TensorFlow solver for quantum PageRank in large-scale networks

Tang, Hao; Shi, Ruoxi; He, Tian-Shen; Zhu, Yanyan; Wang, Tianyu; Lee, Marcus; Jin, Xian-Min

doi:10.1016/j.scib.2020.09.009

Cited by 13 publications

(3 citation statements)

References 37 publications

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“…Our experimental approach is direct, flexible, and holds the potential for scalability. The exploration of quantum algorithms in practical applications is gaining momentum [53][54][55], even though they are currently in a preliminary stage. With the dedicated efforts of scientific researchers, we anticipate that quantum technology will soon be leveraged to tackle challenging real-life problems.…”

Section: Discussionmentioning

confidence: 99%

Experimental implementation of quantum-walk-based portfolio optimization

Qu,

Matwiejew,

Wang

et al. 2024

Quantum Sci. Technol.

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The application of quantum algorithms has attracted much attention as it holds the promise of solving practical problems that are intractable to classical algorithms. One such application is the recent development of a quantum-walk-based optimisation algorithm approach to portfolio optimisation under the modern portfolio theory framework. In this paper, we demonstrate an experimental realisation of the alternating phase-shift and continuous-time quantum walk unitaries that underpin this quantum algorithm using optical networks and single photons. The experimental analysis conﬁrms that the probability of states corresponding to high-quality solutions is eﬃciently ampliﬁed by increasing the number of phase-shift and quantum walk iterations. This work provides strong evidence for practical applications of quantum-walk-based algorithms such as ﬁnancial portfolio optimisation.

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Section: Discussionmentioning

confidence: 99%

Experimental implementation of quantum-walk-based portfolio optimization

Qu,

Matwiejew,

Wang

et al. 2024

Quantum Sci. Technol.

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“…Through interaction with a certain amount of noise, the open (i.e., noisy) QW can produce a remarkable improvement in quantum transport over the noise-free QW ( 5 , 6 ). Also, such a noise-enhanced feature aids in problem-solving efficiency in tasks such as graph isomorphism testing ( 7 ), a maze escape ( 8 , 9 ), and ranking elements ( 10 ) in large networks. While as a prototypical dynamical process, the open QW can model the dissipative evolution of quantum neural networks ( 11 , 12 ), and this simulation enables better performance to process various issues such as pattern recognition ( 13 ).…”

Section: Introductionmentioning

confidence: 99%

Efficient learning of mixed-state tomography for photonic quantum walk

Wang,

Dong,

et al. 2024

Sci. Adv.

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Noise-enhanced applications in open quantum walk (QW) has recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challenging, as mixed-state tomography is a resource-intensive process, and implementing all required measurements is almost impossible due to various physical constraints. To address this challenge, we present a neural-network–based method for reconstructing mixed states with a high fidelity (∼97.5%) while costing only 50% of the number of measurements typically required for open discrete-time QW in one dimension. Our method uses a neural density operator that models the system and environment, followed by a generalized natural gradient descent procedure that significantly speeds up the training process. Moreover, we introduce a compact interferometric measurement device, improving the scalability of our photonic QW setup that enables experimental learning of mixed states. Our results demonstrate that highly expressive neural networks can serve as powerful alternatives to traditional state tomography.

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“…There has been a recent interest in the quantum PageRank. For example, other quantizations have been proposed like coined discrete-time quantum walks [19], continuous-time quantum walks [20][21][22], and has even been realized experimentally in the continuous-time version using photons [23]. It has been also coupled to quantum search as a further step towards a quantum search engine [24].…”

Section: Introductionmentioning

confidence: 99%

Generalized Quantum PageRank Algorithm with Arbitrary Phase Rotations

Ortega¹,

Martín-Delgado²

2022

Preprint

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The quantization of the PageRank algorithm is a promising tool for a future quantum internet.Here we present a modification of the quantum PageRank introducing arbitrary phase rotations (APR) in the underlying Szegedy's quantum walk. We define three different APR schemes with only one phase as a degree of freedom. We have analyzed the behavior of the new algorithms in a small generic graph, observing that a decrease of the phase reduces the standard deviation of the instantaneous PageRank, so the nodes of the network can be distinguished better. However, the algorithm takes more time to converge, so the phase can not be decreased arbitrarily. With these results we choose a concrete value for the phase to later apply the algorithm to complex scale-free graphs. In these networks, the original quantum PageRank is able to break the degeneracy of the residual nodes and detect secondary hubs that the classical algorithm suppresses. Nevertheless, not all of the detected secondary hubs are real according to the PageRank's definition. Some APR schemes can overcome this problem, restoring the degeneration of the residual nodes and highlighting the truly secondary hubs of the networks. Finally, we have studied the stability of the new algorithms. The original quantum algorithm was known to be more stable than the classical. We have found that one of our new algorithms whose PageRank distribution resembles the classical one, has a stability similar to the original quantum algorithm.

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TensorFlow solver for quantum PageRank in large-scale networks

Cited by 13 publications

References 37 publications

Experimental implementation of quantum-walk-based portfolio optimization

Experimental implementation of quantum-walk-based portfolio optimization

Efficient learning of mixed-state tomography for photonic quantum walk

Generalized Quantum PageRank Algorithm with Arbitrary Phase Rotations

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