Physical and logical qubits

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In quantum computing, a qubit is a unit of information analogous to a bit (binary digit) in classical computing, but it is affected by quantum mechanical properties such as superposition and entanglement which allow qubits to be in some ways more powerful than classical bits for some tasks. Qubits are used in quantum circuits and quantum algorithms composed of quantum logic gates to solve computational problems, where they are used for input/output and intermediate computations.

Contents

A physical qubit is a physical device that behaves as a two-state quantum system, used as a component of a computer system. [1] [2] A logical qubit is a physical or abstract qubit that performs as specified in a quantum algorithm or quantum circuit [3] subject to unitary transformations, has a long enough coherence time to be usable by quantum logic gates (c.f. propagation delay for classical logic gates). [1] [4] [5]

Since the development of the first quantum computer in 1998, most technologies used to implement qubits face issues of stability, decoherence, [6] [7] fault tolerance [8] [9] and scalability. [6] [9] [10] Because of this, many physical qubits are needed for the purposes of error-correction to produce an entity which behaves logically as a single qubit would in a quantum circuit or algorithm; this is the subject of quantum error correction. [3] [11] Thus, contemporary logical qubits typically consist of many physical qubits to provide stability, error-correction and fault tolerance needed to perform useful computations. [1] [7] [11]

In 2023, Google researchers showed how quantum error correction can improve logical qubit performance by increasing the physical qubit count. [12] These results found that a larger logical qubit (49 physical qubits) had a lower error rate, about 2.9 percent per round of error correction, compared to a rate of about 3.0 percent for the smaller logical qubit (17 physical qubits). [13]

In 2024, IBM researchers created a quantum error correction code 10 times more efficient than previous research, protecting 12 logical qubits for roughly a million cycles of error checks using 288 qubits. [14] [15] The work demonstrates error correction on near-term devices while reducing overhead – the number of physical qubits required to keep errors low. [16]

In 2024, Microsoft and Quantinuum announced experimental results that showed logical qubits could be created with significantly fewer physical qubits. [17] The team used quantum error correction techniques developed by Microsoft and Quantinuum's trapped ion hardware to use 30 physical qubits to form four logical qubits. Scientists used a qubit virtualization system and active syndrome extraction—also called repeated error correction to accomplish this. [18] This work defines how to achieve logical qubits within quantum computation. [19]

Overview

1-bit and 2-bit quantum gate operations have been shown to be universal. [20] [21] [22] [23] A quantum algorithm can be instantiated as a quantum circuit. [24] [25]

A logical qubit specifies how a single qubit should behave in a quantum algorithm, subject to quantum logic operations which can be built out of quantum logic gates. However, issues in current technologies preclude single two-state quantum systems, which can be used as physical qubits, from reliably encoding and retaining this information for long enough to be useful. Therefore, current attempts to produce scalable quantum computers require quantum error correction, and multiple (currently many) physical qubits must be used to create a single, error-tolerant logical qubit. Depending on the error-correction scheme used, and the error rates of each physical qubit, a single logical qubit could be formed of up to 1,000 physical qubits. [26]

Topological quantum computing

The approach of topological qubits, which takes advantage of topological effects in quantum mechanics, has been proposed as needing many fewer or even a single physical qubit per logical qubit. [10] Topological qubits rely on a class of particles called anyons which have spin that is neither half-integral (fermions) nor integral (bosons), and therefore obey neither the Fermi–Dirac statistics nor the Bose–Einstein statistics of particle behavior. [27] Anyons exhibit braid symmetry in their world lines, which has desirable properties for the stability of qubits. Notably, anyons must exist in systems constrained to two spatial dimensions or fewer, according to the spin–statistics theorem, which states that in 3 or more spatial dimensions, only fermions and bosons are possible. [27]

