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Experimental signature of programmable quantum annealing
Open accessAbstract
Quantum annealing is a general strategy for solving difficult optimization problems with the aid of quantum adiabatic evolution. Both analytical and numerical evidence suggests that under idealized, closed system conditions, quantum annealing can outperform classical thermalization-based algorithms such as simulated annealing. Current engineered quantum annealing devices have a decoherence timescale which is orders of magnitude shorter than the adiabatic evolution time. Do they effectively perform classical thermalization when coupled to a decohering thermal environment? Here we present an experimental signature which is consistent with quantum annealing, and at the same time inconsistent with classical thermalization. Our experiment uses groups of eight superconducting flux qubits with programmable spin–spin couplings, embedded on a commercially available chip with >100 functional qubits. This suggests that programmable quantum devices, scalable with current superconducting technology, implement quantum annealing with a surprising robustness against noise and imperfections. Quantum annealing is the quantum computational equivalent of the classical approach to solving optimization problems known as simulated annealing. Boixo et al.report experimental evidence for the realization of quantum annealing processes that are unexpectedly robust against noise and imperfections.
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Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Scheme for reducing decoherence in quantum computer memory
Shor, P. W. (1995) · Physical Review A · doi:10.1103/physreva.52.r2493 · Cited by 4,479
Quantum annealing in the transverse Ising model
Kadowaki, T., & Nishimori, H. (1998) · Physical Review E · doi:10.1103/physreve.58.5355 · Cited by 2,339
A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
Farhi, E., Goldstone, J., Gutmann, S., Lapan, J., Lundgren, A., & Preda, D. (2001) · Science · doi:10.1126/science.1057726 · Cited by 2,056
Quantum annealing with manufactured spins
Johnson, M. W., Amin, M. H. S., Gildert, S., Lanting, T., Hamze, F., Dickson, N., Harris, R., Berkley, A. J., Johansson, J., Bunyk, P., et al. (2011) · Nature · doi:10.1038/nature10012 · Cited by 1,891
On the computational complexity of Ising spin glass models
Barahona, F. (1982) · Journal of Physics A: Mathematical and General · doi:10.1088/0305-4470/15/10/028 · Cited by 1,306
Statistical Physics of Spin Glasses and Information Processing: An Introduction
Nishimori, H. (2001) · doi:10.1093/acprof:oso/9780198509417.001.0001 · Cited by 1,182
Theory of Quantum Annealing of an Ising Spin Glass
Santoro, G. E., Martoňák, R., Tosatti, E., & Car, R. (2002) · Science · doi:10.1126/science.1068774 · Cited by 734
Some Implementations of the Boxplot
Frigge, M., Hoaglin, D. C., & Iglewicz, B. (1989) · The American Statistician · doi:10.1080/00031305.1989.10475612 · Cited by 719
Colloquium : Quantum annealing and analog quantum computation
Das, A., & Chakrabarti, B. K. (2008) · Reviews of Modern Physics · doi:10.1103/revmodphys.80.1061 · Cited by 700
Quantum annealing: A new method for minimizing multidimensional functions
Finnila, A. B., Gomez, M. A., Sebenik, C., Stenson, C., & Doll, J. D. (1994) · Chemical Physics Letters · doi:10.1016/0009-2614(94)00117-0 · Cited by 644
Statistical physics of spin glasses and information processing : an introduction
Nishimori, H. (2001) · Cited by 617
Maintaining coherence in quantum computers
Unruh, W. G. (1995) · Physical Review A · doi:10.1103/physreva.51.992 · Cited by 594
Quantum Computation by Adiabatic Evolution
Farhi, E., Goldstone, J., Gutmann, S., & Sipser, M. (2000) · arXiv · doi:10.48550/arxiv.quant-ph/0001106 · Cited by 536
Quantum Annealing of a Disordered Magnet
Brooke, J., Bitko, D., F., T., Rosenbaum, & Aeppli, G. (1999) · Science · doi:10.1126/science.284.5415.779 · Cited by 518
Realization of quantum error correction
Chiaverini, J., Leibfried, D., Schaetz, T., Barrett, M. D., Blakestad, R. B., Britton, J., Itano, W. M., Jost, J. D., Knill, E., Langer, C., et al. (2004) · Nature · doi:10.1038/nature03074 · Cited by 517
Robustness of adiabatic quantum computation
Childs, A. M., Farhi, E., & Preskill, J. (2001) · Physical Review A · doi:10.1103/physreva.65.012322 · Cited by 484
Decoherence of Flux Qubits due to1/fFlux Noise
Yoshihara, F., Harrabi, K., Niskanen, A. O., Nakamura, Y., & Tsai, J. S. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.167001 · Cited by 393
Mathematical foundation of quantum annealing
Morita, S., & Nishimori, H. (2008) · Journal of Mathematical Physics · doi:10.1063/1.2995837 · Cited by 381
Finding low-energy conformations of lattice protein models by quantum annealing
Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., & Aspuru-Guzik, A. (2012) · Scientific Reports · doi:10.1038/srep00571 · Cited by 366
