D-Wave Two

Last updated
D-Wave Two
Also known asVesuvius
Developer D-Wave Systems
ManufacturerD-Wave Systems
Product familyD-Wave
Type Quantum computer
CPU Approximately 512-qubit (varies)
Dimensions10 square metre room
Predecessor D-Wave One
Successor D-Wave 2X
Website www.dwavesys.com/d-wave-two-system

D-Wave Two (project code name Vesuvius) is the second commercially available quantum computer, and the successor to the first commercially available quantum computer, D-Wave One. Both computers were developed by Canadian company D-Wave Systems. [1] The computers are not general purpose, but rather are designed for quantum annealing. Specifically, the computers are designed to use quantum annealing to solve a single type of problem known as quadratic unconstrained binary optimization. [2] As of 2015, it was still debated whether large-scale entanglement takes place in D-Wave Two, and whether current or future generations of D-Wave computers will have any advantage over classical computers. [3] [4] [5] [6] [7] [8] [9]

Contents

Processor

D-Wave Two has a QPU (quantum processing unit) of 512 qubits—an improvement over the D-Wave One series' QPUs of about 128 qubits [10] The number of qubits can vary from chip to chip, due to variations in manufacturing. [11] The increase in qubit count for the D-Wave Two was accomplished by tiling qubit pattern of the D-Wave One. This pattern, named chimera by D-Wave Systems, has a limited connectivity such that a given qubit can only interact with at most six other qubits. [9] As with the D-Wave One, this restricted connectivity greatly limits the optimization problems that can be approached with the hardware. [11]

Quantum computing

In March 2013, several groups of researchers at the Adiabatic Quantum Computing workshop at the Institute of Physics in London produced evidence of quantum entanglement in D-Wave CPUs. [12] In March 2014, researchers from University College London and the University of Southern California corroborated their findings; in their tests, the D-Wave Two exhibited the quantum physics outcome that it should while not showing three different classical physics outcomes. [13] [14]

In May 2013, Catherine McGeoch verified that D-Wave Two finds solutions to a synthetic benchmark set of Ising spin optimization problems.[ citation needed ] Boixo et al. (2014) evidenced that the D-Wave Two performs quantum annealing, [15] but that a simulated annealing on a notebook computer also performs well. [16] Jean Francois Puget of IBM compared computation on the D-Wave Two with IBM's CPLEX software. [17]

A D-Wave Two in the Quantum Artificial Intelligence Lab at the NASA Advanced Supercomputing Division of Ames Research Center is used. NASA, Google, and the Universities Space Research Association (USRA) started the lab in 2013. [18] [19] [20] [21]

In July 2016, computer music researcher Alexis Kirke used a harmony algorithm developed for the D-Wave Two [22] live in a public musical performance for mezzo-soprano and electronics in the UK. [23] [24]

In January 2021, a multi-institutional group of researches from ORNL, Purdue and D-Wave generated accurate results from materials science simulations on the DWave-2000Q processor that can be verified with neutron scattering experiments and other practical techniques. [25]

Related Research Articles

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<span class="mw-page-title-main">Sergio Boixo</span> Spanish physicist

