Experimental evaluation of an adiabiatic quantum system for combinatorial optimization

This paper describes an experimental study of a novel computing system (algorithm plus platform) that carries out quantum annealing, a type of adiabatic quantum computation, to solve optimization problems. We compare this system to three conventional software solvers, using instances from three NP-hard problem domains. We also describe experiments to learn how performance of the quantum annealing algorithm depends on input.

[1]  Umesh V. Vazirani,et al.  How powerful is adiabatic quantum computation? , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

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

[3]  J. Biamonte,et al.  Realizable Hamiltonians for Universal Adiabatic Quantum Computers , 2007, 0704.1287.

[4]  Hartmut Neven,et al.  NIPS 2009 Demonstration: Binary Classification using Hardware Implementation of Quantum Annealing , 2009 .

[5]  Endre Boros,et al.  Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO) , 2007, J. Heuristics.

[6]  Seth Lloyd,et al.  Adiabatic quantum computation is equivalent to standard quantum computation , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[7]  Vijay Patel,et al.  Quantum superposition of distinct macroscopic states , 2000, Nature.

[8]  Edward Farhi,et al.  Quantum adiabatic algorithms, small gaps, and different paths , 2009, Quantum Inf. Comput..

[9]  M. W. Johnson,et al.  Quantum annealing with manufactured spins , 2011, Nature.

[10]  Daniel A. Lidar,et al.  Experimental signature of programmable quantum annealing , 2012, Nature Communications.

[11]  Edward Farhi,et al.  A Numerical Study of the Performance of a Quantum Adiabatic Evolution Algorithm for Satisfiability , 2000, ArXiv.

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

[13]  M. Amin,et al.  Does adiabatic quantum optimization fail for NP-complete problems? , 2010, Physical review letters.

[14]  Vicky Choi,et al.  Minor-embedding in adiabatic quantum computation: I. The parameter setting problem , 2008, Quantum Inf. Process..

[15]  G. Rose,et al.  Finding low-energy conformations of lattice protein models by quantum annealing , 2012, Scientific Reports.

[16]  E. Farhi,et al.  A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem , 2001, Science.

[17]  Rosenbaum,et al.  Quantum annealing of a disordered magnet , 1999, Science.

[18]  Vicky Choi,et al.  Adiabatic Quantum Algorithms for the NP-Complete Maximum-Weight Independent Set, Exact Cover and 3SAT Problems , 2010, ArXiv.

[19]  Fabián A. Chudak,et al.  Experimental determination of Ramsey numbers with quantum annealing , 2012 .

[20]  R. Car,et al.  Theory of Quantum Annealing of an Ising Spin Glass , 2002, Science.

[21]  Andrew M. Childs,et al.  Robustness of adiabatic quantum computation , 2001, quant-ph/0108048.

[22]  D. McMahon.  Adiabatic Quantum Computation , 2008 .

[23]  V. Fock,et al.  Beweis des Adiabatensatzes , 1928 .

[24]  Sergio Boixo,et al.  Spectral Gap Amplification , 2011, SIAM J. Comput..

[25]  Daniel A. Lidar,et al.  Experimental Signature of Programmable Quantum Annealing (Author's Manuscript) , 2013 .

[26]  Endre Boros,et al.  New algorithms for quadratic unconstrained binary optimization (qubo) with applications in engineering and social sciences , 2008 .

[27]  Jérémie Roland,et al.  Adiabatic quantum optimization fails for random instances of NP-complete problems , 2009, ArXiv.

[28]  M. W. Johnson,et al.  Experimental demonstration of a robust and scalable flux qubit , 2009, 0909.4321.