pith. machine review for the scientific record. sign in

arxiv: 2604.26927 · v1 · submitted 2026-04-29 · 🪐 quant-ph

Recognition: unknown

The most discriminable quantum states in the multicopy regime

Authors on Pith no claims yet

Pith reviewed 2026-05-07 09:58 UTC · model grok-4.3

classification 🪐 quant-ph
keywords quantum state discriminationk-designsmulticopy regimeminimum-error discriminationquantum advantageBayes capacity
0
0 comments X

The pith

For large enough N, state k-designs exactly maximize success probability in multi-copy pure-state discrimination.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper determines which ensembles of quantum states allow the highest probability of correctly guessing an unknown state when multiple identical copies are provided. For pure states, it proves that k-designs are exactly optimal whenever N is large enough to support one. This matters because multi-copy discrimination underpins tasks like quantum sensing, communication, and computation where one must distinguish states with limited resources. The work further shows mixed states can beat pure ones for larger N, solves the classical version via a link to channel capacity, and establishes a quadratic quantum advantage in copy number.

Core claim

For uniformly distributed ensembles of the form {1/N, ρ_i^{⊗k}}, the sets of pure states that maximize minimum-error discrimination success are precisely the state k-designs, provided N is large enough to support such a design. When N exceeds the design size, mixed states can outperform every pure-state ensemble. The classical analogue corresponds to maximizing the multiplicative Bayes capacity of k independent uses of a classical channel and is solved exactly for N at least the binomial coefficient binom(d+k-1,k).

What carries the argument

State k-designs, ensembles of pure states whose k-th moments equal the Haar-averaged moments, which the paper shows coincide with the optimal pure-state ensembles for the discrimination task.

If this is right

  • Mixed states achieve strictly higher discrimination success than any pure-state ensemble once N exceeds the k-design threshold.
  • Quantum systems require quadratically fewer copies than classical probability distributions to reach the same maximal success probability.
  • Restricting to real quantum states strongly reduces the quadratic advantage over the classical case.
  • Computational methods yield both candidate optimal ensembles and rigorous upper bounds on success probability in analytically intractable regimes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • These optimal ensembles could be used to construct efficient quantum protocols for identifying or verifying states with minimal copies.
  • The classical-quantum comparison may inform bounds on information leakage when multiple uses of a channel are available.
  • The transition point where mixed states overtake pure ones could be mapped numerically for small d to guide practical implementations.

Load-bearing premise

The ensemble is uniformly distributed and k-designs exist with the symmetries required for the given dimension d and copy number k.

What would settle it

For d=2 and k=2 with N=6 (where a 2-design exists), compute the exact minimum-error success probability for the design ensemble versus any other pure-state ensemble of size 6; if any non-design set yields strictly higher success, the optimality claim fails.

Figures

Figures reproduced from arXiv: 2604.26927 by Lucas B. Vieira, Lucas E. A. Porto, Marco T\'ulio Quintino, Maria Kvashchuk, Polina Chernyshova, Ties-A. Ohst.

Figure 1
Figure 1. Figure 1: Pictorial illustration of the k-copy discrimination scenario considered in this work, where dashed wires represent classical systems and single wires represent quantum systems. With uniform probability, the state ρ ⊗k i ∈ L(C d ) ⊗k is pre￾pared. In order to discriminate among a set of N states, one may perform a joint measurement described by a POVM {Mj}j , where Mj ∈ L(C d ) ⊗k . The discrimination task … view at source ↗
Figure 2
Figure 2. Figure 2: An equilateral triangle in the XZ plane of the Bloch view at source ↗
Figure 3
Figure 3. Figure 3: Tetrahedron view at source ↗
Figure 4
Figure 4. Figure 4: Triangle and fully mixed state. 6.4 Asymptotic analysis of Ω q (d, N, k) As discussed in the previous section, for pure states we have Ω q pure(d, N ≥ N ′ , k) ≤ 1 N (k + d − 1)d−1 (d − 1)! . (59) From analogous arguments, the upper bound on Ω q (d, N, k) ensures that Ω q (d, N ≥ N ′ , k) ≤ 1 N (k + d 2 − 1)d 2−1 (d 2 − 1)! , (60) from which we conclude that Ω q (d, N ≥ N ′ , k) = O view at source ↗
Figure 9
Figure 9. Figure 9: (d, N, k) = (2, 6, 3) (a) Ω q pure(2, 7, 3) = 0.5714 (b) Ωq (2, 7, 3) = 0.6429 view at source ↗
Figure 10
Figure 10. Figure 10: (d, N, k) = (2, 7, 3) 11 The relationship between k-copy discrimination and state k-designs In this section we summarise the results obtained in this work that connect the problem of finding the most discriminable k-copy states with the concept of state k-designs. 11.1 Pure quantum states When focusing on pure quantum states (see Section 5), the relationship between the problem of finding the most discrim… view at source ↗
Figure 7
Figure 7. Figure 7: (d, N, k) = (2, 5, 2) (a) Ω q pure(2, 5, 3) = 0.7901 (b) Ωq (2, 5, 3) = 0.8771 view at source ↗
Figure 8
Figure 8. Figure 8: (d, N, k) = (2, 5, 3) (a) Ω q pure(2, 6, 3) = 0.5 (b) Ωq (2, 6, 3) = 0.7418 view at source ↗
read the original abstract

