检索结果-内蒙古大学图书馆

2nd International Workshop on quantum Classical Cooperative Computing (QCCC)

作者： Hansen, Ethan H. Li, Xinpeng Chen, Daniel T. Kulkarni, Vinooth Chaudhary, Vipin Guan, Qiang Liu, Ji Xu, Shuai Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Kent State Univ Dept Comp Sci Kent OH 44242 USA Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9798400701627

At present, quantum computing is in the noisy intermediate-scale quantum (NISQ) era, marked by small qubit counts and high levels of noise and errors. Building a quantum computer with sufficient size and low error rates remains a challenge. In many promising quantum hardware architectures, the state of the physical qubits is controlled by pulse signals. In this paper, we will explore pulse-level control of quantum gates. Unlike the usual gate-level control, the pulse-level control provides increased flexibility and reduced latency. One direct application of pulse-level control is variational quantum algorithms (VQA). The inherent properties of VQA allow us to disregard the gate-based evolution process and concentrate on the final target loss function. From the perspective of pulse-level control, we can generate a sequence of pulse-based gates to rotate the quantum state directly to the desired destination. In this study, we demonstrate an application of pulse-level VQA in estimating the ground state energy of molecular hydrogen. Our experiment is conducted using Quanlse which specializes in pulse-level control of quantum gates. The experimental results reveal a rapid convergence rate of optimization iterations, and the control pulses for each pulse-based gate is also displayed. These results highlight the considerable potential of pulse-level control techniques in practical applications.

关键词： variational quantum algorithms pulse level control variational quantum eigensolver quantum simulation

来源：评论

学校读者我要写书评

暂无评论

Efficient Encodings of the Travelling Salesperson Problem for variational quantum algorithms 3

Efficient Encodings of the Travelling Salesperson Problem fo...

引用

3rd IEEE International Conference on quantum Software (IEEE QSW)

作者： Schnaus, Manuel Palackal, Lilly Poggel, Benedikt Runge, Xiomara Ehm, Hans Lorenz, Jeanette Miriam Mendl, Christian B. Infineon Technol AG Munich Germany Tech Univ Munich Munich Germany Fraunhofer Inst Cognit Syst IKS Munich Germany

ISBN: (纸本)9798350368482;9798350368475

Routing problems are a common optimization problem in industrial applications, which occur on a large scale in supply chain planning. Due to classical limitations for solving NP-hard problems, quantum computing hopes to improve upon speed or solution quality. Several suggestions have been made for encodings of routing problems to solve them with variational quantum algorithms. However, for an end user it is hard to decide a priori which encoding will give the best solutions according to their needs. In this work, we investigate different encodings for the Travelling Salesperson Problem. We compare their scaling and performance when using the quantum Approximate Optimization Algorithm and the variational quantum Eigensolver and provide a clear guide for users when to choose which encoding. For small instances, we find evidence that the permutation encoding can yield good results since it does not suffer from feasibility issues.

关键词： quantum Optimization Travelling Salesperson Problem Encoding variational quantum algorithms

来源：评论

学校读者我要写书评

暂无评论

QuantME4VQA: Modeling and Executing variational quantum algorithms Using Workflows 13

QuantME4VQA: Modeling and Executing Variational Quantum Algo...

引用

13th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Beisel, Martin Barzen, Johanna Bechtold, Marvin Leymann, Frank Truger, Felix Weder, Benjamin Univ Stuttgart Inst Architecture Applicat Syst Stuttgart Germany

ISBN: (纸本)9789897586507

The execution of quantum algorithms typically requires classical pre- and post-processing, making quantum applications inherently hybrid. Classical resources are of particular importance for so-called variational quantum algorithms, as they leverage classical computational power to overcome the limitations imposed by today's noisy quantum devices. However, the additional complex tasks required by these algorithms, complicate the challenge of integrating quantum circuit executions with classical programs. To overcome this challenge, we leverage the advantages and versatility of workflows, and introduce a workflow modeling extension for orchestrating variational quantum algorithms. The modeling extension comprises new task types, data objects, and a comprehensible graphical notation. Furthermore, we ensure interoperability and portability by providing a method for transforming all new modeling constructs to native modeling constructs, and showcase the practical feasibility of our modeling extension by presenting a system architecture, prototype, and case study.

关键词： quantum Workflows quantum Computing variational quantum algorithms Modeling Extension

来源：评论

学校读者我要写书评

暂无评论

Navigating the Dynamic Noise Landscape of variational quantum algorithms with QISMET 2023

Navigating the Dynamic Noise Landscape of Variational Quantu...

