检索结果-内蒙古大学图书馆

Emergence of noise-induced barren plateaus in arbitrary layered noise models

quantum SCIENCE AND TECHNOLOGY 2024年第4期9卷 045019-045019页

作者： Schumann, M. Wilhelm, F. K. Ciani, A. Forschungszentrum Julich Inst Quantum Comp Analyt PGI 12 D-52452 Julich Germany Saarland Univ Theoret Phys Saarbrucken Germany

In variational quantum algorithms the parameters of a parameterized quantum circuit are optimized in order to minimize a cost function that encodes the solution of the problem. The barren plateau phenomenon manifests as an exponentially vanishing dependence of the cost function with respect to the variational parameters, and thus hampers the optimization process. We discuss how, and in which sense, the phenomenon of noise-induced barren plateaus emerges in parameterized quantum circuits with a layered noise model. Previous results have shown the existence of noise-induced barren plateaus in the presence of local Pauli noise (Wang et al 2021 Nat. Commun. 12 6961). We extend these results analytically to arbitrary completely-positive trace preserving maps in two cases: (1) when a parameter-shift rule holds, (2) when the parameterized quantum circuit at each layer forms a unitary 2-design. The second example shows how highly expressive unitaries give rise not only to standard barren plateaus (McClean et al 2018 Nat. Commun. 9 4812), but also to noise-induced ones. In the second part of the paper, we study numerically the emergence of noise-induced barren plateaus in QAOA circuits focusing on the case of MaxCut problems on d-regular graphs and amplitude damping noise.

关键词： quantum computing quantum algorithms variational quantum algorithms barren plateaus noise-induced barren plateaus

来源：评论

学校读者我要写书评

暂无评论

Resource frugal optimizer for quantum machine learning

引用

quantum SCIENCE AND TECHNOLOGY 2023年第4期8卷 045019-045019页

作者： Moussa, Charles Hunter Gordon, Max Baczyk, Michal Cerezo, M. Cincio, Lukasz Coles, Patrick J. Los Alamos Natl Lab Theoret Div Los Alamos NM 87545 USA Leiden Univ LIACS Niels Bohrweg 1 NL-2333 CA Leiden Netherlands Univ Autonoma Madrid Inst Fis Teor UAM CSIC Madrid 28049 Spain Univ Warsaw Fac Phys Ulica Pasteura 5 PL-02093 Warsaw Poland Los Alamos Natl Lab Informat Sci Los Alamos NM 87545 USA Normal Comp Corp New York NY USA

quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. However, training these algorithms can be challenging and calls for tailored optimization procedures. Specifically, QML applications can require a large shot-count overhead due to the large datasets involved. In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function. We consider a highly general loss function that encompasses many QML applications, and we show how to construct an unbiased estimator of its gradient. This allows us to propose a shot-frugal gradient descent optimizer called Refoqus (REsource Frugal Optimizer for quantum Stochastic gradient descent). Our numerics indicate that Refoqus can save several orders of magnitude in shot cost, even relative to optimizers that sample over measurement operators alone.

关键词： quantum machine learning variational quantum algorithms resource frugal shot frugal

来源：评论

学校读者我要写书评

暂无评论

Recompilation-enhanced simulation of electron-phonon dynamics on IBM quantum computers

引用

NEW JOURNAL OF PHYSICS 2022年第9期24卷 093017页

作者： Jaderberg, Benjamin Eisfeld, Alexander Jaksch, Dieter Mostame, Sarah Univ Oxford Clarendon Lab Parks Rd Oxford OX1 3PU England Max Planck Inst Phys Komplexer Syst Nothnitztr Str 38 D-01187 Dresden Germany Univ Hamburg Inst Laserphys D-22761 Hamburg Germany IBM TJ Watson Res Ctr IBM Quantum Yorktown Hts NY 10598 USA

Simulating quantum systems is believed to be one of the first applications for which quantum computers may demonstrate a useful advantage. For many problems in physics, we are interested in studying the evolution of the electron-phonon Hamiltonian, for which efficient digital quantum computing schemes exist. Yet to date, no accurate simulation of this system has been produced on real quantum hardware. In this work, we consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems as dictated by the number of Trotter steps and bosonic energy levels necessary for the convergence of dynamics. We then apply these findings to perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling. Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation. Our results represent a significant step in utilising near term quantum computers for simulation of quantum dynamics and highlight the novelty of approximate circuit recompilation as a tool for reducing noise.

