Quantum isomer search

Terry, Jason; Akrobotu, Prosper D.; Negre, Christian F. A.; Mniszewski, Susan M.

doi:10.1371/journal.pone.0226787

Cited by 11 publications

(3 citation statements)

References 39 publications

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“…This results in chains of physical qubits representing logical qubits leading to a maximum capacity of 64 fully connected logical qubits/variables [24]. The D-Wave quantum computers possess the ability to sample degenerate ground state solutions and have been utilized in solving several problems such as quantum isomer search [25], graph partitioning [26], community detection [27], binary clustering [28], graph isomorphism [29] and machine learning [30,31]. It has also been used in solving physical problems related to atomistic configuration stability [32], job-shop scheduling [33], airport and air traffic management [34,35].…”

Section: Eðsþ ¼mentioning

confidence: 99%

A QUBO formulation for top-τ eigencentrality nodes

et al. 2022

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The efficient calculation of the centrality or “hierarchy” of nodes in a network has gained great relevance in recent years due to the generation of large amounts of data. The eigenvector centrality (aka eigencentrality) is quickly becoming a good metric for centrality due to both its simplicity and fidelity. In this work we lay the foundations for solving the eigencentrality problem of ranking the importance of the nodes of a network with scores from the eigenvector of the network, using quantum computational paradigms such as quantum annealing and gate-based quantum computing. The problem is reformulated as a quadratic unconstrained binary optimization (QUBO) that can be solved on both quantum architectures. The results focus on correctly identifying a given number of the most important nodes in numerous networks given by the sparse vector solution of our QUBO formulation of the problem of identifying the top-τ highest eigencentrality nodes in a network on both the D-Wave and IBM quantum computers.

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Section: Eðsþ ¼mentioning

confidence: 99%

A QUBO formulation for top-τ eigencentrality nodes

et al. 2022

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“…The Boolean equivalence of the Ising problem is referred to as a QUBO problem, with the following equation This results in chains of physical qubits representing logical qubits leading to a maximum capacity of 64 fully connected logical qubits/variables [24]. The D-Wave quantum computers possess the ability to sample degenerate ground state solutions and have been utilized in solving several problems such as quantum isomer search [25], graph partitioning [26], community detection [27], binary clustering [28], graph isomorphism [29] and classifier [30,31]. It has also been used in solving physical problems related to atomistic configuration stability [32], job-shop scheduling [33], airport and air traffic management [34,35].…”

Section: Introductionmentioning

confidence: 99%

A QUBO formulation for top-$τ$ eigencentrality nodes

Akrobotu,

James,

Negre

et al. 2021

Preprint

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The efficient calculation of the centrality or "hierarchy" of nodes in a network has gained great relevance in recent years due to the generation of large amounts of data. The eigenvector centrality is quickly becoming a good metric for centrality due to both its simplicity and fidelity. In this work we lay the foundations for the calculation of eigenvector centrality using quantum computational paradigms such as quantum annealing and gate-based quantum computing. The problem is reformulated as a quadratic unconstrained binary optimization (QUBO) that can be solved on both quantum architectures. The results focus on correctly identifying a given number of the most important nodes in numerous networks given by our QUBO formulation of eigenvector centrality on both the D-Wave and IBM quantum computers.

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“…Some applicational algorithms include a quantum genetic algorithm [ 7 ], quantum support vector machine [ 8 ], quantum principal component analysis [ 9 ], and quantum reinforcement learning [ 10 ]. Research has also been conducted on applying quantum computing environments to real-world problems such as chemistry [ 11 ] and data science [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

A multi-commodity network model for optimal quantum reversible circuit synthesis

Jung

Choi

2021

PLoS ONE

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Quantum computing is a newly emerging computing environment that has recently attracted intense research interest in improving the output fidelity, fully utilizing its high computing power from both hardware and software perspectives. In particular, several attempts have been made to reduce the errors in quantum computing algorithms through the efficient synthesis of quantum circuits. In this study, we present an application of an optimization model for synthesizing quantum circuits with minimum implementation costs to lower the error rates by forming a simpler circuit. Our model has a unique structure that combines the arc-subset selection problem with a conventional multi-commodity network flow model. The model targets the circuit synthesis with multiple control Toffoli gates to implement Boolean reversible functions that are often used as a key component in many quantum algorithms. Compared to previous studies, the proposed model has a unifying yet straightforward structure for exploiting the operational characteristics of quantum gates. Our computational experiment shows the potential of the proposed model, obtaining quantum circuits with significantly lower quantum costs compared to prior studies. The proposed model is also applicable to various other fields where reversible logic is utilized, such as low-power computing, fault-tolerant designs, and DNA computing. In addition, our model can be applied to network-based problems, such as logistics distribution and time-stage network problems.

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Quantum isomer search

Cited by 11 publications

References 39 publications

A QUBO formulation for top-τ eigencentrality nodes

A QUBO formulation for top-τ eigencentrality nodes

A QUBO formulation for top-$τ$ eigencentrality nodes

A multi-commodity network model for optimal quantum reversible circuit synthesis

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