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  • Letter
  • Open Access

Optimized quantum sensor networks for ultralight dark matter detection

Adriel I. Santoso1 and Le Bin Ho2,3,*

  • 1Department of Mechanical and Aerospace Engineering, Tohoku University, Sendai 980-0845, Japan
  • 2Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai 980-8578, Japan
  • 3Department of Applied Physics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan

  • *Contact author: binho@fris.tohoku.ac.jp
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Phys. Rev. D 112, L081301 – Published 1 October, 2025

DOI: https://doi.org/10.1103/rv43-54zq

Abstract

Dark matter (DM) remains one of the most compelling unresolved problems in fundamental physics, motivating the search for new detection approaches. We propose a network-based quantum sensor architecture to enhance sensitivity to ultralight DM fields. Each node in the network is a superconducting qubit, interconnected via controlled-Z gates in symmetric topologies such as line, ring, star, and fully connected graphs. We investigate four- and nine-qubit systems, optimizing both state preparation and measurement using a variational quantum metrology framework. This approach minimizes the quantum and classical Cramér-Rao bounds to identify optimal configurations. Bayesian inference is employed to extract the DM-induced phase shift from measurement outcomes. Our results show that optimized network configurations significantly outperform conventional Greenberger-Horne-Zeilinger-based protocols while maintaining shallow circuit depths compatible with noisy intermediate-scale quantum hardware. Sensitivity remains robust under local dephasing noise. These findings highlight the importance of network structure in quantum sensing and point toward scalable strategies for quantum-enhanced DM detection.

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References (36)

