Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Predicting Magic from Very Few Measurements

J. M. Varela1,2, L. L. Keller2, A. de Oliveira Junior3, D. A. Moreira4, R. Chaves2,4, and R. A. Macêdo1,2,5

PRX Quantum 7, 043009 – Published 14 October, 2026

DOI: https://doi.org/10.1103/2xnf-jcyn

Abstract

The nonstabilizerness of quantum states is a necessary resource for universal quantum computation, yet its characterization is notoriously demanding. Quantifying nonstabilizerness typically requires an exponential number of measurements and a doubly exponential classical post-processing cost to evaluate its standard monotones. In this work, we show that nonstabilizerness is, to a large extent, in the eyes of the beholder: it can be witnessed and quantified using any set of mn-qubit Pauli measurements, provided the set contains anti-commuting pairs. We introduce a general framework that projects the stabilizer polytope onto the subspace defined by these observables and provide an algorithm that estimates magic from Pauli expectation values with runtime exponential in the number of measurements m and polynomial in the number of qubits n. By relating the problem to a stabilizer-restricted variant of the quantum marginal problem, we also prove that deciding membership in the corresponding reduced stabilizer polytope is NP-hard. In particular, unless P=NP, no algorithm polynomial in m can solve the problem in full generality, thus establishing fundamental complexity-theoretic limitations. Finally, we employ our framework to compute nonstabilizerness in different Hamiltonian ground states, demonstrating the practical performance of our method in regimes beyond the reach of existing techniques.

View figure in article

Physics Subject Headings (PhySH)

Popular Summary

Article Text

References (67)