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References

  1. Grossman, Lev (6 February 2014). "The Quantum Quest for a Revolutionary Computer". Time.com . Time Inc. Retrieved 2015-03-20.
  2. Dahl, E. D. (November 2013). "Programming with D-Wave: Map Coloring Problem" (PDF). D-Wave Systems.
  3. Aaronson, Scott (16 January 2014). "What happens when an unstoppable PR force hits an NP-hard problem? The answer's getting clearer". Shtetl-Optimized. Retrieved 1 January 2015.
  4. Aaronson, Scott (6 February 2014). "TIME's cover story on D-Wave: A case study in the conventions of modern journalism". Shtetl-Optimized. Retrieved 1 January 2015.
  5. Aaronson, Scott (6 February 2014). "Umesh Vazirani responds to Geordie Rose". Shtetl-Optimized. Retrieved 1 January 2015.
  6. Rose, Geordie (4 February 2014). "The recent "How Quantum is the D-Wave Machine?" Shin et al. paper". Hack the Multiverse. Retrieved 1 January 2015.
  7. Rønnow, Troels F.; et al. (25 July 2014). "Defining and detecting quantum speedup". Science . 345 (6195): 420–424. arXiv: 1401.2910 . Bibcode:2014Sci...345..420R. doi:10.1126/science.1252319. PMID   25061205. S2CID   5596838.
  8. Katzgraber, Helmut G.; Hamze, Firas; Andrist, Ruben S. (April 2014). "Glassy Chimeras Could Be Blind to Quantum Speedup: Designing Better Benchmarks for Quantum Annealing Machines". Physical Review. 4 (2): 021008. arXiv: 1401.1546 . Bibcode:2014PhRvX...4b1008K. doi:10.1103/PhysRevX.4.021008. S2CID   119273763.
  9. 1 2 Seung Woo Shin; et al. (28 January 2014). "How 'Quantum' is the D-Wave Machine?". arXiv: 1401.7087 [quant-ph].
  10. Smalley, Eric (22 February 2012). "D-Wave Defies World of Critics with 'First Quantum Cloud'". Wired. Retrieved 1 January 2015.
  11. 1 2 King, Andrew D.; McGeoch, Catherine C. (9 October 2014). "Algorithm engineering for a quantum annealing platform". arXiv: 1410.2628 [cs.DS].
  12. Aron, Jacob (8 March 2013). "Controversial quantum computer aces entanglement tests". New Scientist . Reed Business Information. Retrieved 14 May 2013.
  13. Hardy, Quentin (24 March 2014). "Quantum Computing Research May Back Controversial Company". Bits. The New York Times Company.
  14. Albash, Tameem; et al. (2015). "Distinguishing Classical and Quantum Models for the D-Wave Device". Physical Review A. 91 (4): 042314. arXiv: 1403.4228 . Bibcode:2015PhRvA..91d2314A. doi:10.1103/PhysRevA.91.042314. S2CID   111382483.
  15. Boixo, Sergio; et al. (2014). "Quantum annealing with more than one hundred qubits". Nature Physics. 10 (3): 218–224. arXiv: 1304.4595 . Bibcode:2014NatPh..10..218B. doi:10.1038/nphys2900. S2CID   8031023.
  16. Boixo, Sergio; et al. (28 February 2014). "Evidence for quantum annealing with more than one hundred qubits". Nature Physics . 10 (3): 218–224. arXiv: 1304.4595 . Bibcode:2014NatPh..10..218B. doi:10.1038/nphys2900. S2CID   8031023.
  17. Puget, Jean-François (2013-06-12). "D-Wave vs CPLEX Comparison. Part 1: QAP". IBM DeveloperWorks . IBM. Retrieved 1 January 2015.
  18. Choi, Charles (16 May 2013). "Google and NASA Launch Quantum Computing AI Lab". MIT Technology Review .
  19. Hardy, Quentin (16 May 2013). "Google Buys a Quantum Computer". Bits. The New York Times Company. Retrieved 3 June 2013.
  20. "NASA, Google and USRA establish Quantum Computing Research Collaboration; 20% of Computing Time will be Provided to the University Community". USRA.edu. Universities Space Research Association. Retrieved 1 January 2015.
  21. "Launching the Quantum Artificial Intelligence Lab". Google Research Blog. 16 May 2013. Retrieved 1 January 2015.
  22. Kirke, Alexis & Miranda, Eduardo (2017). "Experiments in Sound and Music Quantum Computing". In Miranda, Eduardo (ed.). Guide to Unconventional Computing for Music. Springer. pp. 121–157. doi:10.1007/978-3-319-49881-2_5. hdl:10026.1/11021. ISBN   978-3-319-49880-5.
  23. "Quantum Computers don't make sense, but this one makes music". Wired USA . July 30, 2016.
  24. "Seven ways that AI could be A-OK". The Guardian . August 7, 2016.
  25. Kairys, Paul; King, Andrew D.; Ozfidan, Isil; Boothby, Kelly; Raymond, Jack; Banerjee, Arnab; Humble, Travis S. (2020-12-14). "Simulating the Shastry-Sutherland Ising Model Using Quantum Annealing". PRX Quantum. 1 (2): 020320. arXiv: 2003.01019 . doi: 10.1103/PRXQuantum.1.020320 .

Further reading