This work investigates which sets of quantum states give rise to the highest achievable success probability in minimum-error state discrimination if multiple copies of the unknown state are given. Specifically, we consider uniformly distributed ensembles of the form $\left\{\frac{1}{N},\rho_i^{\otimes k}\right\}_{i=1}^N$, where $N$ states in dimension $d$ are provided in $k$ identical copies, and derive universal limits in this scenario. For pure state ensembles, we prove that whenever $N$ is large enough to support a state $k$-design, these designs will exactly give rise to the maximally discriminable sets. We further show that when $N$ exceeds the size required for a $k$-design, mixed states can outperform all pure state ensembles. We also analyse the analogue classical discrimination problems, in which states are replaced by probability distributions. We recognise that the problem of most discriminable classical states in the multi-copy regime is in one-to-one correspondence to the concept of the multiplicative Bayes capacity of independent uses of classical channels, a concept that emerges naturally in the context of classical information leakage. This connection allows us to completely solve the classical analogue of our problem when $N\geq \binom{d + k - 1}{k}$, and to prove that quantum systems offer a quadratic advantage (in number of copies $k$) over classical ones. Curiously, we also show that this quantum advantage is strongly reduced when one is restricted to real quantum states. Finally, we introduce computational techniques to find sets of most discriminable ensembles, and to obtain rigorous universal upper bounds on the maximal success probability for multi-copy state discrimination in cases that are analytically intractable.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript investigates which uniform ensembles of the form {1/N, ρ_i^{⊗k}} maximize the minimum-error discrimination success probability in the multicopy regime. For pure-state ensembles it proves that k-designs achieve the maximal success probability precisely when N is large enough to support such a design. It further shows that mixed states can outperform all pure-state ensembles for larger N, completely solves the classical analogue (corresponding to multiplicative Bayes capacity) for N ≥ binom(d+k-1,k), establishes a quadratic quantum advantage over classical systems (reduced when restricted to real states), and supplies computational techniques for rigorous universal upper bounds in analytically intractable cases.

Significance. If the central claims hold, the work supplies an explicit, design-based characterization of optimal multicopy discriminability together with a matching universal upper bound, a complete solution of the classical counterpart, and a concrete quadratic separation between quantum and classical performance. The link to information leakage and the provision of computational methods for bounds are additional strengths that increase the result's utility for both theoretical and practical quantum information tasks.

minor comments (2)
  1. The abstract states that k-designs 'exactly give rise to the maximally discriminable sets' when N is large enough; a brief pointer in the introduction to the theorem or equation that shows saturation of the universal bound would improve immediate readability.
  2. The classical-quantum comparison and the reduction for real states are interesting but would benefit from a short clarifying sentence on whether the quadratic advantage is in the number of copies k or in the scaling of the success probability itself.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript, the accurate summary of our results, and the recommendation for minor revision. We appreciate the recognition of the design-based characterization, the classical solution, the quadratic quantum advantage, and the computational techniques. Since the report does not list any specific major comments requiring rebuttal or clarification, we provide no point-by-point responses below and confirm that the referee's summary correctly captures the paper's contributions.

