QUANTUM computing
BACK TO BASICS still beyond the point where decoherence makes quantum algorithms useless.
This mixed situation of current devices has prompted intense experimental efforts to make quantum error correction( QEC) work on the leading hardware platforms. QEC consists in protecting qubits against decoherence by spreading the information of one“ logical” qubit over many“ physical” qubits, and performing regular local measurements to detect and then correct local errors( see Fig. 4). Such a procedure is beneficial— the so-obtained logical qubit is better than the individual physical qubits— only if the individual qubits’ error rates are below a certain threshold. Recent experiments have shown error rates below this threshold, opening perspectives for future QEC. However, the number of physical qubits required to implement algorithms such as time evolution, phase estimation or Shor’ s algorithm exceeds one million, far from the number of qubits( 100-1000) available in today’ s prototypes. Going to these numbers will pose formidable scalability issues that make any prediction as to the first QEC-enabled quantum advantage a very tall order [ 16 ].
Whether near-term, uncorrected hardware will already provide quantum advantage on niche applications like many-body dynamics, or if this will be achieved by quantum error corrected hardware with the more traditional, gate-intensive quantum algorithms, is an open question. In fact, it could very well be that a clever blend of both paradigms delivers on the promises of quantum computers.
REFERENCES
1 ] P. W. Shor, Proceedings 35 th Annual Symposium on Foundations of Computer Science 1, 124( 1995), https:// doi. org / 10.1109 / SFCS. 1994.365700
[ 2 ] M. Schuld, N. Killoran, PRX Quantum 3, 030101( 2022), https:// doi. org / 10.1103 / PRXQuantum. 3.030101
[ 3 ] R. P. Feynman, Int. J. Theor. Phys. 21, 467( 1982). https:// doi. org / 10.1007 / BF02650179 [ 4 ] T. Ayral, C. R. Physique 26, 25( 2025). https:// doi. org / 10.5802 / crphys. 229
[ 5 ] T. Ayral, P. Besserve, D. Lacroix, E. A. Ruiz Guzman, Eur. Phys. J. A 59, 227( 2023), https:// doi. org / 10.1140 / epja / s10050-023-01141-1
[ 6 ] R. Cleve, A. Ekert, C. Macchiavello, M. Mosca, Proc. R. Soc. A 454, 339( 1998), https:// doi. org / 10.1098 / rspa. 1998.0164
[ 7 ] A. W. Harrow, A. Hassidim, S. Lloyd, Phys. Rev. Lett. 103, 150502( 2009). https:// doi. org / 10.1103 / PhysRevLett. 103.150502
[ 8 ] P. W. Shor, Phys. Rev. A 52, R2493( 1995). https:// doi. org / 10.1103 / PhysRevA. 52. R2493
[ 9 ] A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, J. L. O’ Brien, Nat. Commun. 5, 4213( 2013). https:// doi. org / 10.1038 / ncomms5213
[ 10 ] M. Larocca, S. Thanasilp, S. Wang, K. Sharma, J. Biamonte, P. J. Coles, L. Cincio, J. R. McClean, Z. Holmes, M. Cerezo, " A Review of Barren Plateaus in Variational Quantum Computing," arXiv: 2405.00781( 2024). http:// arxiv. org / abs / 2405.00781
[ 11 ] F. Arute et al., Nature 574, 505( 2019). https:// doi. org / 10.1038 / s41586-019-1666-5
[ 12 ] T. Ayral, T. Louvet, Y. Zhou, C. Lambert, E. M. Stoudenmire, X. Waintal, PRX Quantum 4, 020304( 2023). https:// doi. org / 10.1103 / PRXQuantum. 4.020304
[ 13 ] A. Morvan et al., Nature 634, 328( 2024). https:// doi. org / 10.1038 / s41586-024-07998-6 [ 14 ] Y. Kim et al., Nature 618, 500( 2023). https:// doi. org / 10.1038 / s41586-023-06096-3
[ 15 ] J. Tindall, M. Fishman, E. M. Stoudenmire, D. Sels, PRX Quantum 5, 010308( 2024). https:// doi. org / 10.1103 / PRXQuantum. 5.010308
[ 16 ] M. Mohseni et al., " How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities," arXiv: 2411.10406( 2024). http:// arxiv. org / abs / 2411.10406
Photoniques 131 I www. photoniques. com 71