Efficient Classical Computation of Quantum Mean Values for Shallow QAOA Circuits

Zhuang, Wei-Feng; Pu, Yong; Xu, Hong-Ze; Chai, Xudan; Gu, Yu; Ma, Yunheng; Qamar, Shahid; Chen, Qian; Peng, Qian; Xiao, Xiao; Hu, Mengjun; Liu, Dong E.

doi:10.48550/arxiv.2112.11151

Cited by 4 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we introduce some settings throughout the paper. For QAOA circuit, we use the package of Qcover to perform our simulations [35], the classical optimization algorithm we choose is Constrained Optimization BY Linear Approximation (COBYLA). The number of qubits of problems we consider is all N = 7.…”

Section: Methodsmentioning

confidence: 99%

Information scrambling and entanglement in quantum approximate optimization algorithm circuits

Chen¹,

Zhuang²,

Guo³

et al. 2023

Preprint

View full text Add to dashboard Cite

Variational quantum algorithms, which consist of optimal parameterized quantum circuits, are promising for demonstrating quantum advantages in the noisy intermediate-scale quantum (NISQ) era. Apart from classical computational resources, different kinds of quantum resources have their contributions in the process of computing, such as information scrambling and entanglement. Characterizing the relation between complexity of specific problems and quantum resources consumed by solving these problems is helpful for us to understand the structure of VQAs in the context of quantum information processing. In this work, we focus on the quantum approximate optimization algorithm (QAOA), which aims to solve combinatorial optimization problems. We study information scrambling and entanglement in QAOA circuits respectively, and discover that for a harder problem, more quantum resource is required for the QAOA circuit to obtain the solution. We note that in the future, our results can be used to benchmark complexity of quantum many-body problems by information scrambling or entanglement accumulation in the computing process.

show abstract

Section: Methodsmentioning

confidence: 99%

Information scrambling and entanglement in quantum approximate optimization algorithm circuits

Chen¹,

Zhuang²,

Guo³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The fundamental idea involves using graph decomposition and graph-to-circuit mapping methods to divide a large-scale QAOA instance into multiple smaller independent instances, which can be processed in parallel using quantum simulators or hardware. [40] For most optimization problems, our algorithm exhibits a linear scaling of the running time with respect to the number of qubits. Most of the widely-used quantum simulators are compatible with Quafu-Qcover.…”

Section: Core Modulementioning

confidence: 99%

“…The optimal parameters are found using the graph decomposition method, where each subgraph corresponds one-to-one with smaller QAOA circuits that can be executed on quantum hardware or simulators. [40] Thirdly, the QAOA circuit is compiled into an executable quantum circuit on the Quafu quantum computers through an efficient compiler. It should be noted that the determination of optimal parameters and circuit compilation can be realized in parallel.…”

Section: Introductionmentioning

confidence: 99%

Quafu-Qcover: Explore combinatorial optimization problems on cloud-based quantum computers

Xu 许,

Zhuang 庄,

Wang 王

et al. 2024

Chinese Phys. B

View full text Add to dashboard Cite

We introduce Quafu-Qcover, an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends. Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the Quantum Approximate Optimization Algorithm (QAOA). It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization (QUBO) model and its corresponding Ising model, which can be subsequently transformed into a weight graph. The core of Qcover relies on a graph decomposition-based classical algorithm, which efficiently derives the optimal parameters for the shallow QAOA circuit. Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers. Compared to a general-purpose compiler, our compiler demonstrates the ability to generate shorter circuit depths, while also exhibiting superior speed performance. Additionally, the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time, utilizing the most recent calibration data from the superconducting quantum devices. This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity. The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time, enabling asynchronous processing. Moreover, it incorporates modules for results preprocessing and visualization, facilitating an intuitive display of solutions for combinatorial optimization problems. We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.

show abstract

“…Similar to the existing variational quantum algorithms, [31][32][33] the classical optimizer is employed to determine the optimal parameters of quantum circuits. [34] The quantum circuits, when configured with optimal parameters, will function as SG devices for the purposes of spin measurement, specifically in the context of qubits. The variational shallow quantum circuit consists solely of nearest-neighbor two-qubit gates, which makes our SG quantum circuits renders them highly compatible with NISQ hardware implementation.…”

Section: Introductionmentioning

confidence: 99%

Proposal for sequential Stern–Gerlach experiment with programmable quantum processors

Hu 胡,

Miao 缪,

Zhang 张

2024

Chinese Phys. B

View full text Add to dashboard Cite

The historical significance of the Stern-Gerlach experiment lies in its provision of the initial evidence for space quantization. Over time, its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory. To date, the practical implementation of the sequential Stern-Gerlach experiment has not been fully achieved. In this study, we demonstrate the capability of programmable quantum processors to simulate the sequential Stern-Gerlach experiment. The specific parametric shallow quantum circuits, which are suitable for the limitations of current noisy quantum hardware, are given to replicate the functionality of Stern-Gerlach devices with the ability to perform measurements in different directions. Surprisingly, it has been demonstrated that Wigner's Stern-Gerlach interferometer can be readily implemented in our sequential quantum circuit. With the utilization of the identical circuits, it is also feasible to implement Wheeler's delayed-choice experiment. We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments, and the simulation results demonstrate a strong alignment with theoretical predictions. With the rapid advancement of cloud-based quantum computing, such as BAQIS Quafu, it is our belief that the proposed solution is well-suited for deployment on the cloud, allowing for public accessibility. Our findings not only expand the potential applications of quantum computers, but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.

show abstract

Efficient Classical Computation of Quantum Mean Values for Shallow QAOA Circuits

Cited by 4 publications

References 30 publications

Information scrambling and entanglement in quantum approximate optimization algorithm circuits

Information scrambling and entanglement in quantum approximate optimization algorithm circuits

Quafu-Qcover: Explore combinatorial optimization problems on cloud-based quantum computers

Proposal for sequential Stern–Gerlach experiment with programmable quantum processors

Contact Info

Product

Resources

About