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained

full rationale

The central result derives a universal upper bound on minimum-error discrimination success probability for uniform k-copy ensembles and shows saturation exactly when the ensemble's first k moments match those of a pure-state k-design. This is a direct mathematical argument from standard quantum mechanics (trace distance, Helstrom bound, moment conditions) without reducing any prediction to a fitted parameter, self-definition, or load-bearing self-citation. The classical analogue is identified with the known multiplicative Bayes capacity of channels, an external correspondence that does not create circularity. No step matches any enumerated circularity pattern; the proof is independent of the paper's own prior results.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work relies on established quantum mechanics and information theory without new free parameters or invented entities.

axioms (2)
  • domain assumption The ensemble is uniformly distributed over the N states
    This defines the minimum-error discrimination setup considered throughout.
  • standard math Standard quantum mechanical postulates for states, measurements, and success probability
    Used to formalize the discrimination task and derive bounds.

pith-pipeline@v0.9.0 · 5629 in / 1185 out tokens · 81751 ms · 2026-05-07T09:58:03.684083+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

71 extracted references · 37 canonical work pages · 2 internal anchors

  1. [1]

    S. M. Barnett and S. Croke, Quantum state dis- crimination, Advances in Optics and Photonics 1, 238 (2009), arXiv:0810.1970 [quant-ph]

  2. [2]

    Bae and L.-C

    J. Bae and L.-C. Kwek, Quantum state discrim- ination and its applications, Journal of Phys- ics A Mathematical General48, 083001 (2015), arXiv:1707.02571 [quant-ph]

  3. [3]

    C. H. Bennett and G. Brassard, Quantum crypto- graphy: Public key distribution and coin tossing, arXiv e-prints (2020), arXiv:2003.06557 [quant- ph]

  4. [4]

    Wolf,Quantum Key Distribution: An Intro- duction with Exercises(Springer Cham, 2021)

    R. Wolf,Quantum Key Distribution: An Intro- duction with Exercises(Springer Cham, 2021)

  5. [5]

    Chefles, A

    A. Chefles, A. Kitagawa, M. Takeoka, M. Sa- saki, and J. Twamley, Unambiguous discrimina- tion among oracle operators, Journal of Physics A Mathematical General40, 10183–10213 (2007), arXiv:quant-ph/0702245 [quant-ph]

  6. [6]

    Aaronson, Quantum Copy-Protection and Quantum Money, arXiv e-prints (2011), arXiv:1110.5353 [quant-ph]

    S. Aaronson, Quantum Copy-Protection and Quantum Money, arXiv e-prints (2011), arXiv:1110.5353 [quant-ph]

  7. [7]

    Molina, T

    A. Molina, T. Vidick, and J. Watrous, Op- timal counterfeiting attacks and generalizations for Wiesner’s quantum money, arXiv e-prints (2012), arXiv:1202.4010 [quant-ph]

  8. [8]

    Hayashi, Quantum hypothesis testing and discrimination of quantum states, inQuantum Information: An Introduction(Springer Berlin Heidelberg, Berlin, Heidelberg, 2006) pp

    M. Hayashi, Quantum hypothesis testing and discrimination of quantum states, inQuantum Information: An Introduction(Springer Berlin Heidelberg, Berlin, Heidelberg, 2006) pp. 69–91

  9. [9]

    Brunner, M

    N. Brunner, M. Navascués, and T. Vértesi, Di- mension Witnesses and Quantum State Discrim- ination, Phys. Rev. Lett.110, 150501 (2013), arXiv:1209.5643 [quant-ph]

  10. [10]

    C. W. Helstrom, Quantum detection and estima- tion theory, Journal of statistical physics1, 231– 252 (1969)

  11. [11]

    J. Zhou, S. Chessa, E. Chitambar, and F. Led- itzky, On the distinguishability of geometric- ally uniform quantum states, Journal of Phys- ics A Mathematical General58, 415303 (2025), arXiv:2501.12376 [quant-ph]

  12. [12]

    Watrous,The Theory of Quantum Informa- tion(Cambridge University Press, 2018)

    J. Watrous,The Theory of Quantum Informa- tion(Cambridge University Press, 2018)

  13. [13]

    Skrzypczyk and D

    P. Skrzypczyk and D. Cavalcanti,Semidefinite Programming in Quantum Information Science, IOP Series in Quantum Technology (Institute of Physics Publishing, 2023)

  14. [14]

    Elron and Y

    N. Elron and Y. C. Eldar, Optimal encod- ing of classical information in a quantum me- dium, IEEE Transactions on Information Theory 53, 1900–1907 (2007), arXiv:quant-ph/0601010 [quant-ph]

  15. [15]

    Heinosaari, L

    T. Heinosaari, L. Leppäjärvi, and M. Plávala, Encoding and decoding of information in gen- eral probabilistic theories, International Journal of Quantum Information22, 2440007 (2024), arXiv:2311.17522 [quant-ph]

  16. [16]

