Network Community Detection on Small Quantum Computers

In recent years a number of quantum computing devices with small numbers of qubits became available. We present a hybrid quantum local search (QLS) approach that combines a classical machine and a small quantum device to solve problems of practical size. The proposed approach is applied to the network community detection problem. QLS is hardware-agnostic and easily extendable to new quantum computing devices as they become available. We demonstrate it to solve the 2-community detection problem on graphs of size up to 410 vertices using the 16-qubit IBM quantum computer and D-Wave 2000Q, and compare their performance with the optimal solutions. Our results demonstrate that QLS perform similarly in terms of quality of the solution and the number of iterations to convergence on both types of quantum computers and it is capable of achieving results comparable to state-of-the-art solvers in terms of quality of the solution including reaching the optimal solutions.

[1]  G. Long,et al.  Experimental realization of a fetching algorithm in a 7-qubit NMR spin Liouville space computer , 2002, quant-ph/0207079.

[2]  John M. Martinis,et al.  Logic gates at the surface code threshold: Superconducting qubits poised for fault-tolerant quantum computing , 2014 .

[3]  Graham R. Fleming,et al.  Bulletin of the American Physical Society , 2015 .

[4]  K. K. Nambiar,et al.  Foundations of Computer Science , 2001, Lecture Notes in Computer Science.

[5]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[6]  J. McClean,et al.  Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz , 2017, Quantum Science and Technology.

[8]  Hallie Preskill The Value of Lived Experience , 2018 .

[9]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[10]  E. Farhi,et al.  A Quantum Approximate Optimization Algorithm , 2014, 1411.4028.

[11]  Mike Paterson,et al.  Proceedings of the 17th International Colloquium on Automata, Languages and Programming , 1990 .

[12]  Aidan Roy,et al.  Discrete optimization using quantum annealing on sparse Ising models , 2014, Front. Phys..

[13]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[14]  Andreas Noack,et al.  Multilevel local search algorithms for modularity clustering , 2011, JEAL.

[15]  Lov K. Grover A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[16]  Arto Salomaa,et al.  ICALP'88: Proceedings of the 15th International Colloquium on Automata, Languages and Programming , 1988 .

[17]  Angelo Bifone,et al.  Community detection in weighted brain connectivity networks beyond the resolution limit , 2016, NeuroImage.

[18]  Telecommunications Board,et al.  Quantum computing , 2019, Mathematics and Computation.

[19]  Alejandro Perdomo-Ortiz,et al.  Strengths and weaknesses of weak-strong cluster problems: A detailed overview of state-of-the-art classical heuristics versus quantum approaches , 2016, 1604.01746.

[20]  A. Harrow,et al.  Quantum Supremacy through the Quantum Approximate Optimization Algorithm , 2016, 1602.07674.

[21]  Elham Kashefi,et al.  Demonstration of Blind Quantum Computing , 2011, Science.

[22]  Catherine C. McGeoch Adiabatic Quantum Computation and Quantum Annealing: Theory and Practice , 2014, Adiabatic Quantum Computation and Quantum Annealing: Theory and Practice.

[23]  Abraham P. Punnen,et al.  A survey of very large-scale neighborhood search techniques , 2002, Discret. Appl. Math..

[24]  D. Deutsch Quantum theory, the Church–Turing principle and the universal quantum computer , 1985, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[25]  Feng Hu,et al.  Factoring larger integers with fewer qubits via quantum annealing with optimized parameters , 2019, Science China Physics, Mechanics & Astronomy.

[26]  Aidan Roy,et al.  Mapping Constrained Optimization Problems to Quantum Annealing with Application to Fault Diagnosis , 2016, Front. ICT.

[27]  Christian F. A. Negre,et al.  Graph Partitioning using Quantum Annealing on the D-Wave System , 2017, ArXiv.

[28]  Helmut G. Katzgraber,et al.  Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods , 2017, Physical review. E.

[29]  Harry Buhrman,et al.  The European Quantum Technologies Roadmap , 2017, 1712.03773.

[30]  R. Barends,et al.  Superconducting quantum circuits at the surface code threshold for fault tolerance , 2014, Nature.

[31]  Andrew W. Cross,et al.  The IBM Q experience and QISKit open-source quantum computing software , 2018 .

[32]  J. K. Lenstra,et al.  Local Search in Combinatorial Optimisation. , 1997 .

[33]  M. W. Johnson,et al.  Phase transitions in a programmable quantum spin glass simulator , 2018, Science.

[34]  Sven Leyffer,et al.  Fast response to infection spread and cyber attacks on large-scale networks , 2012, J. Complex Networks.

[35]  Velimir V. Vesselinov,et al.  Nonnegative/Binary matrix factorization with a D-Wave quantum annealer , 2017, PloS one.

[36]  M. Lukin,et al.  Quantum optimization of maximum independent set using Rydberg atom arrays , 2018, Science.

[37]  Gian Giacomo Guerreschi,et al.  QAOA for Max-Cut requires hundreds of qubits for quantum speed-up , 2018, Scientific Reports.

[38]  Harry Buhrman,et al.  The quantum technologies roadmap: a European community view , 2018, New Journal of Physics.

