Quantum

Sense, sensibility, and superconductors

Published

2 years ago

February 23, 2020

Jonathan Monroe disagreed with his PhD supervisor—with respect. They needed to measure a superconducting qubit, a tiny circuit in which current can flow forever. The qubit emits light, which carries information about the qubit’s state. Jonathan and Kater intensify the light using an amplifier. They’d fabricated many amplifiers, but none had worked. Jonathan suggested changing their strategy—with a politeness to which Emily Post couldn’t have objected. Jonathan’s supervisor, Kater Murch, suggested repeating the protocol they’d performed many times.

“That’s the definition of insanity,” Kater admitted, “but I think experiment needs to involve some of that.”

I watched the exchange via Skype, with more interest than I’d have watched the Oscars with. Someday, I hope, I’ll be able to weigh in on such a debate, despite working as a theorist. Someday, I’ll have partnered with enough experimentalists to develop insight.

I’m partnering with Jonathan and Kater on an experiment that coauthors and I proposed in a paper blogged about here. The experiment centers on an uncertainty relation, an inequality of the sort immortalized by Werner Heisenberg in 1927. Uncertainty relations imply that, if you measure a quantum particle’s position, the particle’s momentum ceases to have a well-defined value. If you measure the momentum, the particle ceases to have a well-defined position. Our uncertainty relation involves weak measurements. Weakly measuring a particle’s position doesn’t disturb the momentum much and vice versa. We can interpret the uncertainty in information-processing terms, because we cast the inequality in terms of entropies. Entropies, described here, are functions that quantify how efficiently we can process information, such as by compressing data. Jonathan and Kater are checking our inequality, and exploring its implications, with a superconducting qubit.

With chip

I had too little experience to side with Jonathan or with Kater. So I watched, and I contemplated how their opinions would sound if expressed about theory. Do I try one strategy again and again, hoping to change my results without changing my approach?

At the Perimeter Institute for Theoretical Physics, Masters students had to swallow half-a-year of course material in weeks. I questioned whether I’d ever understand some of the material. But some of that material resurfaced during my PhD. Again, I attended lectures about Einstein’s theory of general relativity. Again, I worked problems about observers in free-fall. Again, I calculated covariant derivatives. The material sank in. I decided never to question, again, whether I could understand a concept. I might not understand a concept today, or tomorrow, or next week. But if I dedicate enough time and effort, I chose to believe, I’ll learn.

My decision rested on experience and on classes, taught by educational psychologists, that I’d taken in college. I’d studied how brains change during learning and how breaks enhance the changes. Sense, I thought, underlay my decision—though expecting outcomes to change, while strategies remain static, sounds insane.

Old cover

Does sense underlie Kater’s suggestion, likened to insanity, to keep fabricating amplifiers as before? He’s expressed cynicism many times during our collaboration: Experiment needs to involve some insanity. The experiment probably won’t work for a long time. Plenty more things will likely break.

Jonathan and I agree with him. Experiments have a reputation for breaking, and Kater has a reputation for knowing experiments. Yet Jonathan—with professionalism and politeness—remains optimistic that other methods will prevail, that we’ll meet our goals early. I hope that Jonathan remains optimistic, and I fancy that Kater hopes, too. He prophesies gloom with a quarter of a smile, and his record speaks against him: A few months ago, I met a theorist who’d collaborated with Kater years before. The theorist marveled at the speed with which Kater had operated. A theorist would propose an experiment, and boom—the proposal would work.

Sea monsters

Perhaps luck smiled upon the implementation. But luck dovetails with the sense that underlies Kater’s opinion: Experiments involve factors that you can’t control. Implement a protocol once, and it might fail because the temperature has risen too high. Implement the protocol again, and it might fail because a truck drove by your building, vibrating the tabletop. Implement the protocol again, and it might fail because you bumped into a knob. Implement the protocol a fourth time, and it might succeed. If you repeat a protocol many times, your environment might change, changing your results.

Sense underlies also Jonathan’s objections to Kater’s opinions. We boost our chances of succeeding if we keep trying. We derive energy to keep trying from creativity and optimism. So rebelling against our PhD supervisors’ sense is sensible. I wondered, watching the Skype conversation, whether Kater the student had objected to prophesies of doom as Jonathan did. Kater exudes the soberness of a tenured professor but the irreverence of a Californian who wears his hair slightly long and who tattooed his wedding band on. Science thrives on the soberness and the irreverence.