    Anshu and S

    A. Anshu and S. Arunachalam, A survey on the complexity of learning quantum states, Nature Reviews Physics6, 59–69 (2024), arXiv:2305.20069 [quant-ph]

  17. [17]

    O’Donnell and J

    R. O’Donnell and J. Wright, Efficient quantum tomography, arXiv e-prints (2015), arXiv:1508.01907 [quant-ph]

  18. [18]

    Tóth and I

    G. Tóth and I. Apellaniz, Quantum metrology from a quantum information science perspective, Journal of Physics A Mathematical General47, 424006 (2014), arXiv:1405.4878 [quant-ph]

  19. [19]

    M. S. Alvim, K. Chatzikokolakis, A. McIver, C. Morgan, C. Palamidessi, and G. Smith, The Science of Quantitative Information Flow (Springer, 2020)

  20. [20]

    M. S. Alvim, K. Chatzikokolakis, C. Palamidessi, and G. Smith, Measuring information leakage using generalized gain functions, in2012 IEEE 25th Computer Security Foundations Symposium (2012) pp. 265–279

  21. [21]

    M. S. Alvim, K. Chatzikokolakis, A. Mciver, C. Morgan, C. Palamidessi, and G. Smith, Ad- ditive and multiplicative notions of leakage, and their capacities, in2014 IEEE 27th Computer Se- curity Foundations Symposium(2014) pp. 308– 322

  22. [22]

    Espinoza and G

    B. Espinoza and G. Smith, Min-entropy as a re- source, Information and Computation226, 57–75 (2013), special Issue: Information Security as a Resource. 22

  23. [23]

    D. M. Smith and G. Smith, Tight bounds on information leakage from repeated independent runs, in2017 IEEE 30th Computer Security Foundations Symposium (CSF)(2017) pp. 318– 327

  24. [24]

    Köpf and G

    B. Köpf and G. Smith, Vulnerability bounds and leakage resilience of blinded cryptography under timing attacks, in2010 23rd IEEE Computer Se- curity Foundations Symposium(2010) pp. 44–56

  25. [25]

    Boreale, F

    M. Boreale, F. Pampaloni, and M. Paolini, Asymptoticinformation leakage underone-tryat- tacks, inFoundations of Software Science and Computational Structures, edited by M. Hofmann (Springer Berlin Heidelberg, Berlin, Heidelberg,

  26. [26]

    Boreale, F

    M. Boreale, F. Pampaloni, and M. Paolini, Quantitative information flow, with a view, in Computer Security – ESORICS 2011, edited by V. Atluri and C. Diaz (Springer Berlin Heidel- berg, Berlin, Heidelberg, 2011) pp. 588–606

  27. [27]

    Braun, K

    C. Braun, K. Chatzikokolakis, and C. Palam- idessi, Quantitative notions of leakage for one- try attacks, Electronic Notes in Theoretical Com- puter Science249, 75–91 (2009), proceedings of the 25th Conference on Mathematical Founda- tions of Programming Semantics (MFPS 2009)

  28. [28]

    A. W. Harrow, The Church of the Symmet- ric Subspace, arXiv e-prints , arXiv:1308.6595 (2013), arXiv:1308.6595 [quant-ph]

  29. [29]

    A. A. Mele, Introduction to Haar Measure Tools in Quantum Information: A Beginner’s Tutorial, Quantum8, 1340 (2024), arXiv:2307.08956 [quant-ph]

  30. [30]

    A.AmbainisandJ.Emerson,Quantumt-designs: t-wise independence in the quantum world, arXiv e-prints , quant-ph/0701126 (2007), arXiv:quant- ph/0701126 [quant-ph]

  31. [31]

    Hayashi, T

    A. Hayashi, T. Hashimoto, and M. Horibe, Reex- amination of optimal quantum state estimation of pure states, Phys. Rev. A72, 032325 (2005), arXiv:quant-ph/0410207 [quant-ph]

  32. [32]

    Seymour and T

    P. Seymour and T. Zaslavsky, Averaging sets: A generalization of mean values and spherical designs, Advances in Mathematics52, 213–240 (1984)

  33. [33]

    Bondarenko, D

    A. Bondarenko, D. Radchenko, and M. Viazovska, Optimal asymptotic bounds for spherical designs, Annals of mathematics , 443–452 (2013), arXiv:1009.4407 [math.MG]

  34. [34]

    R. H. Hardin and N. J. A. Sloane, Spherical designs,http://neilsloane.com/sphdesigns/, accessed: 2026-04-29

  35. [35]

