on a small trappedion quantum computer Norbert M Linke Joint Quantum Institute University of Maryland College Park MD USA 4 Feb 2019 UT Quantum Workshop College Park Maryland USA Overview ID: 1026450
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1. Quantum-classical hybrid algorithms on a small trapped-ion quantum computerNorbert M. LinkeJoint Quantum Institute, University of Maryland, College Park, MD USA4 Feb 2019, UT Quantum WorkshopCollege Park, Maryland, USA
2. OverviewQuantum computer module prototype (5-7 qubits) modular gates and compiler Quantum algorithms Benchmark comparisons Deuteron nucleus Quantum Machine Learning Outlook: challenges and scaling upQuantum computing hardware why ions make good qubits
3. Trapped ions A good quantum computing candidate – why?Isolated quantum system, preparation and read-out with laser lightgate operations (using lasers/microwaves)1〉|0〉|1〉|0〉|laserdetector++
4. The ion trap quantum computer (vision)Ion trap Quantum computing – the big picquantum register“accumulator” segmented electrodes D. J. Wineland et al. 1998C. Monroe / J. Kim et al. 2013Are we there yet…? – challengesHigher fidelity operationsScalability: control over more qubits
5. trapped ion Coulomb crystalsIon traps: hardware in current UMD module
6. S. Olmschenk, et al., PRA 76 (2007)Trapped ion qubits: 171Yb+ level structureatomic clock qubit -> B-field insensitivelong coherence times: ~1scompare: “true” clock qubit in 43Ca+ at 146Gcoherence time ~1minT.P. Harty, et al., PRL 113 (2015)
7. Modular architectureS. Debnath et al. Nature 536 (2016)Grover, Hidden Shift, EC …
8. Hardware2S1/22P3/2D=33 THz|0|1355 nm2P1/2171Yb+D=66 THz
9. Hardware: Read-out
10. Modular architectureS. Debnath et al. Nature 536 (2016)Grover, Hidden Shift, EC …
11. Quantum control: Single qubit rotationsR-gateRaman beat note
12. Beatnote frequencytransition probabilitycarrierredsidebandbluesidebandQuantum control: Exciting the motion…mode1mode21515K. Mølmer and A. Sørensen, Phys. Rev. Lett. 82 (1999)S.-L. Zhu et. al., Phys. Rev. Lett. 97 (2006)T. Choi et al., Phys. Rev. Lett. 112 (2014)
13. Quantum control: Full connectivitynot limited to local operationsNML et al. PNAS 114, 13 (2017)
14. Modular architectureS. Debnath et al. Nature 536 (2016)Grover, Hidden Shift, EC …
15. C-SWAPQuantum compiler: Fredkin gate
16. NML et al., arxiv 1712.08581 (2017) Quantum compiler: Fredkin gate circuit
17. Quantum compiler: Fredkin gate resultsFredkin [1,2:4], F=86.8(3)%(corrected for 2% spam error)
18. Modular architectureGrover, Hidden Shift, EC …S. Debnath et al. Nature 536 (2016)
19. Quantum algorithms: build it …and they will come!1 S. Debnath et al. Nature 536 (2016) 2 NML et al., PNAS 114, 13 (2017)3 NML et al., Sci Adv. 3, 10 (2017) 4 C. Figgatt et al., Nat. Communs. 8, 1918 (2017)5 N. Solmeyer et al., QST 3 045002 (2018) 6 NML et al., Phys. Rev. A 98, 052334 (2018)7 K. A. Landsman et al., arxiv 1806.02807 8 D. Zhu et al., arXiv 1812.08862 (2018)9 A. Seif et al., J. Phys. B 51 174006 (2018) 10 in preparationFault-tolerant quantum error detection3 – K. Brown (Georgia Tech.)Renyi entropy measurement of a Fermi-Hubbard model system6 – S. Johri (Intel)Quantum game theory and Nash equilibria5 – N. Solmeyer (Army Research Lab)Quantum machine learning8 – A. Ortiz (NASA)Quantum scrambling and out-of-time-order correlators7 – N. Yao (UC Berkeley)Hidden Shift algorithm2 – M. Roetteler (Microsoft)Quantum Fourier Transform, Bernstein-Vazirani algorithm, Deutsch-Josza algorithm1Grover’s algorithm4 – D. Maslov (NSF)…Bacon-Shor quantum error correction codes10 – T. Yoder (Harvard)Deuteron VQE10 – R. Pooser (Oak Ridge)Quantum Approximate Optimization (QAOA) of critical states10 – T. Hsieh (Perimeter)Neural-network-based qubit readout9 – A. Seif (QuiCS/UMD)