Quantum adiabatic Markovian master equations
Albash, T., Boixo, S., Lidar, D. A., & Zanardi, P. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/12/123016 · Cited by 281
Self-citationExperimental investigation of an eight-qubit unit cell in a superconducting optimization processor
Harris, R., Johnson, M. W., Lanting, T., Berkley, A. J., Johansson, J., Bunyk, P., Tolkacheva, E., Ladizinsky, E., Ladizinsky, N., Oh, T., et al. (2010) · Physical Review B · doi:10.1103/physrevb.82.024511 · Cited by 273
Adiabatic Quantum Computation in Open Systems
Sarandy, M. S., & Lidar, D. A. (2005) · Physical Review Letters · doi:10.1103/physrevlett.95.250503 · Cited by 219
Self-citationExperimental demonstration of a robust and scalable flux qubit
Harris, R., Johansson, J., Berkley, A. J., Johnson, M. W., Lanting, T., Han, S., Bunyk, P., Ladizinsky, E., Oh, T., Perminov, I., et al. (2010) · Physical Review B · doi:10.1103/physrevb.81.134510 · Cited by 177
A scalable control system for a superconducting adiabatic quantum optimization processor
Johnson, M. W., Bunyk, P., Maibaum, F., Tolkacheva, E., Berkley, A. J., Chapple, E. M., Harris, R., Johansson, J., Lanting, T., Perminov, I., et al. (2010) · Superconductor Science and Technology · doi:10.1088/0953-2048/23/6/065004 · Cited by 162
Tunable quantum tunnelling of magnetic domain walls
Brooke, J., Rosenbaum, T. F., & Aeppli, G. (2001) · Nature · doi:10.1038/35098037 · Cited by 133
Thermally Assisted Adiabatic Quantum Computation
Amin, M. H. S., Love, P. J., & Truncik, C. J. S. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.060503 · Cited by 120
A scalable readout system for a superconducting adiabatic quantum optimization system
Berkley, A. J., Johnson, M. W., Bunyk, P., Harris, R., Johansson, J., Lanting, T. M., Ladizinsky, E., Tolkacheva, E., Amin, M. H. S., & Rose, G. (2010) · Superconductor Science and Technology · doi:10.1088/0953-2048/23/10/105014 · Cited by 112
Adiabatic Dynamics in Open Quantum Critical Many-Body Systems
Patanè, D., Silva, A., Luigi Amico, A., Fazio, R., & Santoro, G. E. (2008) · Physical Review Letters · doi:10.1103/physrevlett.101.175701 · Cited by 109
Experimental Determination of Ramsey Numbers
Bian, Z., Chudak, F. A., Macready, W. G., Clark, L., & Gaitan, F. (2013) · Physical Review Letters · doi:10.1103/physrevlett.111.130505 · Cited by 105
Classical signature of quantum annealing
Smolin, J. A., & Smith, G. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00052 · Cited by 79
Eigenpath traversal by phase randomization
Boixo, S., Knill, E., & Somma, R. D. (2009) · Quantum Information and Computation · doi:10.26421/qic9.9-10-7 · Cited by 73
Self-citationNecessary condition for the quantum adiabatic approximation
Boixo, S., & Somma, R. D. (2010) · Physical Review A · doi:10.1103/physreva.81.032308 · Cited by 59
Self-citationEffects of dissipation on an adiabatic quantum search algorithm
de Vega, I., Bañuls, M. C., & Pérez, A. (2010) · New Journal of Physics · doi:10.1088/1367-2630/12/12/123010 · Cited by 24
Kaiser, C., Meckbach, J. M., Ilin, K. S., Lisenfeld, J., Schäfer, R., Ustinov, A. V., & Siegel, M. (2010) · Superconductor Science and Technology · doi:10.1088/0953-2048/24/3/035005 · Cited by 18
Experimental determination of Ramsey numbers with quantum annealingPreprint
Bian, Z., Chudak, F. A., Macready, W. G., et al. (2012) · arXiv (Cornell University) · Cited by 12
Research works printed but not linked: 1
- Bertoin, J., Martinelli, F., Peres, Y. & Bernard, P. Lectures on Glauber Dynamics for Discrete Spin Models Vol. 1717, 93–191Springer: Berlin, Germany, (2004).
Cited by
Ising formulations of many NP problems
Lucas, A. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00005 · Cited by 2,512
Microwave photonics with superconducting quantum circuits
Gu, X., Kockum, A. F., Miranowicz, A., Liu, Y.-X., & Nori, F. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.10.002 · Cited by 1,307
Evidence for quantum annealing with more than one hundred qubits
Boixo, S., Rønnow, T. F., Isakov, S. V., Wang, Z., Wecker, D., Lidar, D. A., Martinis, J. M., & Troyer, M. (2014) · Nature Physics · doi:10.1038/nphys2900 · Cited by 810
Self-citationContinuous Variable Quantum Information: Gaussian States and Beyond
Adesso, G., Ragy, S., & Lee, A. R. (2014) · Open Systems & Information Dynamics · doi:10.1142/s1230161214400010 · Cited by 787
Defining and detecting quantum speedup
Rønnow, T. F., Wang, Z., Job, J., Boixo, S., Isakov, S. V., Wecker, D., Martinis, J. M., Lidar, D. A., & Troyer, M. (2014) · Science · doi:10.1126/science.1252319 · Cited by 601
Self-citation
Metadata
- Authors
- Boixo, S., Albash, T., Spedalieri, F. M., Chancellor, N., & Lidar, D. A.
- Venue
- Nature Communications
- Published
- 2013-06-28
- Volume
- 4
- Issue
- 1
- Pages
- 2067-2067
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2149800642
- Cited by
- 293
- References
- 36
- FWCI
- 37.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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