See also

Related Research Articles

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References

  1. 1 2 3 Shaw, Bilal; Wilde, Mark M.; Oreshkov, Ognyan; Kremsky, Isaac; Lidar, Daniel A. (2008-07-18). "Encoding One Logical Qubit Into Six Physical Qubits". Physical Review A. 78 (1): 012337. arXiv: 0803.1495 . Bibcode:2008PhRvA..78a2337S. doi:10.1103/PhysRevA.78.012337. ISSN   1050-2947. S2CID   40040752.
  2. Viola, Lorenza; Knill, Emanuel; Laflamme, Raymond (2001-09-07). "Constructing Qubits in Physical Systems". Journal of Physics A: Mathematical and General. 34 (35): 7067–7079. arXiv: quant-ph/0101090 . Bibcode:2001JPhA...34.7067V. doi:10.1088/0305-4470/34/35/331. ISSN   0305-4470. S2CID   14713492.
  3. 1 2 Heeres, Reinier W.; Reinhold, Philip; Ofek, Nissim; Frunzio, Luigi; Jiang, Liang; Devoret, Michel H.; Schoelkopf, Robert J. (2016-08-08). "Implementing a Universal Gate Set on a Logical Qubit Encoded in an Oscillator". Nature Communications. 8 (1): 94. arXiv: 1608.02430 . doi:10.1038/s41467-017-00045-1. ISSN   2041-1723. PMC   5522494 . PMID   28733580.
  4. "Logical Qubits (LogiQ)". Intelligence Advanced Research Projects Activity. Retrieved 2018-09-18.
  5. "Logical Qubits (LogiQ)". iarpa.gov. Retrieved 2018-10-04.
  6. 1 2 Ristè, D.; Poletto, S.; Huang, M.-Z.; Bruno, A.; Vesterinen, V.; Saira, O.-P.; DiCarlo, L. (2014-10-20). "Detecting bit-flip errors in a logical qubit using stabilizer measurements". Nature Communications. 6 (1): 6983. arXiv: 1411.5542 . doi:10.1038/ncomms7983. ISSN   2041-1723. PMC   4421804 . PMID   25923318.
  7. 1 2 Kapit, Eliot (2016-04-12). "A Very Small Logical Qubit". Physical Review Letters. 116 (15): 150501. arXiv: 1510.06117 . doi:10.1103/PhysRevLett.116.150501. ISSN   0031-9007. PMID   27127945. S2CID   118476207.
  8. Nigg, Daniel; Mueller, Markus; Martinez, Esteban A.; Schindler, Philipp; Hennrich, Markus; Monz, Thomas; Martin-Delgado, Miguel A.; Blatt, Rainer (2014-07-18). "Experimental Quantum Computations on a Topologically Encoded Qubit". Science. 345 (6194): 302–305. arXiv: 1403.5426 . Bibcode:2014Sci...345..302N. doi:10.1126/science.1253742. ISSN   0036-8075. PMID   24925911. S2CID   9677048.
  9. 1 2 "Achieving scalability in quantum computing". Microsoft Cloud Blogs. Microsoft. 2018-05-16. Retrieved 2018-09-18.
  10. 1 2 Mishmash, Ryan; Alicea, Jason (2017-08-16). "Topological qubits: Arriving in 2018?". Quantum Frontiers. Retrieved 2018-09-17.
  11. 1 2 Jones, Cody; Fogarty, Michael A.; Morello, Andrea; Gyure, Mark F.; Dzurak, Andrew S.; Ladd, Thaddeus D. (2018-06-01). "A logical qubit in a linear array of semiconductor quantum dots". Physical Review X. 8 (2): 021058. arXiv: 1608.06335 . Bibcode:2018PhRvX...8b1058J. doi:10.1103/PhysRevX.8.021058. ISSN   2160-3308. S2CID   119108989.
  12. Acharya, Rajeev (2023-02-22). "Suppressing quantum errors by scaling a surface code logical qubit". Nature. 614 (7949): 676–681. arXiv: 2207.06431 . Bibcode:2023Natur.614..676G. doi:10.1038/s41586-022-05434-1. ISSN   1476-4687. PMC   9946823 . PMID   36813892.
  13. Conover, Emily (2023-02-22). "Google's quantum computer reached an error-correcting milestone". ScienceNews. Retrieved 2024-07-09.
