Supervised graph classification for chiral quantum walks

Kryukov, A K; Abramov, Roman; Fedichkin, Leonid; Alodjants, A. P.; Melnikov, A. A.

doi:10.1103/physreva.105.022208

Cited by 8 publications

(7 citation statements)

References 56 publications

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“…In a number of works we attacked this problem by means of ML approach [39,40,306]. We explore a supervised learning approach to predict a quantum speedup just by looking at a graph.…”

Section: Machine Learning In Random Walks Problemsmentioning

confidence: 99%

“…In particular, we examined in Ref. [39,40,[306][307][308] continuoustime random walks and suppose that classical random walk representing stochastic (Markovian) process defined on a connected graph. It starts at the time t = 0 from initial node i and hits target vertex t. Unlike the classical case, a quantum particle due to interference phenomenon will be "smeared" across all vertices of the graphs.…”

Section: Machine Learning In Random Walks Problemsmentioning

confidence: 99%

“…Mathematically graph is characterized by it weighted adjacency matrix A that is relevant to Hamiltonian H = A. Notice, for chiral QW time asymmetry may be obtained by using complexvalued adjacency matrix elements [306]. We characterize quantum transport by means of Gorini-Kossakowski-Sudarshan-Lindblad equation that look like (cf.…”

Section: Machine Learning In Random Walks Problemsmentioning

confidence: 99%

See 2 more Smart Citations

Quantum Machine Learning: from physics to software engineering

Melnikov¹,

Kordzanganeh²,

Alodjants³

et al. 2023

Preprint

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Quantum machine learning (QML) is a new, rapidly growing, and fascinating area of research where quantum information science and quantum technologies meet novel machine learning and artificial intelligent facilities. A comprehensive analysis of the main directions of current QML methods and approaches is performed in this review. The aim of our work is twofold. First, we show how classical machine learning approach can help improve the facilities of quantum computers and simulators available today. It is most important due to the modern noisy intermediate-scale quantum (NISQ) era of quantum technologies. In particular, the classical machine learning approach allows optimizing quantum hardware for achieving desired quantum states by implementing quantum devices. Second, we discuss how quantum algorithms and quantum computers may be useful for solving keystone classical machine learning tasks. Currently, quantum-inspired algorithms, which use a quantum approach to classical information processing, represent a powerful tool in software engineering for improving classical computation capacities. In this work, we discuss various quantum neural network capabilities that can be implemented in quantum-classical training algorithms for variational circuits. It is expected that quantum computers will be involved in routine machine learning procedures. In this sense, we are showing how it is essential to elucidate the speedup problem for random walks on arbitrary graphs, which are used in both classical and quantum algorithms. Quantum technologies enhanced by machine learning in fundamental and applied quantum physics, as well as quantum tomography and photonic quantum computing, are also covered.

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“…In a number of works we attacked this problem by means of ML approach [39,40,306]. We explore a supervised learning approach to predict a quantum speedup just by looking at a graph.…”

Section: Machine Learning In Random Walks Problemsmentioning

confidence: 99%

Section: Machine Learning In Random Walks Problemsmentioning

confidence: 99%

Section: Machine Learning In Random Walks Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Quantum Machine Learning: from physics to software engineering

Melnikov¹,

Kordzanganeh²,

Alodjants³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Quantum interference, which is at the heart of quantum walks [19] , [20] , [21] , [22] , [23] , [24] , potentially enables to accelerate energy transfer in Fenna-Matthews-Olson complexes [25] , [26] , [27] and quantum photonic circuits [28] , [29] . Understanding quantum walk advantage in particle transfer requires efficient simulation techniques and graph classification algorithms [30] , [31] , [32] . Simulating quantum walks is computationally a #P-hard problem, which makes classical simulators inefficient for the task [33] , [34] .…”

Section: Introductionmentioning

confidence: 99%

Quantum walk processes in quantum devices

Madhu

Melnikov

Fedichkin

et al. 2023

Heliyon

Self Cite

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“…Examples are found in quantum computation [4][5][6] and quantum algorithms. [7][8][9][10][11][12][13][14][15][16][17] Since a CTQW evolves over a graph, it is strongly related to applications over networks, including quantum spatial search, [18][19][20][21][22][23][24] quantum DOI: 10.1002/qute.202200093 routing, [25][26][27] quantum transport, and state transfer. [28][29][30] The ability to redirect or control information over a graph in an efficient way is essential to develop protocols involving quantum networks and to deal with a large amount of structured data.…”

Section: Introductionmentioning

confidence: 99%

Feedback‐Assisted Quantum Search by Continuous‐Time Quantum Walks

et al. 2022

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The quantum search of a target node on a cycle graph by means of a quantum walk assisted by continuous measurement and feedback are addressed. Unlike previous spatial search approaches, where the oracle is described as a projector on the target state, a dynamical oracle implemented through a feedback Hamiltonian is considered. The idea is based on continuously monitoring the position of the quantum walker on the graph and then applying a unitary feedback operation based on the information obtained from measurement. The feedback changes the couplings between the nodes and it is optimized at each time via a numerical procedure. The stochastic trajectories describing the evolution for graphs of dimensions up to N = 15 are numerically simulated, and the performance of the protocol is quantified via the average fidelity between the state of the walker and the target node. Different constraints on the control strategy are discussed. For unbounded controls, the protocol is able to quickly localize the walker on the target node. How the performance is lowered by posing an upper bound on the control couplings is then discussed. Finally, it is shown how a digital feedback protocol seems in general as efficient as the continuous bounded one.

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Supervised graph classification for chiral quantum walks

Cited by 8 publications

References 56 publications

Quantum Machine Learning: from physics to software engineering

Quantum Machine Learning: from physics to software engineering

Quantum walk processes in quantum devices

Feedback‐Assisted Quantum Search by Continuous‐Time Quantum Walks

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