20. Example algorithms on multiple platforms (Princeton)P. Murali and M. Martonosi (Princeton), A. J. Abhari (IBM), NML (UMD) et al. ISCA-2019 #1300
21. Example algorithms on multiple platforms (Princeton)P. Murali and M. Martonosi (Princeton), A. J. Abhari (IBM), NML (UMD) et al. ISCA-2019 #1300
22. Quantum-classical hybrid computing
23. Ground state of the Deuteron nucleusH3 = 15.531709 I + 0.218291 Z0 − 6.125 Z1 − 9.625 Z2 − 2.143304 X0 X1 − 2.143304 Y0 Y1 − 3.913119 X1 X2 − 3.913119 Y1 Y23-qubit Hamiltonian (EFT), -2.046MeV:nuclear binding energy (NIST table): -2.2MeV
24. Dumitrescu, E. F., et al. PRL 120 (2018)Ground state of the Deuteron nucleusCanonical 3-qubit UCC ansatz
25. Ground state of the Deuteron nucleusRichardson, L. F. Phil. Trans. Roy. Soc. A 210 (1911)Temme, K. et al. PRL 119 (2017)Li, Y. PRX 7, 2 (2017)Zero-noise extrapolation experiment for the (theory-)optimal anglesDumitrescu, E. F., et al. PRL 120 (2018)UMD/IonQ: error margin 0.8(3)%
26. Dumitrescu, E. F., et al. PRL 120 (2018)Ground state of the Deuteron nucleusCanonical 4-qubit UCC ansatz4-qubit Hamiltonian (EFT), -2.14MeV:
27. Ground state of the Deuteron4-qubit theory: -2.14 MeV:Experimental binding energy value: -2.2(1)MeVparameter space
28. Quantum machine learning: Bars and Stripes
29. Quantum machine learning: Bars and StripesD. Zhu et al. arXiv 1806.02807
30. Quantum machine learning: Bars and StripesD. Zhu et al. arXiv 1806.02807
31. Quantum machine learning: Bars and StripesD. Zhu et al. arXiv 1806.02807
32. Quantum machine learning: Bars and StripesAnimation from Wikipedia by EphramacClassical Leaner 2: Bayesian Optimization (BO)Classical Leaner 1: Particle Swarm Optimization (PSO)surrogate modelUsing “Optaas” package by Mindfoundry (Oxford)
33. D. Zhu et al. arXiv 1806.02807Quantum machine learning: Particle Swarm Results
34. D. Zhu et al. arXiv 1806.02807Quantum machine learning: Particle Swarm Results
35. D. Zhu et al. arXiv 1806.02807Quantum machine learning: Particle Swarm Results
36. Quantum machine learning: Particle Swarm ResultsD. Zhu et al. arXiv 1806.02807
37. Quantum machine learning: Bayesian Optimization ResultsD. Zhu et al. arXiv 1806.02807
38. Quantum machine learning: Bayesian Optimization ResultsD. Zhu et al. arXiv 1806.02807
39. Quantum machine learning: Bayesian Optimization ResultsD. Zhu et al. arXiv 1806.02807successful 26-parameter optimization
40. Quantum machine learning… thoughtsD. Zhu et al. arXiv 1806.02807
41. no system will be fully connected for large Nthe compilation challengeD. Kielpinski et al., Nature 417 (2002)C. Monroe et al., Phys. Rev. A 89 (2014)Outlook : the future - scaling upD. Hucul, et al., Nature Phys. 11 (2015)
42. Michael Goldman 0.5 m Marko CetinaScaling concept 1: control over ~20 qubitsLaird Egan
43. “EURIQA”system
44. no system will be fully connected for large Nthe compilation challengeD. Kielpinski et al., Nature 417 (2002)C. Monroe et al., Phys. Rev. A 89 (2014)Scaling concept 2: ion-photon entanglementD. Hucul, et al., Nature Phys. 11 (2015)
45. A direct-transmission networking nodesmallest-wavelength minimum (Telecom O-band)
46. “phonon-polariton” statesScaling concept 3: motional degrees of freedom
47. Chris MonroeAlejandroPerdomo-Ortiz(NASA)Marcello Benedetti(UCL)Omar Shehab(IonQ)Yunseong Nam(IonQ)Cinthia H. AldereteDaiwei ZhuNhung NguyenNMLAutumn ChiuKevin LandsmanMika ChmielewskiSonika Johri (Intel)Tim Hsieh(Perimeter)