  1. G. Bertone and D. Hooper, History of dark matter, Rev. Mod. Phys. 90, 045002 (2018).
  2. M. Tanabashi et al. (Particle Data Group), Review of particle physics, Phys. Rev. D 98, 030001 (2018).
  3. S. D. M. White and M. J. Rees, Core condensation in heavy halos: A two-stage theory for galaxy formation and clustering, Mon. Not. R. Astron. Soc. 183, 341 (1978).
  4. E. Bertschinger, Simulations of structure formation in the Universe, Annu. Rev. Astron. Astrophys. 36, 599 (1998).
  5. F. Zwicky, Republication of: The redshift of extragalactic nebulae, Gen. Relativ. Gravit. 41, 207 (2009).
  6. V. C. Rubin, W. K. Ford, and N. Thonnard, Rotational properties of 21 SC galaxies with a large range of luminosities and radii, from NGC 4605 (R=4  kpc) to UGC 2885 (R=122  kpc), Astrophys. J. 238, 471 (1980).
  7. J. L. Feng, Dark matter candidates from particle physics and methods of detection, Annu. Rev. Astron. Astrophys. 48, 495 (2010).
  8. L. Baudis, Dark matter detection, J. Phys. G 43, 044001 (2016).
  9. A. Boyarsky, D. Iakubovskyi, and O. Ruchayskiy, Next decade of sterile neutrino studies, Phys. Dark Universe 1, 136 (2012).
  10. A. H. G. Peter, Dark matter: A brief review, arXiv:1201.3942.
  11. M. D. Campos and W. Rodejohann, Testing keV sterile neutrino dark matter in future direct detection experiments, Phys. Rev. D 94, 095010 (2016).
  12. M. Schumann, Direct detection of wimp dark matter: Concepts and status, J. Phys. G 46, 103003 (2019).
  13. L. Roszkowski, E. M. Sessolo, and S. Trojanowski, Wimp dark matter candidates and searches—Current status and future prospects, Rep. Prog. Phys. 81, 066201 (2018).
  14. A. Ringwald, Exploring the role of axions and other wisps in the dark universe, Phys. Dark Universe 1, 116 (2012).
  15. A. O. Sushkov, Quantum science and the search for axion dark matter, PRX Quantum 4, 020101 (2023).
  16. S. Chen, H. Fukuda, T. Inada, T. Moroi, T. Nitta, and T. Sichanugrist, Detecting hidden photon dark matter using the direct excitation of transmon qubits, Phys. Rev. Lett. 131, 211001 (2023).
  17. A. V. Dixit, S. Chakram, K. He, A. Agrawal, R. K. Naik, D. I. Schuster, and A. Chou, Searching for dark matter with a superconducting qubit, Phys. Rev. Lett. 126, 141302 (2021).
  18. R. Moretti et al., Design and simulation of a transmon qubit chip for axion detection, IEEE Trans. Appl. Supercond. 34, 1 (2024).
  19. S. Chen, H. Fukuda, T. Inada, T. Moroi, T. Nitta, and T. Sichanugrist, Quantum enhancement in dark matter detection with quantum computation, Phys. Rev. Lett. 133, 021801 (2024).
  20. S. Chen, H. Fukuda, T. Inada, T. Moroi, T. Nitta, and T. Sichanugrist, Search for QCD axion dark matter with transmon qubits and quantum circuit, Phys. Rev. D 110, 115021 (2024).
  21. M. H. Zaheer, N. J. Matjelo, D. B. Hume, M. S. Safronova, and D. R. Leibrandt, Quantum metrology algorithms for dark matter searches with clocks, Phys. Rev. A 111, 012601 (2025).
  22. T. J. Proctor, P. A. Knott, and J. A. Dunningham, Multiparameter estimation in networked quantum sensors, Phys. Rev. Lett. 120, 080501 (2018).
  23. C. Dailey, C. Bradley, D. F. Jackson Kimball, I. A. Sulai, S. Pustelny, A. Wickenbrock, and A. Derevianko, Quantum sensor networks as exotic field telescopes for multi-messenger astronomy, Nat. Astron. 5, 150 (2021).
  24. N. Shettell and D. Markham, Graph states as a resource for quantum metrology, Phys. Rev. Lett. 124, 110502 (2020).
  25. P. T. Nguyen, T. K. Le, H. Q. Nguyen, and L. B. Ho, Harnessing graph state resources for robust quantum magnetometry under noise, Sci. Rep. 14, 20528 (2024).
  26. T. K. Le, H. Q. Nguyen, and L. B. Ho, Variational quantum metrology for multiparameter estimation under dephasing noise, Sci. Rep. 13, 17775 (2023).
  27. J. J. Meyer, J. Borregaard, and J. Eisert, A variational toolbox for quantum multi-parameter estimation, npj Quantum Inf. 7, 89 (2021).
  28. B. MacLellan, P. Roztocki, S. Czischek, and R. G. Melko, End-to-end variational quantum sensing, npj Quantum Inf. 10, 118 (2024).
  29. D. DeMille, N. R. Hutzler, A. M. Rey, and T. Zelevinsky, Quantum sensing and metrology for fundamental physics with molecules, Nat. Phys. 20, 741 (2024).
  30. V. Giovannetti, S. Lloyd, and L. Maccone, Advances in quantum metrology, Nat. Photonics 5, 222 (2011).
  31. V. Cimini, M. Valeri, S. Piacentini, F. Ceccarelli, G. Corrielli, R. Osellame, N. Spagnolo, and F. Sciarrino, Variational quantum algorithm for experimental photonic multiparameter estimation, npj Quantum Inf. 10, 26 (2024).
  32. J. A. H. Nielsen, M. J. Kicinski, T. N. Arge, K. Vijayadharan, J. Foldager, J. Borregaard, J. J. Meyer, J. S. Neergaard-Nielsen, T. Gehring, and U. L. Andersen, Variational quantum algorithm for enhanced continuous variable optical phase sensing, npj Quantum Inf. 11, 70 (2025).
  33. A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien, A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5, 4213 (2014).
  34. M. Cerezo, A. Sone, T. Volkoff, L. Cincio, and P. J. Coles, Cost function dependent barren plateaus in shallow parametrized quantum circuits, Nat. Commun. 12, 1791 (2021).
  35. L. B. Ho, A stochastic evaluation of quantum fisher information matrix with generic Hamiltonians, Eur. Phys. J. Quantum Technol. 10, 37 (2023).
  36. A. I. Santoso and L. B. Ho, Supporting data for optimized quantum sensor networks for ultralight dark matter detection (2025), https://github.com/echkon/darkmatter.

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Phys. Rev. D 112, L081301– Published 1 October, 2025
  • Received 27 May 2025
  • Accepted 15 September 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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