    L. Cui, T. Schuster, F. Brandao, and H.-Y. Huang, Unitary designs in nearly optimal depth, arXiv e-prints (2025), arXiv:2507.06216 [quant- ph]

  36. [36]

    Nemoz, R

    T. Nemoz, R. Alléaume, and P. Brown, Exact dis- tinguishability between real-valued and complex- valued Haar random quantum states, arXiv e- prints (2025), arXiv:2507.16939 [quant-ph]

  37. [37]

    Arora and B

    S. Arora and B. Barak,Computational Complex- ity: A Modern Approach(Cambridge University Press, 2009)

  38. [38]

    Bavaresco, M

    J. Bavaresco, M. Murao, and M. T. Quintino, Unitary channel discrimination beyond group structures: Advantages of sequential and indefinite-causal-order strategies, Journal of Mathematical Physics63, 042203 (2022), arXiv:2105.13369 [quant-ph]

  39. [39]

    A ‘Pretty Good’ Measurement for Distinguishing Quantum States

    P. Hausladen and W. K. Wootters, A ‘pretty good’ measurement for distinguish- ing quantum states, Journal of Mod- ern Optics41, 2385–2390 (1994), ht- tps://doi.org/10.1080/09500349414552221

  40. [40]

    T. M. Cover and J. A. Thomas,Elements of Information Theory (Wiley Series in Tele- communications and Signal Processing)(Wiley- Interscience, USA, 2006)

  41. [41]

    L. B. Vieira, S. Milz, G. Vitagliano, and C. Budroni, Witnessing environment dimension through temporal correlations, Quantum8, 1224 (2024), arXiv:2305.19175 [quant-ph]

  42. [42]

    OEIS Foundation Inc., Sequence A063170 (2026), the On-Line Encyclopedia of Integer Sequences, published electronically athttps://oeis.org/ A063170

  43. [43]

    Prodinger, An identity conjectured by La- casse via the tree function, arXiv e-prints (2013), arXiv:1301.3669 [math.CO]

    H. Prodinger, An identity conjectured by La- casse via the tree function, arXiv e-prints (2013), arXiv:1301.3669 [math.CO]

  44. [44]

    Flajolet, P

    P. Flajolet, P. J. Grabner, P. Kirschenhofer, and H. Prodinger, On ramanujan’s q-function, Journal of Computational and Applied Mathem- atics58, 103–116 (1995)

  45. [45]

    P. E. Frenkel and M. Weiner, Classical Inform- ation Storage in an n-Level Quantum System, Communications in Mathematical Physics340, 563–574 (2015), arXiv:1304.5723 [cs.IT]

  46. [46]

    Heinosaari and M

    T. Heinosaari and M. Hillery, Can a qudit carry more information than a dit?, Contempor- ary Physics65, 2–11 (2024), arXiv:2406.16566 [quant-ph]

  47. [47]

    Jacquet and W

    P. Jacquet and W. Szpankowski, Entropy com- putations via analytic depoissonization, IEEE Transactions on Information Theory45, 1072– 1081 (1999)

  48. [48]

    How to evaluate n∑ k=0 √(n k )

    robjohn, Answer to "How to evaluate n∑ k=0 √(n k ) ", Mathematics Stack Exchange, version 2014-07- 15

  49. [49]

    Boyd and L

    S. Boyd and L. Vandenberghe,Convex Optimiz- ation(Cambridge University Press, 2004)

  50. [50]

    A hybrid quantum-classical algorithm for Bayes-optimal quantum state discrimination using the source code

    A. Mohan, J. Sikora, and S. Upadhyay, A gener- alized framework for quantum state discrimina- tion, hybrid algorithms, and the quantum change 23 point problem, arXiv e-prints , arXiv:2312.04023 (2023), arXiv:2312.04023 [quant-ph]

  51. [51]

    D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv e-prints , arXiv:1412.6980v9 (2017), arXiv:1412.6980 [cs.LG]

  52. [52]

    ApS,MOSEK Solver(2025)

    M. ApS,MOSEK Solver(2025)

  53. [53]

    Kvashchuk, GitHub repository: The most dis- criminable quantum states in the multicopy re- gime (2026)

    M. Kvashchuk, GitHub repository: The most dis- criminable quantum states in the multicopy re- gime (2026)

  54. [54]

    A. C. Doherty, P. A. Parrilo, and F. M. Sped- alieri, Complete family of separability criteria, Phys. Rev. A69, 022308 (2004)

  55. [55]