关键词： quantum computing quantum algorithms variational quantum algorithms quantum circuit recompilation quantum simulation electron-phonon

来源：评论

学校读者我要写书评

暂无评论

Mitigating barren plateaus with transfer-learning-inspired parameter initializations

引用

NEW JOURNAL OF PHYSICS 2023年第1期25卷 013039页

作者： Liu, Huan-Yu Sun, Tai-Ping Wu, Yu-Chun Han, Yong-Jian Guo, Guo-Ping Univ Sci & Technol China Chinese Acad Sci Sch Phys Key Lab Quantum Informat Hefei 230026 Anhui Peoples R China Univ Sci & Technol China CAS Ctr Excellence Quantum Informat & Quantum Phys Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Hefei Natl Lab Hefei 230088 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Anhui Peoples R China Origin Quantum Comp Hefei 230026 Anhui Peoples R China

variational quantum algorithms (VQAs) are widely applied in the noisy intermediate-scale quantum era and are expected to demonstrate quantum advantage. However, training VQAs faces difficulties, one of which is the so-called barren plateaus (BPs) phenomenon, where gradients of cost functions vanish exponentially with the number of qubits. In this paper, inspired by transfer learning, where knowledge of pre-solved tasks could be further used in a different but related work with training efficiency improved, we report a parameter initialization method to mitigate BP. In the method, a small-sized task is solved with a VQA. Then the ansatz and its optimum parameters are transferred to tasks with larger sizes. Numerical simulations show that this method could mitigate BP and improve training efficiency. A brief discussion on how this method can work well is also provided. This work provides a reference for mitigating BP, and therefore, VQAs could be applied to more practical problems.

关键词： quantum computation variational quantum algorithms barren plateaus transfer learning

来源：评论

学校读者我要写书评

暂无评论

quantum Architecture Search with Meta-Learning

引用

ADVANCED quantum TECHNOLOGIES 2022年第8期5卷

作者： He, Zhimin Chen, Chuangtao Li, Lvzhou Zheng, Shenggen Situ, Haozhen Foshan Univ Sch Elect & Informat Engn Foshan 528000 Peoples R China Foshan Univ Sch Mechatron Engn & Automat Foshan 528000 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Inst Quantum Comp & Comp Theory Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R 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, a meta quantum architecture search (MetaQAS) algorithm is proposed, 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 fewer gradient updates, which leads to fast learning on new tasks. The proposed MetaQAS can be used with arbitrary gradient-based QAS algorithms. Simulation results on variational quantum compiling (VQC) and quantum approximate optimization algorithm (QAOA) show that the architectures optimized by MetaQAS converge 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.

关键词： meta-learning quantum architecture search variational quantum algorithms

来源：评论

学校读者我要写书评

暂无评论

Minimum hardware requirements for hybrid quantum-classical DMFT

引用

quantum SCIENCE AND TECHNOLOGY 2020年第3期5卷

作者： Jaderberg, B. Agarwal, A. Leonhardt, K. Kiffner, M. Jaksch, D. Univ Oxford Clarendon Lab Parks Rd Oxford OX1 3PU England Natl Univ Singapore Ctr Quantum Technol 3 Sci Dr 2 Singapore 117543 Singapore

We numerically emulate noisy intermediate-scale quantum (NISQ) devices and determine the minimal hardware requirements for two-site hybrid quantum-classical dynamical mean-field theory (DMFT). We develop a circuit recompilation algorithm which significantly reduces the number of quantum gates of the DMFT algorithm and find that the quantum-classical algorithm converges if the two-qubit gate fidelities are larger than 99%. The converged results agree with the exact solution within 10%, and perfect agreement within noise-induced error margins can be obtained for two-qubit gate fidelities exceeding 99.9%. By comparison, the quantum-classical algorithm without circuit recompilation requires a two-qubit gate fidelity of at least 99.999% to achieve perfect agreement with the exact solution. We thus find quantum-classical DMFT calculations can be run on the next generation of NISQ devices if combined with the recompilation techniques developed in this work.

关键词： hybrid quantum-classical algorithms variational quantum algorithms two-site DMFT dynamical mean-field theory quantum computing quantum chemistry NISQ

来源：评论

学校读者我要写书评

暂无评论

Simulation of a Three-Nucleons System Transition on quantum Circuits

引用

ADVANCED quantum TECHNOLOGIES 2024年第0期

作者： Nigro, Luca Barbieri, Carlo Prati, Enrico Univ Milan Dipartimento Fis Aldo Pontremoli via Celoria 16 I-20133 Milan Italy

quantum computers have proven to be effective in simulating many quantum systems. Simulating nuclear processes and state preparation poses significant challenges, even for traditional supercomputers. This study demonstrates the feasibility of a complete simulation of a nuclear transition, including the preparation of both ground and first excited states. To tackle the complexity of strong interactions between two and three nucleons, the states are modeled on the tritium nucleus. Both the initial and final states are represented using quantum circuits with variational quantum algorithms and inductive biases. Describing the spin-isospin states requires four qubits, and a parameterized quantum circuit that exploits a total of 16 parameters is initialized. The estimated energy has a relative error of approximate to$\approx$2% for the ground state and approximate to$\approx$10% for the first excited state of the system. The simulation estimates the transition probability between the two states as a function of the dipole polarization angle. This work marks a first step toward leveraging digital quantum computers to simulate nuclear physics.