[39]  Susana Gomez,et al.  Advances in Optimization and Numerical Analysis , 1994 .

[40]  M. Powell A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation , 1994 .

[41]  Angelo Bifone,et al.  Hierarchical organization of functional connectivity in the mouse brain: a complex network approach , 2016, Scientific Reports.

[42]  Ojas Parekh,et al.  Quantum Optimization and Approximation Algorithms. , 2019 .

[43]  Helmut G. Katzgraber,et al.  The pitfalls of planar spin-glass benchmarks: raising the bar for quantum annealers (again) , 2017, 1703.00622.

[44]  J. Ignacio Cirac,et al.  Computational speedups using small quantum devices , 2018, Physical review letters.

[45]  Jacob M. Taylor,et al.  Coherent Manipulation of Coupled Electron Spins in Semiconductor Quantum Dots , 2005, Science.

[46]  Barbara Elissa Savedoff,et al.  Art interpretation : a dissertation submitted to the Graduate School - New Brunswick, Rutgers, The State University of New Jersey in partial fulfillment of the requirements for the degree of Doctor of Pholosophy , 1989 .

[47]  Jérôme Kunegis,et al.  KONECT: the Koblenz network collection , 2013, WWW.

[48]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[49]  Stuart Hadfield,et al.  The Quantum Approximation Optimization Algorithm for MaxCut: A Fermionic View , 2017, 1706.02998.

[50]  A. Saiani,et al.  Bulletin of the American Physical Society , 2002 .

[51]  U. Brandes,et al.  Maximizing Modularity is hard , 2006, physics/0608255.

[52]  G. Long,et al.  Parallel Quantum Computing in a Single Ensemble Quantum Computer , 2003, quant-ph/0307055.

[53]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[54]  Aidan Roy,et al.  Fast clique minor generation in Chimera qubit connectivity graphs , 2015, Quantum Inf. Process..

[55]  Jonathan Mizrahi,et al.  Cloud-Based Trapped-Ion Quantum Computing , 2018 .

[56]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Spectral methods for graph clustering - A survey , 2011, Eur. J. Oper. Res..

[57]  Allan Kuchinsky,et al.  GLay: community structure analysis of biological networks , 2010, Bioinform..

[58]  N. Linke,et al.  High-Fidelity Quantum Logic Gates Using Trapped-Ion Hyperfine Qubits. , 2015, Physical review letters.

[59]  Peter W. Shor,et al.  Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[60]  Peter D Karp,et al.  Metabolic pathways for the whole community , 2014, BMC Genomics.

[61]  Thomas G. Walker,et al.  Quantum information with Rydberg atoms , 2009, 0909.4777.

[62]  David Von Dollen,et al.  Traffic Flow Optimization Using a Quantum Annealer , 2017, Front. ICT.

[63]  William W. Hager,et al.  A multilevel bilinear programming algorithm for the vertex separator problem , 2018, Comput. Optim. Appl..

[64]  Andris Ambainis,et al.  Quantum Speedups for Exponential-Time Dynamic Programming Algorithms , 2018, SODA.

[65]  Jingchun Chen,et al.  Detecting functional modules in the yeast protein-protein interaction network , 2006, Bioinform..

[66]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[67]  M. Sipser,et al.  Quantum Computation by Adiabatic Evolution , 2000, quant-ph/0001106.

[68]  Eldad Haber,et al.  Building an iterative heuristic solver for a quantum annealer , 2015, Computational Optimization and Applications.

[69]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[70]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.

[71]  Ewin Tang,et al.  A quantum-inspired classical algorithm for recommendation systems , 2018, Electron. Colloquium Comput. Complex..

[72]  Gili Rosenberg,et al.  Boosting quantum annealer performance via sample persistence , 2016, Quantum Inf. Process..

[73]  Fred W. Glover,et al.  Effective Variable Fixing and Scoring Strategies for Binary Quadratic Programming , 2011, EvoCOP.

[74]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[75]  W. Marsden I and J , 2012 .

[76]  Daniel A. Lidar,et al.  Exploring More-Coherent Quantum Annealing , 2018, 2018 IEEE International Conference on Rebooting Computing (ICRC).

[77]  Tomoyuki Morimae,et al.  Efficient universal blind quantum computation. , 2013, Physical review letters.

[78]  Lan Zhou,et al.  Blind quantum computation with a noise channel , 2016, Physical Review A.

[79]  Mark W. Johnson,et al.  Observation of topological phenomena in a programmable lattice of 1,800 qubits , 2018, Nature.

[80]  J. Herskowitz,et al.  Proceedings of the National Academy of Sciences, USA , 1996, Current Biology.

[81]  G. Long,et al.  Fetching marked items from an unsorted database in NMR ensemble computing , 2001, quant-ph/0112162.

[82]  A. Folkesson IT and society , 2013 .

[83]  Lan Zhou,et al.  Deterministic entanglement distillation for secure double-server blind quantum computation , 2013, Scientific Reports.

[84]  Yu-Bo Sheng,et al.  Distributed secure quantum machine learning. , 2017, Science bulletin.

[85]  E. Farhi,et al.  A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem , 2014, 1412.6062.

[86]  M. Hastings,et al.  From local to global ground states in Ising spin glasses , 2014, 1408.1901.