### Abstract

Comparative analyses of graph-structured datasets underly diverse problems. Examples of these problems include identification of conserved functional components (biochemical interactions) across species, structural similarity of large biomolecules, and recurring patterns of interactions in social networks. A large class of such analyses methods quantify the topological similarity of nodes across networks. The resulting correspondence of nodes across networks, also called node alignment, can be used to identify invariant subgraphs across the input graphs. Given k graphs as input, alignment algorithms use topological information to assign a similarity score to each k-tuple of nodes, with elements (nodes) drawn from each of the input graphs. Nodes are considered similar if their neighbors are also similar. An alternate, equivalent view of these network alignment algorithms is to consider the Kronecker product of the input graphs and to identify high-ranked nodes in the Kronecker product graph. Conventional methods such as PageRank and HITS (Hypertext-Induced Topic Selection) can be used for this purpose. These methods typically require computation of the principal eigenvector of a suitably modified Kronecker product matrix of the input graphs. We adopt this alternate view of the problem to address the problem of multiple network alignment. Using the phase estimation algorithm, we show that the multiple network alignment problem can be efficiently solved on quantum computers. We characterize the accuracy and performance of our method and show that it can deliver exponential speedups over conventional (non-quantum) methods.

Original language | English |
---|---|

Pages (from-to) | 2653-2666 |

Number of pages | 14 |

Journal | Quantum Information Processing |

Volume | 13 |

Issue number | 12 |

DOIs | |

Publication status | Published - 1 Jan 2014 |

### Fingerprint

### Keywords

- Network alignment
- NP Problems
- Phase estimation algorithms
- Quantum algorithms
- Quantum bioinformatics

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials
- Statistical and Nonlinear Physics
- Signal Processing
- Modelling and Simulation
- Theoretical Computer Science

### Cite this

*Quantum Information Processing*,

*13*(12), 2653-2666. https://doi.org/10.1007/s11128-014-0818-7

**Multiple network alignment on quantum computers.** / Daskin, Anmer; Grama, Ananth; Kais, Sabre.

Research output: Contribution to journal › Article

*Quantum Information Processing*, vol. 13, no. 12, pp. 2653-2666. https://doi.org/10.1007/s11128-014-0818-7

}

TY - JOUR

T1 - Multiple network alignment on quantum computers

AU - Daskin, Anmer

AU - Grama, Ananth

AU - Kais, Sabre

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Comparative analyses of graph-structured datasets underly diverse problems. Examples of these problems include identification of conserved functional components (biochemical interactions) across species, structural similarity of large biomolecules, and recurring patterns of interactions in social networks. A large class of such analyses methods quantify the topological similarity of nodes across networks. The resulting correspondence of nodes across networks, also called node alignment, can be used to identify invariant subgraphs across the input graphs. Given k graphs as input, alignment algorithms use topological information to assign a similarity score to each k-tuple of nodes, with elements (nodes) drawn from each of the input graphs. Nodes are considered similar if their neighbors are also similar. An alternate, equivalent view of these network alignment algorithms is to consider the Kronecker product of the input graphs and to identify high-ranked nodes in the Kronecker product graph. Conventional methods such as PageRank and HITS (Hypertext-Induced Topic Selection) can be used for this purpose. These methods typically require computation of the principal eigenvector of a suitably modified Kronecker product matrix of the input graphs. We adopt this alternate view of the problem to address the problem of multiple network alignment. Using the phase estimation algorithm, we show that the multiple network alignment problem can be efficiently solved on quantum computers. We characterize the accuracy and performance of our method and show that it can deliver exponential speedups over conventional (non-quantum) methods.

AB - Comparative analyses of graph-structured datasets underly diverse problems. Examples of these problems include identification of conserved functional components (biochemical interactions) across species, structural similarity of large biomolecules, and recurring patterns of interactions in social networks. A large class of such analyses methods quantify the topological similarity of nodes across networks. The resulting correspondence of nodes across networks, also called node alignment, can be used to identify invariant subgraphs across the input graphs. Given k graphs as input, alignment algorithms use topological information to assign a similarity score to each k-tuple of nodes, with elements (nodes) drawn from each of the input graphs. Nodes are considered similar if their neighbors are also similar. An alternate, equivalent view of these network alignment algorithms is to consider the Kronecker product of the input graphs and to identify high-ranked nodes in the Kronecker product graph. Conventional methods such as PageRank and HITS (Hypertext-Induced Topic Selection) can be used for this purpose. These methods typically require computation of the principal eigenvector of a suitably modified Kronecker product matrix of the input graphs. We adopt this alternate view of the problem to address the problem of multiple network alignment. Using the phase estimation algorithm, we show that the multiple network alignment problem can be efficiently solved on quantum computers. We characterize the accuracy and performance of our method and show that it can deliver exponential speedups over conventional (non-quantum) methods.

KW - Network alignment

KW - NP Problems

KW - Phase estimation algorithms

KW - Quantum algorithms

KW - Quantum bioinformatics

UR - http://www.scopus.com/inward/record.url?scp=84911369979&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84911369979&partnerID=8YFLogxK

U2 - 10.1007/s11128-014-0818-7

DO - 10.1007/s11128-014-0818-7

M3 - Article

VL - 13

SP - 2653

EP - 2666

JO - Quantum Information Processing

JF - Quantum Information Processing

SN - 1570-0755

IS - 12

ER -