关键词： nuclear physics quantum simulation variational quantum algorithms

来源：评论

学校读者我要写书评

暂无评论

Building spatial symmetries into parameterized quantum circuits for faster training

引用

quantum SCIENCE AND TECHNOLOGY 2024年第1期9卷

作者： Sauvage, Frederic Larocca, Martin Coles, Patrick J. Cerezo, M. Los Alamos Natl Lab Theoret Div Los Alamos NM 87545 USA Los Alamos Natl Lab Ctr Nonlinear Studies Los Alamos NM 87545 USA Quantum Sci Ctr Oak Ridge TN 37931 USA Los Alamos Natl Lab Informat Sci Los Alamos NM 87545 USA

Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates employed and by the correlations of their parameters. While much research has been devoted to devising adequate gate-sets, typically respecting some symmetries of the problem, very little is known about how their parameters should be structured. In this work, we show that an ideal parameter structure naturally emerges when carefully considering spatial symmetries (i.e. the symmetries that are permutations of parts of the system under study). Namely, we consider the automorphism group of the problem Hamiltonian, leading us to develop a circuit construction that is equivariant under this symmetry group. The benefits of our novel circuitstructure, called ORB, are numerically probed in several ground-state problems. We find a consistent improvement (in terms of circuit depth, number of parameters required, and gradient magnitudes) compared to literature circuit constructions.

关键词： variational quantum algorithms quantum machine learning symmetries

来源：评论

学校读者我要写书评

暂无评论

On the universality of the quantum approximate optimization algorithm

引用

quantum INFORMATION PROCESSING 2020年第9期19卷 291-291页

作者： Morales, M. E. S. Biamonte, J. D. Zimboras, Z. Skolkovo Inst Sci & Technol Deep Quantum Lab 3 Nobel St Moscow 121205 Russia Hungarian Acad Sci Wigner Res Ctr Phys Budapest Hungary MTA BME Lendulet Quantum Informat Theory Res Grp Budapest Hungary Budapest Univ Technol & Econ Math Inst Budapest Hungary

The quantum approximate optimization algorithm (QAOA) is considered to be one of the most promising approaches towards using near-term quantum computers for practical application. In its original form, the algorithm applies two different Hamiltonians, called the mixer and the cost Hamiltonian, in alternation with the goal being to approach the ground state of the cost Hamiltonian. Recently, it has been suggested that one might use such a set-up as a parametric quantum circuit with possibly some other goal than reaching ground states. From this perspective, a recent work (Lloyd, arXiv:1812.11075) argued that for one-dimensional local cost Hamiltonians, composed of nearest neighbour ZZ terms, this set-up is quantum computationally universal and provides a universal gate set, i.e. all unitaries can be reached up to arbitrary precision. In the present paper, we complement this work by giving a complete proof and the precise conditions under which such a one-dimensional QAOA might produce a universal gate set. We further generalize this type of gate-set universality for certain cost Hamiltonians with ZZ and ZZZ terms arranged according to the adjacency structure of certain graphs and hypergraphs.

关键词： quantum computation variational quantum algorithms Universal quantum gate sets quantum control

来源：评论

学校读者我要写书评

暂无评论

Noise Robustness and Experimental Demonstration of a quantum Generative Adversarial Network for Continuous Distributions

引用

ADVANCED quantum TECHNOLOGIES 2021年第5期4卷

作者： Anand, Abhinav Romero, Jonathan Degroote, Matthias Aspuru-Guzik, Alan Univ Toronto Dept Chem Chem Phys Theory Grp 80 StGeorge St Toronto ON M5S 3H6 Canada Zapata Comp Inc 100 Fed St20th Floor Boston MA 02110 USA Univ Toronto Dept Comp Sci 40 St George St Toronto ON M5S 2E4 Canada Vector Inst CIFAR AI Chair 661 Univ Ave Suite 710 Toronto ON M5G 1M1 Canada Canadian Inst Adv Res CIFAR 661 Univ Ave Toronto ON M5G 1M1 Canada

The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical, and experimental explorations will most likely be required to understand its power. There have been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modeling. In this paper, a hybrid architecture for quantum generative adversarial networks (QGANs) is employed and their robustness in the presence of noise is studied. A simple way of adding different types of noise to the quantum generator circuit is devised, and the noisy hybrid QGANs (HQGANs) are simulated numerically to learn continuous probability distributions, and to show that the performance of HQGANs remains unaffected. The effect of different parameters on the training time is also investigated to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. The training on Rigetti's Aspen-4-2Q-A quantum processing unit is then performed, and the results from the training are presented. The authors' results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate-scale quantum devices.

关键词： quantum computing quantum machine learning variational quantum algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：