Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Hardware Trojans in Quantum Circuits, Their Impacts, and Defense (2402.01552v1)

Published 2 Feb 2024 in cs.CR

Abstract: The reliability of the outcome of a quantum circuit in near-term noisy quantum computers depends on the gate count and depth for a given problem. Circuits with a short depth and lower gate count can yield the correct solution more often than the variant with a higher gate count and depth. To work successfully for Noisy Intermediate Scale Quantum (NISQ) computers, quantum circuits need to be optimized efficiently using a compiler that decomposes high-level gates to native gates of the hardware. Many 3rd party compilers are being developed for lower compilation time, reduced circuit depth, and lower gate count for large quantum circuits. Such compilers, or even a specific release version of a compiler that is otherwise trustworthy, may be unreliable and give rise to security risks such as insertion of a quantum trojan during compilation that evades detection due to the lack of a golden/Oracle model in quantum computing. Trojans may corrupt the functionality to give flipped probabilities of basis states, or result in a lower probability of correct basis states in the output. In this paper, we investigate and discuss the impact of a single qubit Trojan (we have chosen a Hadamard gate and a NOT gate) inserted one at a time at various locations in benchmark quantum circuits without changing the the depth of the circuit. Results indicate an average of 16.18% degradation for the Hadamard Trojan without noise, and 7.78% with noise. For the NOT Trojan (with noise) there is 14.6% degradation over all possible inputs. We then discuss the detection of such Trojans in a quantum circuit using CNN-based classifier achieving an accuracy of 90%.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (17)
  1. F. Bova, A. Goldfarb, and R. G. Melko, “Commercial applications of quantum computing,” EPJ quantum technology, vol. 8, no. 1, p. 2, 2021.
  2. National Academies of Sciences, Engineering, and Medicine and others,“Quantum computing: progress and prospects,” 2019.
  3. D. P. DiVincenzo, “Quantum computation,” Science, vol. 270, no. 5234,pp. 255–261, 1995.
  4. M. A. Nielsen and I. Chuang, “Quantum computation and quantum information,” 2002.
  5. P. Krantz, M. Kjaergaard, F. Yan, T. P. Orlando, S. Gustavsson, and W. D. Oliver, “A quantum engineer’s guide to superconducting qubits,” Applied physics reviews, vol. 6, no. 2, p. 021318, 2019.
  6. C. D. Bruzewicz, J. Chiaverini, R. McConnell, and J. M. Sage, “Trappedion quantum computing: Progress and challenges,” Applied Physics Reviews, vol. 6, no. 2, p. 021314, 2019.
  7. S. Slussarenko and G. J. Pryde, “Photonic quantum information processing: A concise review,” Applied Physics Reviews, vol. 6, no. 4, p.041303, 2019.
  8. Y. Arakawa and M. J. Holmes, “Progress in quantum-dot single photon sources for quantum information technologies: A broad spectrum overview,” Applied Physics Reviews, vol. 7, no. 2, p. 021309, 2020.
  9. S. Pezzagna and J. Meijer, “Quantum computer based on color centers in diamond,” Applied Physics Reviews, vol. 8, no. 1, p. 011308, 2021.
  10. M. A. Cusumano, “The business of quantum computing,” Communications of the ACM, vol. 61, no. 10, pp. 20–22, 2018.
  11. A. D. Corcoles, A. Kandala, A. Javadi-Abhari, D. T. McClure, A. W.Cross, K. Temme, P. D. Nation, M. Steffen, and J. M. Gambetta, “Challenges and opportunities of near-term quantum computing systems,” arXiv preprint arXiv:1910.02894, 2019.
  12. R. Shaydulin, H. Ushijima-Mwesigwa, C. F. Negre, I. Safro, S. M. Mniszewski, and Y. Alexeev, “A hybrid approach for solving optimization problems on small quantum computers,” Computer, vol. 52, no. 6, pp. 18–26, 2019.
  13. R. S. Smith, E. C. Peterson, M. G. Skilbeck, and E. J. Davis, “An opensource, industrial-strength optimizing compiler for quantum programs,” Quantum Science and Technology, vol. 5, no. 4, p. 044001, 2020.
  14. Z. Computing, “Orquestra,” Apr 2023. [Online]. Available: https://www.zapatacomputing.com/orquestra-platform/
  15. C. Q. Computing, “Pytket,” Apr 2023. [Online]. Available: https: //cqcl.github.io/tket/pytket/api/index.html
  16. A. Cross, “The ibm q experience and qiskit open-source quantum computing software,” in APS Meeting Abstracts, 2018.
  17. Y. Nam, N. J. Ross, Y. Su, A. M. Childs, and D. Maslov, “Automated optimization of large quantum circuits with continuous parameters,” npj Quantum Information, vol. 4, no. 1, pp. 1–12, 2018.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Rupshali Roy (5 papers)
  2. Subrata Das (26 papers)
  3. Swaroop Ghosh (97 papers)

Summary

We haven't generated a summary for this paper yet.