  1. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, England, 2010).
  2. M. M. Wilde, Quantum Information Theory (Cambridge University Press, Cambridge, England, 2013).
  3. D. Gottesman. Stabilizer codes and quantum error correction, Ph.D. thesis, 1997.
  4. D. Gottesman, The heisenberg representation of quantum computers, arXiv:quant-ph/9807006.
  5. S. Aaronson and D. Gottesman, Improved simulation of stabilizer circuits, Phys. Rev. A 70, 052328 (2004).
  6. S. Bravyi and A. Kitaev, Universal quantum computation with ideal Clifford gates and noisy ancillas, Phys. Rev. A 71, 022316 (2005).
  7. A. Gu, L. Leone, S. Ghosh, J. Eisert, S. F. Yelin, and Y. Quek, Pseudomagic quantum states, Phys. Rev. Lett. 132, 210602 (2024).
  8. C. J. Cao, G. Cheng, A. Hamma, L. Leone, W. Munizzi, and S. F. E. Oliviero, Gravitational backreaction is magical, PRX Quantum 6, 040375 (2025).
  9. S. Zamora, R. A. Macêdo, T. S. Sarubi, Més Alves, D. Poderini, and R. Chaves, Semi-device-independent nonstabilizerness certification in the prepare-and-measure scenario, Phys. Rev. A 112, 042410 (2025).
  10. A. de Oliveira Junior, R. A. Macedo, J. Czartowski, J. B. Brask, and R. Chaves, Trading athermality for nonstabiliserness, Phys. Rev. A 113, 052421 (2025).
  11. H. Lóio, G. Lami, L. Leone, M. McGinley, X. Turkeshi, and J. De Nardis, Quantum state designs via magic teleportation, arXiv:2510.13950.
  12. V. Veitch, S. A. Hamed Mousavian, D. Gottesman, and J. Emerson, The resource theory of stabilizer quantum computation, New J. Phys. 16, 013009 (2014).
  13. L. Leone, J. Eisert, and S. F. Oliviero, The unbearable hardness of deciding about magic, arXiv:2602.22330.
  14. M. Heinrich and D. Gross, Robustness of magic and symmetries of the stabiliser polytope, Quantum 3, 132 (2019).
  15. H. Timsina, Y.-M. Ding, E. Tirrito, P. S. Tarabunga, B.-B. Mao, M. Collura, Z. Yan, and M. Dalmonte, Robustness of nonstabilizerness in the quantum ising chain via quantum Monte Carlo tomography, Phys. Rev. B 112, 165135 (2025).
  16. F. Arute et al., Quantum supremacy using a programmable superconducting processor, Nature (London) 574, 505 (2019).
  17. H.-S. Zhong et al., Quantum computational advantage using photons, Science 370, 1460 (2020).
  18. S. Ebadi, T. T. Wang, H. Levine, A. Keesling, G. Semeghini, A. Omran, D. Bluvstein, R. Samajdar, H. Pichler, W. W. Ho, S. Choi, S. Sachdev, M. Greiner, V. Vuletić, and M. D. Lukin, Quantum phases of matter on a 256-atom programmable quantum simulator, Nature (London) 595, 227 (2021).
  19. Y. Wu et al., Strong quantum computational advantage using a superconducting quantum processor, Phys. Rev. Lett. 127, 180501 (2021).
  20. H.-Y. Huang, R. Kueng, and J. Preskill, Predicting many properties of a quantum system from very few measurements, Nat. Phys. 16, 1050 (2020).
  21. P. Gokhale, O. Angiuli, Y. Ding, K. Gui, T. Tomesh, M. Suchara, M. Martonosi, and F. T. Chong, Minimizing state preparations in variational quantum eigensolver by partitioning into commuting families, arXiv:1907.13623.
  22. A. Chapman and S. T. Flammia, Characterization of solvable spin models via graph invariants, Quantum 4, 278 (2020).
  23. R. L. Mann, S. J. Elman, D. R. Wood, and A. Chapman, A graph-theoretic framework for free-parafermion solvability, Proc. R. Soc. A 481, 20240671 (2025).
  24. Z.-P. Xu, J. Wang, Q. Ye, G. Koßmann, R. Schwonnek, and A. Winter, Simultaneous variances of Pauli strings, weighted independence numbers, and a new kind of perfection of graphs, arXiv:2511.13531.
  25. G. Ziegler, Lectures on Polytopes, Graduate Texts in Mathematics, Vol. 152 (Springer, New York, 1995), p. 254.
  26. E. Chitambar and G. Gour, Quantum resource theories, Rev. Mod. Phys. 91, 025001 (2019).
  27. A. Palhares, S. Zamora, R. A. Macêdo, T. S. Sarubi, J. M. Varela, G. W. C. Rocha, D. A. Moreira, and R. Chaves, A trace distance-based geometric analysis of the stabilizer polytope for few-qubit systems, Phys. Lett. A 576, 131417 (2026).
  28. L. Leone, S. F. E. Oliviero, and A. Hamma, Stabilizer Rényi entropy, Phys. Rev. Lett. 128, 050402 (2022).
  29. M. Howard and E. Campbell, Application of a resource theory for magic states to fault-tolerant quantum computing, Phys. Rev. Lett. 118, 090501 (2017).
  30. H. Pashayan, J. J. Wallman, and S. D. Bartlett, Estimating outcome probabilities of quantum circuits using quasiprobabilities, Phys. Rev. Lett. 115, 070501 (2015).
  31. P. M. Vaidya, Speeding-up linear programming using fast matrix multiplication, in 30th Annual Symposium on Foundations of Computer Science (IEEE Computer Society, Los Alamitos, CA, 1989), pp. 332–337.
  32. J. Sun, L. Cheng, and S.-X. Zhang, Stabilizer ground states for simulating quantum many-body physics: Theory, algorithms, and applications, Quantum 9, 1782 (2025).
  33. To see why it also imposes commutativity, remember that a property of the Pauli group is that a Pauli pair either commutes or anti-commutes. But if there is an anticommuting pair P, P′∈Q, note that PQPQ=−1∈⟨Q⟩. Hence, −1∉⟨Q⟩ ensures that ⟨Q⟩ is abelian.

  34. B. Amaral and M. T. Cunha, On Graph Approaches to Contextuality and Their Role in Quantum Theory (Springer, 2018).
  35. Z. Liu, T. Haug, Q. Ye, Z.-W. Liu, and I. Roth, Triangle criterion: A mixed-state magic criterion with applications in distillation and detection, PRX Quantum 7, 033016 (2026).
  36. In this section, since we are interested in the complexity-theoretic aspects, assume that all continuous-variable inputs are poly(n) sized. This includes the Pauli expectation values in Definition t10 and the stabilizer probabilities in Definition t11. This can all be done by assuming rational inputs.