Green cover

Who won Jonathan and Kater’s argument? Both, I think. Last week, they reported having fabricated amplifiers that work. The lab followed a protocol similar to their old one, but with more conscientiousness.

I’m looking forward to watching who wins the debate about how long the rest of the experiment takes. Either way, check out Jonathan’s talk about our experiment if you attend the American Physical Society’s March Meeting. Jonathan will speak on Thursday, March 5, at 12:03, in room 106. Also, keep an eye out for our paper—which will debut once Jonathan coaxes the amplifier into synching with his qubit.

Source: https://quantumfrontiers.com/2020/02/23/sense-sensibility-and-superconductors/

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Quantum

Instance Independence of Single Layer Quantum Approximate Optimization Algorithm on Mixed-Spin Models at Infinite Size

Published

1 day ago

September 15, 2021

Republished by Plato

Jahan Claes^1,2 and Wim van Dam^1,3,4

¹QC Ware Corporation, Palo Alto, CA USA
²Department of Physics and Institute for Condensed Matter Theory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
³Department of Computer Science, University of California, Santa Barbara, CA USA
⁴Department of Physics, University of California, Santa Barbara, CA USA

Find this paper interesting or want to discuss? Scite or leave a comment on SciRate.

Abstract

This paper studies the application of the Quantum Approximate Optimization Algorithm (QAOA) to spin-glass models with random multi-body couplings in the limit of a large number of spins. We show that for such mixed-spin models the performance of depth $1$ QAOA is independent of the specific instance in the limit of infinite sized systems and we give an explicit formula for the expected performance. We also give explicit expressions for the higher moments of the expected energy, thereby proving that the expected performance of QAOA concentrates.

► BibTeX data

@article{Claes2021instance, doi = {10.22331/q-2021-09-15-542}, url = {https://doi.org/10.22331/q-2021-09-15-542}, title = {Instance {I}ndependence of {S}ingle {L}ayer {Q}uantum {A}pproximate {O}ptimization {A}lgorithm on {M}ixed-{S}pin {M}odels at {I}nfinite {S}ize}, author = {Claes, Jahan and Dam, Wim van}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{“{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {5}, pages = {542}, month = sep, year = {2021}<br /> }

► References

[1] Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028, 2014. URL https://arxiv.org/abs/1411.4028.
arXiv:1411.4028

[2] Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Leo Zhou. The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick model at infinite size. arXiv preprint arXiv:1910.08187, 2019. URL https://arxiv.org/abs/1910.08187.
arXiv:1910.08187

[3] Andrea Montanari. Optimization of the Sherrington–Kirkpatrick hamiltonian. SIAM Journal on Computing, 0 (0): FOCS19–1, 2021. 10.1137/20M132016X.
https://doi.org/10.1137/20M132016X

[4] Ahmed El Alaoui, Andrea Montanari, and Mark Sellke. Optimization of mean-field spin glasses. arXiv preprint arXiv:2001.00904, 2020. URL https://arxiv.org/abs/2001.00904.
arXiv:2001.00904

[5] Ahmed El Alaoui and Andrea Montanari. Algorithmic thresholds in mean field spin glasses. arXiv preprint arXiv:2009.11481, 2020. URL https://arxiv.org/abs/2009.11481.
arXiv:2009.11481

[6] Zhang Jiang, Eleanor G Rieffel, and Zhihui Wang. Near-optimal quantum circuit for Grover’s unstructured search using a transverse field. Physical Review A, 95 (6): 062317, 2017. 10.1103/PhysRevA.95.062317.
https://doi.org/10.1103/PhysRevA.95.062317

[7] Zhihui Wang, Stuart Hadfield, Zhang Jiang, and Eleanor G Rieffel. Quantum approximate optimization algorithm for maxcut: A fermionic view. Physical Review A, 97 (2): 022304, 2018. 10.1103/PhysRevA.97.022304.
https://doi.org/10.1103/PhysRevA.97.022304