  14. Bravyi, Sergei (2024-03-27). "High-threshold and low-overhead fault-tolerant quantum memory". Nature. 627 (8005): 778–782. arXiv: 2308.07915 . Bibcode:2024Natur.627..778B. doi:10.1038/s41586-024-07107-7. ISSN   1476-4687. PMC   10972743 . PMID   38538939.
  15. Swayne, Matt (2024-03-28). "IBM Reports 10 Times More Efficient Error-Correcting Method Brings Practical Quantum Computers Closer To Reality". The Quantum Insider. Retrieved 2024-07-09.
  16. Crane, Leah (2023-08-18). "IBM has just made error correction easier for quantum computers". New Scientist. Retrieved 2024-07-09.
  17. Choi, Charles (2024-04-03). "Microsoft Tests New Path to Reliable Quantum Computers - 1,000 physical qubits for each logical one? Try a dozen, says Redmond". IEEE Spectrum. Retrieved 2024-07-09.
  18. Timmer, John (2024-04-03). "Quantum error correction used to actually correct errors". Ars Technica. Retrieved 2024-07-09.
  19. Sutor, Bob (2024-04-05). "Quantum in Context: Microsoft & Quantinuum Create Real Logical Qubits". The Futurum Group. Retrieved 2024-07-09.
  20. DiVincenzo, David P. (1995-02-01). "Two-bit gates are universal for quantum computation". Physical Review A. 51 (2): 1015–1022. arXiv: cond-mat/9407022 . Bibcode:1995PhRvA..51.1015D. doi:10.1103/PhysRevA.51.1015. PMID   9911679. S2CID   2317415.
  21. Deutsch, David; Barenco, Adriano; Ekert, Artur (1995-06-08). "Universality in Quantum Computation". Proceedings of the Royal Society of London A: Mathematical and Physical Sciences. 449 (1937): 669–677. arXiv: quant-ph/9505018 . Bibcode:1995RSPSA.449..669D. CiteSeerX   10.1.1.54.2646 . doi:10.1098/rspa.1995.0065. ISSN   1471-2946. S2CID   15088854.
  22. Barenco, Adriano (1995-06-08). "A Universal Two-Bit Gate for Quantum Computation". Proceedings of the Royal Society of London A: Mathematical and Physical Sciences. 449 (1937): 679–683. arXiv: quant-ph/9505016 . Bibcode:1995RSPSA.449..679B. doi:10.1098/rspa.1995.0066. ISSN   1471-2946. S2CID   119447556.
  23. Lloyd, Seth (1995-07-10). "Almost Any Quantum Logic Gate is Universal". Physical Review Letters. 75 (2): 346–349. Bibcode:1995PhRvL..75..346L. doi:10.1103/PhysRevLett.75.346. PMID   10059671.
  24. Yazdani, Maryam; Zamani, Morteza Saheb; Sedighi, Mehdi (2013-06-09). "A Quantum Physical Design Flow Using ILP and Graph Drawing". Quantum Information Processing Journal. 12 (10): 3239. arXiv: 1306.2037 . Bibcode:2013QuIP...12.3239Y. doi:10.1007/s11128-013-0597-6. S2CID   12195937.
  25. Whitney, Mark; Isailovic, Nemanja; Patel, Yatish; Kubiatowicz, John (2007-04-02). "Automated Generation of Layout and Control for Quantum Circuits". ACM Computing Frontiers. arXiv: 0704.0268 .
  26. Fowler, Austin G.; Mariantoni, Matteo; Martinis, John M.; Cleland, Andrew N. (2012). "Surface codes: Towards practical large-scale quantum computation". Physical Review A. 86 (3): 032324. arXiv: 1208.0928 . Bibcode:2012PhRvA..86c2324F. doi:10.1103/PhysRevA.86.032324. ISSN   1050-2947. S2CID   119277773.
  27. 1 2 Wilczek, Frank (2018-02-27). "How 'Anyon' Particles Emerge From Quantum Knots | Quanta Magazine". Quanta Magazine. Retrieved 2018-09-18.