  37. Y.-K. Liu, Consistency of local density matrices is QMA-complete, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, edited by J. Díaz, K. Jansen, J. D. P. Rolim, and U. Zwick (Springer, Berlin, Heidelberg, 2006), pp. 438–449.
  38. I. Pitowsky, Quantum Probability—quantum Logic (Springer, 1989).
  39. I. Pitowsky, Correlation polytopes: Their geometry and complexity, Math. Program 50, 395 (1991).
  40. C.-Y. Hsieh, G. N. M. Tabia, Y.-C. Yin, and Y.-C. Liang, Resource marginal problems, Quantum 8, 1353 (2024).
  41. S. Arora and B. Barak, Computational Complexity: A Modern Approach (Cambridge University Press, 2009).
  42. N. Hunter-Jones, Unitary designs from statistical mechanics in random quantum circuits, arXiv:1905.12053.
  43. F. G. S. L. Brandão, W. Chemissany, N. Hunter-Jones, R. Kueng, and J. Preskill, Models of quantum complexity growth, PRX Quantum 2, 030316 (2021).
  44. J. S. Cotler, D. K. Mark, H.-Y. Huang, F. Hernández, J. Choi, A. L. Shaw, M. Endres, and S. Choi, Emergent quantum state designs from individual many-body wave functions, PRX Quantum 4, 010311 (2023).
  45. S. F. E. Oliviero, L. Leone, and A. Hamma, Magic-state resource theory for the ground state of the transverse-field ising model, Phys. Rev. A 106, 042426 (2022).
  46. M. Frau, P. S. Tarabunga, M. Collura, M. Dalmonte, and E. Tirrito, Nonstabilizerness versus entanglement in matrix product states, Phys. Rev. B 110, 045101 (2024).
  47. C. Cao, Sudden death of entanglement, rebirth of magic, arXiv:2605.22603.
  48. B. Zeng, X. Chen, D.-L. Zhou, X.-G. Wen et al., Quantum Information Meets Quantum Matter (Springer, 2019).
  49. W. Selke, The ANNNI model—theoretical analysis and experimental application, Phys. Rep. 170, 213 (1988).
  50. D. V. Dmitriev, V. Y. Krivnov, and A. A. Ovchinnikov, Gap generation in the XXZ model in a transverse magnetic field, Phys. Rev. B 65, 172409 (2002).
  51. S. Sachdev, Quantum phase transitions, 2nd ed. (Cambridge University Press, 2011).
  52. A. Bakshi, A. Liu, A. Moitra, and E. Tang, High-temperature gibbs states are unentangled and efficiently preparable, in 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS) (IEEE, 2024), pp. 1027–1036.
  53. S. Aaronson, The learnability of quantum states, Proc. R. Soc. A 463, 3089 (2007).
  54. A. Rocchetto, Stabiliser states are efficiently PAC-learnable, Quantum Info. Comput. 18, 541 (2018).
  55. Z.-P. Xu, Ré Schwonnek, and A. Winter, Bounding the joint numerical range of pauli strings by graph parameters, PRX Quantum 5, 020318 (2024).
  56. D. Gottesman, Surviving as a quantum computer in a classical world, Textbook Manuscript Preprint 8, 8 (2024).
  57. D. B. Shapiro, Compositions of Quadratic Forms (Walter de Gruyter, 2011), Vol. 33.
  58. A. A. Mele, Introduction to Haar measure tools in quantum information: A beginner’s tutorial, Quantum 8, 1340 (2024).
  59. Cf. Eq. (19).

  60. J. W. Moon and L. Moser, On cliques in graphs, Isr. J. Math 3, 23 (1965).
  61. S. Tsukiyama, M. Ide, H. Ariyoshi, and I. Shirakawa, A new algorithm for generating all the maximal independent sets, SIAM J. Comput. 6, 505 (1977).
  62. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to algorithms, 4th ed. (MIT press, 2022).
  63. On all runtime evaluations, we have used that D≤|S|.

  64. H. J. García, I. L. Markov, and A. W. Cross, On the geometry of stabilizer states, arXiv:1711.07848.
  65. R. Wagner, F. C. R. Peres, E. Zambrini Cruzeiro, and E. F. Galvão, Unitary-invariant method for witnessing nonstabilizerness in quantum processors, J. Phys. A 58, 285302 (2025).
  66. J. Dehaene and B. De Moor, Clifford group, stabilizer states, and linear and quadratic operations over GF(2), Phys. Rev. A 68, 042318 (2003).
  67. K. Markov, I. Patel, and J. Hayes, Optimal synthesis of linear reversible circuits, Quantum Inf. Comput. 8, 0282 (2008).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation