Orthogonalised Self-Guided Quantum Tomography: Insights from Single-Pixel Imaging
Pith reviewed 2026-05-10 17:11 UTC · model grok-4.3
The pith
Orthogonalised self-guided quantum tomography achieves higher fidelity by importing an orthogonalisation step from single-pixel imaging.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Self-guided imaging is mathematically equivalent to single-pixel imaging, which permits the orthogonalisation step from orthogonalised ghost imaging to be applied directly to self-guided quantum tomography, improving reconstruction accuracy with no added experimental overhead.
What carries the argument
Orthogonalised SGQT, formed by adding an orthogonalisation step to the measurement patterns of SGQT, adapted from the orthogonalisation used in single-pixel imaging to decorrelate successive measurements.
If this is right
- Numerical fidelity rises from 95.2 percent to 99.17 percent.
- Experimental fidelity rises from 92.1 percent to 95.3 percent.
- The procedure adds no extra experimental overhead.
- Routines from single-pixel imaging and self-guided quantum tomography can be interchanged to further optimise measurements.
Where Pith is reading between the lines
- The same mapping might allow other single-pixel imaging advances, such as compressed sensing patterns, to be imported into quantum tomography.
- If the equivalence extends to noisy or incomplete data regimes, orthogonalised SGQT could reduce the total number of measurements needed for a target fidelity.
- Testing the method on higher-dimensional states or different quantum platforms would clarify how widely the performance gain holds.
Load-bearing premise
The mathematical equivalence between self-guided imaging and single-pixel imaging permits the orthogonalisation procedure to transfer directly without introducing quantum-specific errors or hidden overhead.
What would settle it
An experiment in which orthogonalised SGQT either fails to raise fidelity above standard SGQT or requires additional measurements beyond the standard protocol.
Figures
read the original abstract
We introduce the concept of self-guided imaging (SGI) as a linear analogue of self-guided quantum tomography (SGQT). We show that SGI is mathematically equivalent to single-pixel imaging (SPI). Taking inspiration from orthogonalised ghost imaging, a recent advance in SPI, we introduce orthogonalised SGQT. This requires no additional experimental overhead and leads to faster and more accurate final convergence, as we demonstrate numerically (fidelity $95.2\% \rightarrow 99.17\%$) and experimentally (fidelity $92.1\% \rightarrow 95.3\%$). This work suggests that further routines from SPI and SGQT can be interchanged to optimise measurements and convergence.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper claims to introduce self-guided imaging (SGI) as a linear analogue of self-guided quantum tomography (SGQT), establish its mathematical equivalence to single-pixel imaging (SPI), and adapt orthogonalisation from ghost imaging to create orthogonalised SGQT. This new approach is said to require no additional experimental overhead while providing faster and more accurate convergence, as shown by numerical fidelity improvements from 95.2% to 99.17% and experimental improvements from 92.1% to 95.3%. It concludes by suggesting that further routines from SPI and SGQT can be interchanged for optimization.
Significance. This result, if the equivalence and transfer are rigorously established, is significant because it creates a direct link between quantum tomography techniques and classical single-pixel imaging methods. The explicit numerical and experimental demonstrations of fidelity gains without added overhead are particular strengths, offering reproducible evidence for the practical utility. Such cross-fertilization could accelerate the development of efficient quantum measurement protocols by leveraging mature techniques from the classical imaging community.
minor comments (3)
- The reported fidelity values lack accompanying statistical details such as standard deviations or the number of experimental trials, making it harder to gauge the robustness of the claimed improvements.
- A more detailed explanation of how the orthogonalisation procedure is implemented in the quantum setting, including any potential differences from the classical SPI case, would enhance clarity even if the equivalence is exact.
- The manuscript would benefit from a figure or table illustrating the convergence speed comparison between SGQT and orthogonalised SGQT to visually support the 'faster convergence' claim.
Simulated Author's Rebuttal
We thank the referee for their positive assessment of our manuscript, accurate summary of the contributions, and recommendation for minor revision. We are pleased that the significance of linking SGQT with classical SPI techniques is recognized.
Circularity Check
No significant circularity detected
full rationale
The paper defines SGI as a linear analogue of SGQT, establishes its mathematical equivalence to SPI via direct proof, and transfers the orthogonalisation routine from prior orthogonalised ghost imaging work in SPI. Central performance claims (fidelity gains from 95.2% to 99.17% numerically and 92.1% to 95.3% experimentally) are supported by independent simulation and lab measurements rather than any derivation by construction. No self-definitional loops, fitted inputs presented as predictions, load-bearing self-citations reducing the result to unverified inputs, or ansatz smuggling appear in the derivation chain. The equivalence and transfer are self-contained with external empirical validation.
Axiom & Free-Parameter Ledger
Reference graph
Works this paper leans on
-
[1]
V. Gebhart, R. Santagati, A. A. Gentile, E. M. Gauger, D. Craig, N. Ares, L. Banchi, F. Marquardt, L. Pezzè, and C. Bonato, Learning quantum systems, Nature Re- views Physics5, 141 (2023)
work page 2023
-
[3]
G. Mauro D’Ariano, M. G. Paris, and M. F. Sacchi, Quantum Tomography, inAdvances in Imaging and Elec- tron Physics, Vol. 128 (Elsevier, 2003) pp. 205–308
work page 2003
- [6]
-
[8]
R. T. Thew, K. Nemoto, A. G. White, and W. J. Munro, Quditquantum-statetomography,PhysicalReviewA66, 012303 (2002)
work page 2002
-
[9]
R. J. Chapman, C. Ferrie, and A. Peruzzo, Experimental demonstrationofself-guidedquantumtomography,Phys. Rev. Lett.117, 040402 (2016)
work page 2016
-
[10]
M. Rambach, M. Qaryan, M. Kewming, C. Ferrie, A. G. White, and J. Romero, Robust and efficient high- dimensional quantum state tomography, Phys. Rev. Lett. 126, 100402 (2021)
work page 2021
- [11]
- [12]
-
[13]
M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, Single-pixel imaging via compressive sampling, IEEE Signal Process- ing Magazine25, 83 (2008)
work page 2008
-
[14]
A. Kallepalli, L. Viani, D. Stellinga, E. Rotunno, R. Bow- man, G. M. Gibson, M.-J. Sun, P. Rosi, S. Frabboni, R. Balboni, A. Migliori, V. Grillo, and M. J. Padgett, Challenging Point Scanning across Electron Microscopy and Optical Imaging using Computational Imaging, In- telligent Computing2022, 0001 (2022)
work page 2022
-
[15]
C. Popa and R. Zdunek, Kaczmarz extended algorithm for tomographic image reconstruction from limited-data, Mathematics and Computers in Simulation65, 579 (2004)
work page 2004
-
[16]
T. Strohmer and R. Vershynin, A randomized kacz- marz algorithm with exponential convergence, Journal of Fourier Analysis and Applications15, 262 (2009)
work page 2009
-
[17]
R. G. Pires, D. R. Pereira, L. A. Pereira, A. F. Mansano, and J. P. Papa, Projections onto convex sets parameter estimation through harmony search and its application forimagerestoration,NaturalComputing15,493(2016)
work page 2016
- [18]
-
[19]
J. C. Spall, Multivariate stochastic-approximation us- ing a simultaneous perturbation gradient approximation, Ieee Transactions on Automatic Control37, 332 (1992)
work page 1992
- [20]
-
[21]
T. B. Pittman, Y. H. Shih, D. V. Strekalov, and A. V. Sergienko, Optical imaging by means of two-photon quantum entanglement, Physical Review A52, R3429 (1995)
work page 1995
-
[22]
R. S. Bennink, S. J. Bentley, and R. W. Boyd, “two- photon” coincidence imaging with a classical source, Phys. Rev. Lett.89, 113601 (2002)
work page 2002
-
[23]
B. I. Erkmen and J. H. Shapiro, Ghost imaging: from quantum to classical to computational, Adv. Opt. Pho- ton.2, 405 (2010)
work page 2010
-
[24]
A. M. Yao and M. J. Padgett, Orbital angular momen- tum: origins, behavior and applications, Advances in Op- tics and Photonics3, 161 (2011)
work page 2011
- [25]
-
[26]
A. Mair, A. Vaziri, G. Weihs, and A. Zeilinger, Entangle- ment of the orbital angular momentum states of photons, Nature412, 313 (2001). Supplementary Information for Orthogonalised Self-Guided Quantum Tomography: Insights from Single-Pixel Imaging Kiki Dekkers1∗, Alice Ruget1∗, Fazilah Nothlawala2, Sabrina Henry1, Stirling Scholes1, Miles Padgett3, Andre...
work page 2001
-
[27]
However, this gives us a skewed metric with nonphysical values
estimate the fidelity between|σk⟩and|ψ⟩based the detected counts. However, this gives us a skewed metric with nonphysical values. After a certainkour recorded coincidences surpass the counts we obtain for|σ⟩=|ψ⟩as can ben seen in Fig. 5, which would represent nonphys- ical fidelities that are larger than 100%. This is easily explained by the fact that SGQ...
-
[28]
G. Mauro D’Ariano, M. G. Paris, and M. F. Sacchi, Quan- tum Tomography, inAdvances in Imaging and Electron Physics, Vol. 128 (Elsevier, 2003) pp. 205–308
work page 2003
-
[29]
M.; Diamanti, E.; Kerenidis, I
E. Bolduc, G. C. Knee, E. M. Gauger, and J. Leach, Pro- jected gradient descent algorithms for quantum state to- mography, Npj Quantum Information3, 10.1038/s41534- 017-0043-1 (2017)
-
[30]
D. F. V. James, P. G. Kwiat, W. J. Munro, and A. G. White, Measurement of qubits, Phys. Rev. A64, 052312 (2001)
work page 2001
- [31]
-
[32]
J. C. Spall, Multivariate stochastic-approximation using a simultaneous perturbation gradient approximation, Ieee Transactions on Automatic Control37, 332 (1992)
work page 1992
-
[33]
Ferrie, Self-guided quantum tomography, Phys
C. Ferrie, Self-guided quantum tomography, Phys. Rev. Lett.113, 190404 (2014)
work page 2014
-
[34]
G. M. Gibson, S. D. Johnson, and M. J. Padgett, Single- pixel imaging 12 years on: a review, Opt. Express28, 28190 (2020)
work page 2020
-
[35]
M. Rambach, M. Qaryan, M. Kewming, C. Ferrie, A. G. White, and J. Romero, Robust and Efficient High- Dimensional Quantum State Tomography, Physical Re- view Letters126, 100402 (2021)
work page 2021
-
[36]
V. Srivastav, N. H. Valencia, S. Leedumrongwatthanakun, W. McCutcheon, and M. Malik, Characterizing and Tailoring Spatial Correlations in Multimode Parametric Down-Conversion, Physical Review Applied18, 054006 (2022). 6 100 101 102 Iteration k 1 2 3 4 5 6Coincidences ×103 SGQT, Eq. (1) OSGQT, Eq. (6) Threshold |σ⟩ = |ψ⟩ FIG. 5. Comparison of recorded coun...
work page 2022
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.