[8] Asier Ozaeta, Wim van Dam, and Peter L. McMahon. Expectation values from the single-layer quantum approximate optimization algorithm on Ising problems. arXiv preprint arXiv:2012.03421, 2020. URL https://arxiv.org/abs/2012.03421.
arXiv:2012.03421

[9] Gavin E Crooks. Performance of the quantum approximate optimization algorithm on the maximum cut problem. arXiv preprint arXiv:1811.08419, 2018. URL https://arxiv.org/abs/1811.08419.
arXiv:1811.08419

[10] Leo Zhou, Sheng-Tao Wang, Soonwon Choi, Hannes Pichler, and Mikhail D Lukin. Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices. Physical Review X, 10 (2): 021067, 2020. 10.1103/PhysRevX.10.021067.
https://doi.org/10.1103/PhysRevX.10.021067

[11] Fernando GSL Brandao, Michael Broughton, Edward Farhi, Sam Gutmann, and Hartmut Neven. For fixed control parameters the quantum approximate optimization algorithm’s objective function value concentrates for typical instances. arXiv preprint arXiv:1812.04170, 2018. URL https://arxiv.org/abs/1812.04170.
arXiv:1812.04170

[12] David Sherrington and Scott Kirkpatrick. Solvable model of a spin-glass. Physical Review Letters, 35 (26): 1792, 1975. 10.1103/PhysRevLett.35.1792.
https://doi.org/10.1103/PhysRevLett.35.1792

[13] Giorgio Parisi. A sequence of approximated solutions to the SK model for spin glasses. Journal of Physics A: Mathematical and General, 13 (4): L115, 1980. 10.1088/0305-4470/13/4/009.
https://doi.org/10.1088/0305-4470/13/4/009

[14] Michel Talagrand. The Parisi formula. Annals of mathematics, pages 221–263, 2006. 10.4007/annals.2006.163.221.
https://doi.org/10.4007/annals.2006.163.221

[15] Dmitry Panchenko. The Sherrington-Kirkpatrick model: an overview. Journal of Statistical Physics, 149 (2): 362–383, 2012. 10.1007/s10955-012-0586-7.
https://doi.org/10.1007/s10955-012-0586-7

[16] Dmitry Panchenko. The Sherrington-Kirkpatrick model. Springer Science & Business Media, 2013. 10.1007/978-1-4614-6289-7.
https://doi.org/10.1007/978-1-4614-6289-7

[17] Antonio Auffinger, Wei-Kuo Chen, et al. Parisi formula for the ground state energy in the mixed $ p $-spin model. Annals of Probability, 45 (6B): 4617–4631, 2017. 10.1214/16-AOP1173.
https://doi.org/10.1214/16-AOP1173

[18] Dmitry Panchenko et al. The Parisi formula for mixed $p$-spin models. Annals of Probability, 42 (3): 946–958, 2014. 10.1214/12-AOP800.
https://doi.org/10.1214/12-AOP800

[19] Leo Zhou, Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick model at infinite size. Talk presented at QIP, 2021. URL https://youtu.be/UP-Zuke7IUg.
https://youtu.be/UP-Zuke7IUg

Cited by

[1] V. Akshay, D. Rabinovich, E. Campos, and J. Biamonte, “Parameter concentrations in quantum approximate optimization”, Physical Review A 104 1, L010401 (2021).

[2] E. Campos, D. Rabinovich, V. Akshay, and J. Biamonte, “Training Saturation in Layerwise Quantum Approximate Optimisation”, arXiv:2106.13814.

[3] Jordi R. Weggemans, Alexander Urech, Alexander Rausch, Robert Spreeuw, Richard Boucherie, Florian Schreck, Kareljan Schoutens, Jiří Minář, and Florian Speelman, “Solving correlation clustering with QAOA and a Rydberg qudit system: a full-stack approach”, arXiv:2106.11672.

The above citations are from SAO/NASA ADS (last updated successfully 2021-09-15 16:50:26). The list may be incomplete as not all publishers provide suitable and complete citation data.

Could not fetch Crossref cited-by data during last attempt 2021-09-15 16:50:24: Could not fetch cited-by data for 10.22331/q-2021-09-15-542 from Crossref. This is normal if the DOI was registered recently.

This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.

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Source: https://quantum-journal.org/papers/q-2021-09-15-542/