引用

28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Ravi, Gokul Subramanian Smith, Kaitlin Baker, Jonathan M. Kannan, Tejas Earnest, Nathan Javadi-Abhari, Ali Hoffmann, Henry Chong, Frederic T. Univ Chicago Chicago IL 60637 USA IBM Quantum Yorktown Hts NY USA

ISBN: (纸本)9781450399166

In the Noisy Intermediate Scale quantum (NISQ) era, the dynamic nature of quantum systems causes noise sources to constantly vary over time. Transient errors from the dynamic NISQ noise landscape are challenging to comprehend and are especially detrimental to classes of applications that are iterative and/or long-running, and therefore their timely mitigation is important for quantum advantage in real-world applications. The most popular examples of iterative long-running quantum applications are variational quantum algorithms (VQAs). Iteratively, VQA's classical optimizer evaluates circuit candidates on an objective function and picks the best circuits towards achieving the application's target. Noise fluctuation can cause a significant transient impact on the objective function estimation of the VQA iterations' tuning candidates. This can severely affect VQA tuning and, by extension, its accuracy and convergence. This paper proposes QISMET: quantum Iteration Skipping to Mitigate Error Transients, to navigate the dynamic noise landscape of VQAs. QISMET actively avoids instances of high fluctuating noise which are predicted to have a significant transient error impact on specific VQA iterations. To achieve this, QISMET estimates transient error in VQA iterations and designs a controller to keep the VQA tuning faithful to the transient-free scenario. By doing so, QISMET efficiently mitigates a large portion of the transient noise impact on VQAs and is able to improve the fidelity by 1.3x-3x over a traditional VQA baseline, with 1.6-2.4x improvement over alternative approaches, across different applications and machines.

关键词： quantum computing variational quantum algorithms error mitigation transient error superconducting qubits noisy intermediatescale quantum variational quantum eigensolver

来源：评论

学校读者我要写书评

暂无评论

CAFQA: A Classical Simulation Bootstrap for variational quantum algorithms 2023

CAFQA: A Classical Simulation Bootstrap for Variational Quan...

引用

28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Ravi, Gokul Subramanian Gokhale, Pranav Ding, Yi Kirby, William Smith, Kaitlin Baker, Jonathan M. Love, Peter J. Hoffmann, Henry Brown, Kenneth R. Chong, Frederic T. Univ Chicago Chicago IL 60637 USA Super tech Morgan Hill CA USA MIT Cambridge MA USA Tufts Univ Medford MA 02155 USA Duke Univ Durham NC USA

ISBN: (纸本)9781450399159

Classical computing plays a critical role in the advancement of quantum frontiers in the NISQ era. In this spirit, this work uses classical simulation to bootstrap variational quantum algorithms (VQAs). VQAs rely upon the iterative optimization of a parameterized unitary circuit (ansatz) with respect to an objective function. Since quantum machines are noisy and expensive resources, it is imperative to classically choose the VQA ansatz initial parameters to be as close to optimal as possible to improve VQA accuracy and accelerate their convergence on today's devices. This work tackles the problem of finding a good ansatz initialization, by proposing CAFQA, a Clifford Ansatz For quantum Accuracy. The CAFQA ansatz is a hardware-efficient circuit built with only Clifford gates. In this ansatz, the parameters for the tunable gates are chosen by searching efficiently through the Clifford parameter space via classical simulation. The resulting initial states always equal or outperform traditional classical initialization (e.g., Hartree-Fock), and enable high-accuracy VQA estimations. CAFQA is well-suited to classical computation because: a) Clifford-only quantum circuits can be exactly simulated classically in polynomial time, and b) the discrete Clifford space is searched efficiently via Bayesian Optimization. For the variational quantum Eigensolver (VQE) task of molecular ground state energy estimation (up to 18 qubits), CAFQA's Clifford Ansatz achieves a mean accuracy of nearly 99% and recovers as much as 99.99% of the molecular correlation energy that is lost in Hartree-Fock initialization. CAFQA achieves mean accuracy improvements of 6.4x and 56.8x, over the state-of-the-art, on different metrics. The scalability of the approach allows for preliminary ground state energy estimation of the challenging chromium dimer (Cr2) molecule. With CAFQA's high-accuracy initialization, the convergence of VQAs is shown to accelerate by 2.5x, even for small molecules. Furthermore,

关键词： quantum computing variational quantum algorithms chemistry clifford bayesian optimization noisy intermediate-scale quantum variational quantum eigensolver

来源：评论

学校读者我要写书评

暂无评论

Enabling High Performance Debugging for variational quantum algorithms using Compressed Sensing 23

Enabling High Performance Debugging for Variational Quantum ...

