancilla register for the decomposition of multi-controlled gates. We focus on the one-dimensional and periodic shift, but note that the method can be extended to more complex cases.
基态转移是许多量子算法的核心,尤其是量子行走。高效的实现对于实现计算应用的量子加速至关重要。我们通过将不同方向的移动并行化来优化状态转移算法。与目前已知的方法相比,这大大减少了量子电路的深度,与基于量子傅里叶变换的最先进方法的二次缩放相比,门的数量与工作量子位的数量呈线性缩放。对于大小为2n的一维数组n >4,我们推导了并行电路中15n + 74个双量子位CX门的总数,总共使用2n−2个量子位,包括一个辅助寄存器,用于多控制门的分解。我们的重点是一维和周期位移,但注意,该方法可以扩展到更复杂的情况。
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引用次数: 0
Orbital Expansion Variational Quantum Eigensolver 轨道展开变分量子本征求解器
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-09-27 DOI: 10.1088/2058-9565/acf9c7
Yusen Wu, Zigeng Huang, Jinzhao Sun, Xiao Yuan, Jingbo B Wang, Dingshun Lv
Abstract Variational quantum eigensolver (VQE) has emerged as a promising method for investigating ground state properties in quantum chemistry, materials science, and condensed matter physics. However, the conventional VQE method generally lacks systematic improvement and convergence guarantees, particularly when dealing with strongly correlated systems. In light of these challenges, we present a novel framework called orbital expansion VQE (OE-VQE) to address these limitations. The key idea is to devise an efficient convergence path by utilizing shallower quantum circuits, starting from a highly compact active space and gradually expanding it until convergence to the ground state is achieved. To validate the effectiveness of the OE-VQE framework, we conducted benchmark simulations on several small yet representative molecules, including the H 6 chain, H 10 ring and N 2 . The simulation results demonstrate that our proposed convergence paths significantly enhance the performance of conventional VQE. Overall, our work sheds valuable insight into the simulation of molecules based on shallow quantum circuits, offering a promising avenue for advancing the efficiency and accuracy of VQE approaches in tackling complex molecular systems.
变分量子本征求解器(VQE)已经成为研究量子化学、材料科学和凝聚态物理中基态性质的一种很有前途的方法。然而,传统的VQE方法普遍缺乏系统性改进和收敛性保证,特别是在处理强相关系统时。鉴于这些挑战,我们提出了一种称为轨道扩展VQE (OE-VQE)的新框架来解决这些限制。关键思想是利用较浅的量子电路设计一个有效的收敛路径,从一个高度紧凑的有源空间开始,逐渐扩大它,直到收敛到基态。为了验证OE-VQE框架的有效性,我们对几个小而有代表性的分子进行了基准模拟,包括h6链、h10环和n2。仿真结果表明,我们提出的收敛路径显著提高了传统VQE的性能。总的来说,我们的工作为基于浅量子电路的分子模拟提供了有价值的见解,为提高VQE方法在处理复杂分子系统中的效率和准确性提供了一条有前途的途径。
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引用次数: 2
Optimal, hardware native decomposition of parameterized multi-qubit Pauli gates 参数化多量子位泡利门的最优硬件原生分解
2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Pub Date : 2023-09-25 DOI: 10.1088/2058-9565/acfa20
P V Sriluckshmy, Vicente Pina-Canelles, Mario Ponce, Manuel G Algaba, Fedor Šimkovic IV, Martin Leib
Abstract We show how to efficiently decompose a parameterized multi-qubit Pauli (PMQP) gate into native parameterized two-qubit Pauli (P2QP) gates minimizing both the circuit depth and the number of P2QP gates. Given a realistic quantum computational model, we argue that the technique is optimal in terms of the number of hardware native gates and the overall depth of the decomposition. Starting from PMQP gate decompositions for the path and star hardware graph, we generalize the procedure to any generic hardware graph and provide exact expressions for the depth and number of P2QP gates of the decomposition. Furthermore, we show how to efficiently combine the decomposition of multiple PMQP gates to further reduce the depth as well as the number of P2QP gates for a combinatorial optimization problem using the Lechner–Hauke–Zoller mapping.
我们展示了如何有效地将参数化多量子位泡利门(PMQP)分解为本地参数化双量子位泡利门(P2QP),从而最小化电路深度和P2QP门的数量。给定一个现实的量子计算模型,我们认为该技术在硬件原生门的数量和分解的整体深度方面是最佳的。从路径和星形硬件图的PMQP门分解出发,将此过程推广到任何一般硬件图,并给出了分解的P2QP门的深度和个数的精确表达式。此外,我们展示了如何有效地结合多个PMQP门的分解,以进一步减少深度和P2QP门的数量,用于使用lechner - hake - zoller映射的组合优化问题。
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引用次数: 2
期刊
Quantum Science and Technology
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