    C. M. Caves, C. A. Fuchs, and R. Schack, Un- known quantum states: The quantum de Finetti representation, Journal of Mathematical Physics 43, 4537–4559 (2002)

  56. [56]

    Peres, Separability criterion for density matrices, Phys

    A. Peres, Separability criterion for density matrices, Phys. Rev. Lett.77, 1413–1415 (1996)

  57. [57]

    Diamond and S

    S. Diamond and S. Boyd, CVXPY: A Python- embedded modeling language for convex optim- ization, Journal of Machine Learning Research 17, 1–5 (2016)

  58. [58]

    Agrawal, R

    A. Agrawal, R. Verschueren, S. Diamond, and S. Boyd, A rewriting system for convex optimiz- ation problems, Journal of Control and Decision 5, 42–60 (2018)

  59. [59]

    O’Donoghue, Operator splitting for a homo- geneous embedding of the linear complementar- ity problem, SIAM Journal on Optimization31, 1999–2023 (2021)

    B. O’Donoghue, Operator splitting for a homo- geneous embedding of the linear complementar- ity problem, SIAM Journal on Optimization31, 1999–2023 (2021)

  60. [60]

    L. B. Vieira, GitHub repository: The most dis- criminable quantum states in the multicopy re- gime (2026)

  61. [61]

    An and X

    C. An and X. Zhuang, A Survey on Spher- ical Designs: Existence, Numerical Construc- tions, and Applications, arXiv e-prints (2026), arXiv:2601.11963 [cs.NA]

  62. [62]

    Plávala, General probabilistic theories: An introduction, Physics Reports1033, 1–64 (2023), arXiv:2103.07469 [quant-ph]

    M. Plávala, General probabilistic theories: An introduction, Physics Reports1033, 1–64 (2023), arXiv:2103.07469 [quant-ph]

  63. [63]

    Barrett, Information processing in generalized probabilistic theories, arXiv e-prints , quant- ph/0508211 (2005), arXiv:quant-ph/0508211 [quant-ph]

    J. Barrett, Information processing in generalized probabilistic theories, arXiv e-prints , quant- ph/0508211 (2005), arXiv:quant-ph/0508211 [quant-ph]

  64. [64]

    L. J. Dmello, L. T. Ligthart, and D. Gross, En- tanglement swapping in generalized probabilistic theories and iterated Clauser-Horne-Shimony- Holt games, Phys. Rev. A110, 022225 (2024), arXiv:2405.13819 [quant-ph]

  65. [65]

    Skrzypczyk, N

    P. Skrzypczyk, N. Brunner, and S. Popescu, Emergence of Quantum Correlations from Non- locality Swapping, Phys. Rev. Lett.102, 110402 (2009), arXiv:0811.2937 [quant-ph]

  66. [66]

    Short, S

    T. Short, S. Popescu, and N. Gisin, Entangle- ment swapping for generalized non-local correla- tions, arXiv e-prints , quant-ph/0508120 (2005), arXiv:quant-ph/0508120 [quant-ph]

  67. [67]

    L. J. Dmello and D. Gross, Probabilistic the- ories stable under teleportation, arXiv e-prints , arXiv:2603.21347 (2026), arXiv:2603.21347 [quant-ph]

  68. [68]

    Matsumoto and G

    K. Matsumoto and G. Kimura, Information stor- ing yields a point-asymmetry of state space in general probabilistic theories, arXiv e-prints , arXiv:1802.01162 (2018), arXiv:1802.01162 [quant-ph]

  69. [69]

    Bavaresco, M

    J. Bavaresco, M. T. Quintino, L. Guerini, T. O. Maciel, D. Cavalcanti, and M. T. Cunha, Most incompatible measurements for robust steer- ing tests, Phys. Rev. A96, 022110 (2017), arXiv:1704.02994 [quant-ph]

  70. [70]

    Achenbach, L

    T. Achenbach, L. Leppäjärvi, L. Hanwool, and T. Heinosaari, Nonclassical traits in multi-copy state discrimination (2026), arXiv:2604.xxxx [quant-ph]

  71. [71]

    left” and “right

    P. Delsarte, J. Goethals, and J. Seidel, Spherical codes and designs, inGeometry and Combinat- orics, edited by D. Corneil and R. Mathon (Aca- demic Press, 1991) pp. 68–93. 24 Appendix A Summary of results for two-dimensional systems The table below presents the values ofΩ(d= 2,N,k), including analytical and numerical calculations. N Ω cl Ω real pure Ω q ...