引用

50th Annual International Symposium on Computer Architecture (ISCA)

作者： Liu, Kun Hao, Tianyi Tannu, Swamit Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Wisconsin Madison Madison WI USA

ISBN: (纸本)9798400700958

variational quantum algorithms (VQAs) can potentially solve practical problems using contemporary Noisy Intermediate Scale quantum (NISQ) computers. VQAs find near-optimal solutions in the presence of qubit errors by classically optimizing a loss function computed by parameterized quantum circuits. However, developing and testing VQAs is challenging due to the limited availability of quantum hardware, their high error rates, and the significant overhead of classical simulations. Furthermore, VQA researchers must pick the right initialization for circuit parameters, utilize suitable classical optimizer configurations, and deploy appropriate error mitigation methods. Unfortunately, these tasks are done in an adhoc manner today, as there are no software tools to configure and tune the VQA hyperparameters. In this paper, we present OSCAR (cOmpressed Sensing based Cost lAndscape Reconstruction) to help configure: 1) correct initialization, 2) noise mitigation techniques, and 3) classical optimizers to maximize the quality of the solution on NISQ hardware. OSCAR enables efficient debugging and performance tuning by providing users with the loss function landscape without running thousands of quantum circuits as required by the grid search. Using OSCAR, we can accurately reconstruct the complete cost landscape with up to 100X speedup. Furthermore, OSCAR can compute an optimizer function query in an instant by interpolating a computed landscape, thus enabling the trial run of a VQA configuration with considerably reduced overhead.

关键词： quantum Computing variational quantum algorithms Debugging

来源：评论

学校读者我要写书评

暂无评论

Normalized Gradient Descent for variational quantum algorithms 2

Normalized Gradient Descent for Variational Quantum Algorith...

引用

IEEE International Conference on quantum Computing and Engineering (QCE) / quantum Week

作者： Suzuki, Yudai Yano, Hiroshi Raymond, Rudy Yamamoto, Naoki Keio Univ Dept Mech Engn Kohoku Ku Hiyoshi 3-14-1 Yokohama Kanagawa 2238522 Japan Keio Univ Dept Appl Phys & Physicoinformat Kohoku Ku Hiyoshi 3-14-1 Yokohama Kanagawa 2238522 Japan IBM Japan Ltd IBM Quantum Chuo Ku 19-21 Nihonbashi Tokyo 1038510 Japan Keio Univ Quantum Comp Ctr Kohoku Ku Hiyoshi 3-14-1 Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781665416917

variational quantum algorithms (VQAs) are promising methods that leverage noisy quantum computers and classical computing techniques for practical applications. In VQAs, the classical optimizers such as gradient-based optimizers are utilized to adjust the parameters of the quantum circuit so that the objective function is minimized. However, they often suffer from the so-called vanishing gradient or barren plateau issue. On the other hand, the normalized gradient descent (NGD) method, which employs the normalized gradient vector to update the parameters, has been successfully utilized in several optimization problems. Here, we study the performance of the NGD methods in the optimization of VQAs for the first time. Our goal is twofold. The first is to examine the effectiveness of NGD and its variants for overcoming the vanishing gradient problems. The second is to propose a new NGD that can attain the faster convergence than the ordinary NGD. We performed numerical simulations of these gradient-based optimizers in the context of quantum chemistry where VQAs are used to find the ground state of a given Hamiltonian. The results show the effective convergence property of the NGD methods in VQAs, compared to the relevant optimizers without normalization. Moreover, we make use of some normalized gradient vectors at the past iteration steps to propose the novel historical NGD that has a theoretical guarantee to accelerate the convergence speed, which is observed in the numerical experiments as well.

关键词： variational quantum algorithms Optimization Normalized Gradient Descent

来源：评论

学校读者我要写书评

暂无评论

VarSaw: Application-tailored Measurement Error Mitigation for variational quantum algorithms 28

VarSaw: Application-tailored Measurement Error Mitigation fo...

