Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Gong, Mali; Huang, He-Liang; Wang, Shiyu; Guo, Chao; Li, Shaowei; Wu, Yonggang; Zhu, Qing–Ling; Zhao, Youwei; Guo, Shaojun; Qian, Hao; Yangsen, Ye,; Zha, Chen; Chen, Fusheng; Chen, Ying; Yu, Jiale; Fan, Daojin; Wu, Donghui; Hong, Su Young; Deng, Hui; Rong, Hao; Zhang, Kaili; Cao, Shuchao; Lin, Jenshan; Xu, Yu; Sun, Lin; Guo, Cheng; Li, Na; Liang, Futian; Sakurai, Aizoh; Nemoto, Kae; Munro, W. J.; Huo, Yongheng; Lu, Chao‐Yang; Peng, Cheng-Zhi; Zhu, Xiulin; Pan, Jian-Wei

doi:10.48550/arxiv.2201.05957

Cited by 9 publications

(14 citation statements)

References 17 publications

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“…In Ref. [60], a quantum neuronal sensing model has been proposed to classify ergodic and localized phases of matter with a 61qubit superconducting quantum processor. Moreover, in Ref.…”

Section: Introductionmentioning

confidence: 99%

Quantum Neural Network Classifiers: A Tutorial

Deng

2022

SciPost Phys. Lect. Notes

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Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.

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“…In Ref. [60], a quantum neuronal sensing model has been proposed to classify ergodic and localized phases of matter with a 61qubit superconducting quantum processor. Moreover, in Ref.…”

Section: Introductionmentioning

confidence: 99%

Quantum Neural Network Classifiers: A Tutorial

Deng

2022

SciPost Phys. Lect. Notes

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“…In such algorithms, the complexity of the system is divided between a quantum simulator and a classical optimizer, allowing an imperfect shallow NISQ circuit to eventually achieve quantum advantage over classical computers. The quantum-classical variational algorithms have been found useful for several applications in various fields, including computational chemistry [9][10][11][12][13][14], simulating strongly correlated systems [15][16][17][18] and their phase detection [19], optimization [20][21][22][23][24], solving linear [25][26][27] and nonlinear [28] equations, classification problems [29,30], generative models [31][32][33] and quantum neural networks [34,35]. Among these algorithms, the Variational Quantum Eigensolver (VQE) [10,36,37], as a special type of VQAs, has been developed for efficiently generating the ground state of many-body systems on quantum simulators.…”

Section: Introductionmentioning

confidence: 99%

Robust resource-efficient quantum variational ansatz through evolutionary algorithm

Huang,

Li,

Hou

et al. 2022

Preprint

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Variational quantum algorithms (VQAs) are promising methods to demonstrate quantum advantage on near-term devices as the required resources are divided between a quantum simulator and a classical optimizer. As such, designing a VQA which is resource-efficient and robust against noise is a key factor to achieve potential advantage with the existing noisy quantum simulators. It turns out that a fixed VQA circuit design, such as the widely-used hardware efficient ansatz, is not necessarily robust against imperfections. In this work, we propose a genome-length-adjustable evolutionary algorithm to design a robust VQA circuit that is optimized over variations of both circuit ansatz and gate parameters, without any prior assumptions on circuit structure or depth. Remarkably, our method not only generates a noise-effect-minimized circuit with shallow depth, but also accelerates the classical optimization by substantially reducing the number of parameters. In this regard, the optimized circuit is far more resource-efficient with respect to both quantum and classical resources. As applications, based on two typical error models in VQA, we apply our method to calculating the ground energy of the hydrogen molecule and the Heisenberg model. Simulations suggest that compared with conventional hardware efficient ansatz, our circuit-structure-tunable method can generate circuits apparently more robust against both coherent and incoherent noise, and hence more likely to be implemented on near-term devices.

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“…On the other hand, quantum computing [10] aims to revolutionize computation by harnessing uniquely quantum phenomena to surpass the capabilities of classical computers, with remarkable recent breakthroughs [11][12][13], including digital-analog quantum neural networks [14]. The fusion of quantum computing and neuromorphic computing is known as neuromorphic quantum computation (NQC) [15], which aims to implement brain-inspired devices with quantum hardware and software, and may lead to new groundbreaking technologies.…”

Section: Introductionmentioning

confidence: 99%

Tripartite entanglement in quantum memristors

Kumar¹,

Cárdenas-López²,

Hegade³

et al. 2022

Preprint

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We study the entanglement and memristive properties of three coupled quantum memristors. We consider quantum memristors based on superconducting asymmetric SQUID architectures which are coupled via inductors. The three quantum memristors are arranged in two different geometries: linear and triangular coupling configurations. We obtain a variety of correlation measures, including bipartite entanglement and tripartite negativity. We find that, for identical quantum memristors, entanglement and memristivity follow the same behavior for the triangular case and the opposite one in the linear case. Finally, we study the multipartite correlations with the tripartite negativity and entanglement monogamy relations, showing that our system has genuine tripartite entanglement. Our results show that quantum correlations in multipartite memristive systems have a non-trivial role and can be used to design quantum memristor arrays for quantum neural networks and neuromorphic quantum computing architectures.

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Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Cited by 9 publications

References 17 publications

Quantum Neural Network Classifiers: A Tutorial

Quantum Neural Network Classifiers: A Tutorial

Robust resource-efficient quantum variational ansatz through evolutionary algorithm

Tripartite entanglement in quantum memristors

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