引用

28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Dangwal, Siddharth Ravi, Gokul Subramanian Das, Poulami Smith, Kaitlin N. Baker, Jonathan M. Chong, Frederic T. Univ Chicago Chicago IL 60637 USA Univ Michigan Ann Arbor MI 48109 USA Univ Texas Austin Austin TX USA Infleqtion Austin TX USA

ISBN: (纸本)9798400703942

For potential quantum advantage, variational quantum algorithms (VQAs) need high accuracy beyond the capability of today's NISQ devices, and thus will benefit from error mitigation. In this work we are interested in mitigating measurement errors which occur during qubit measurements after circuit execution and tend to be the most error-prone operations, especially detrimental to VQAs. Prior work, JigSaw, has shown that measuring only small subsets of circuit qubits at a time and collecting results across all such 'subset' circuits can reduce measurement errors. Then, running the entire ('global') original circuit and extracting the qubit-qubit measurement correlations can be used in conjunction with the subsets to construct a high-fidelity output distribution of the original circuit. Unfortunately, the execution cost of JigSaw scales polynomially in the number of qubits in the circuit, and when compounded by the number of circuits and iterations in VQAs, the resulting execution cost quickly turns insurmountable. To combat this, we propose VarSaw, which improves JigSaw in an application-tailored manner, by identifying considerable redundancy in the JigSaw approach for VQAs: spatial redundancy across subsets from different VQA circuits and temporal redundancy across globals from different VQA iterations. VarSaw then eliminates these forms of redundancy by commuting the subset circuits and selectively executing the global circuits, reducing computational cost (in terms of the number of circuits executed) over naive JigSaw for VQA by 25x on average and up to 1000x, for the same VQA accuracy. Further, it can recover, on average, 45% of the infidelity from measurement errors in the noisy VQA baseline. Finally, it improves fidelity by 55%, on average, over JigSaw for a fixed computational budget. VarSaw can be accessed here: https://***/siddharthdangwal/VarSaw

关键词： quantum Computing variational quantum algorithms Error Mitigation Measurement Errors Noisy Intermediate-Scale quantum variational quantum Eigensolver

来源：评论

学校读者我要写书评

暂无评论

EQC: Ensembled quantum Computing for variational quantum algorithms 22

EQC: Ensembled Quantum Computing for Variational Quantum Alg...

引用

49th IEEE/ACM Annual International Symposium on Computer Architecture (ISCA)

作者： Stein, Samuel Wiebe, Nathan Ding, Yufei Bo, Peng Kowalski, Karol Baker, Nathan Ang, James Li, Ang Pacific Northwest Natl Lab Richland WA 99354 USA Univ Calif Santa Barbara Santa Barbara CA USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781450386104

variational quantum algorithm (VQA), which is comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches for harvesting the power of quantum computers in the noisy intermediate scale quantum (NISQ) era. However, the deployment of VQAs on contemporary NISQ devices often faces considerable system and time-dependant noise and prohibitively slow training speeds. On the other hand, the expensive supporting resources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a virtualized way of building up a quantum backend for variational quantum algorithms: rather than relying on a single physical device which tends to introduce ever-changing device-specific noise with less reliable performance as time-since-calibration grows, we propose to constitute a quantum ensemble, which dynamically distributes quantum tasks asynchronously across a set of physical devices, and adjusts the ensemble configuration with respect to machine status. In addition to reduced machine-dependant noise, the ensemble can provide significant speedups for VQA training. With this idea, we build a novel VQA training framework called EQC - a distributed gradient-based processor-performance-aware optimization system - that comprises: (i) a system architecture for asynchronous parallel VQA cooperative training;(ii) an analytical model for assessing the quality of a circuit output concerning its architecture, transpilation, and runtime conditions;(iii) a weighting mechanism to adjust the quantum ensemble's computational contribution according to the systems' current performance. Evaluations comprising 500K times' circuit evaluations across 10 IBMQ NISQ devices using a VQE and a QAOA applications demonstrate that EQC can attain error rates very close to the most performant device of the ensemble, while boosting the training speed by 10.5x on average (up to 86x and at least 5.2x). EQC is available at

关键词： quantum Computing variational quantum algorithms Distributed Computing

来源：评论

学校读者我要写书评

暂无评论

Global optimization in variational quantum algorithms via dynamic tunneling method

引用

NEW JOURNAL OF PHYSICS 2024年第7期26卷 073053-073053页

作者： Park, Seung Baek, Kyunghyun Lee, Seungjin Choi, Mahn-Soo Korea Univ Dept Phys Seoul 02841 South Korea Elect & Telecommun Res Inst Daejeon 34129 South Korea

We present a global optimization routine for the variational quantum algorithms, which utilizes the dynamic tunneling flow. Originally designed to leverage information gathered by a gradient-based optimizer around local minima, we adapt the conventional dynamic tunneling flow to exploit the distance measure of quantum states, resolving issues of extrinsic degeneracy arising from the parametrization of quantum states. Our global optimization algorithm is applied to the variational quantum eigensolver for the transverse-field Ising model to demonstrate the performance of our routine while comparing it with the conventional dynamic tunneling method, which is based on the Euclidean distance measure on the parameter space.

关键词： quantum computation variational quantum algorithms dynamic tunneling method local minima quantum machine learning

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：