Robust GHZ State Preparation via Majority-Voted Boundary Measurements
Pith reviewed 2026-05-15 21:06 UTC · model grok-4.3
The pith
Group-Majority-Voting prepares high-fidelity GHZ states by fusing parallel local states with majority-voted boundary measurements.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Group-MV partitions arbitrary coupling graphs, prepares local GHZ states in parallel, and fuses them via majority-voted mid-circuit measurements. The majority vote over redundant boundary links mitigates measurement errors that would otherwise propagate through classical feedforward. It generalizes to arbitrary GHZ sizes on arbitrary coupling topologies, achieving 2.4x higher fidelity than the Line Dynamic method while tracking the unitary baseline within 3% on Heavy-hex and Grid topologies for 30-60 qubits.
What carries the argument
Group-Majority-Voting protocol that partitions the coupling graph and uses majority votes on redundant boundary measurements to fuse local GHZ states while suppressing error propagation.
If this is right
- Group-MV applies to any GHZ size and any coupling topology.
- It delivers 2.4 times higher fidelity than the Line Dynamic method.
- Fidelity remains within 3% of the ideal unitary performance.
- The protocol has been tested on heavy-hex and grid graphs up to 60 qubits under realistic noise.
Where Pith is reading between the lines
- Dynamic circuits with mid-circuit measurements can be hardened against readout noise by adding redundant links and voting.
- Similar redundancy techniques could improve preparation of other multi-qubit entangled states such as cluster states or W states.
- Optimizing the number and placement of boundary measurements might yield further fidelity gains on specific hardware topologies.
- The approach suggests a general strategy for scaling entanglement generation on near-term devices without requiring perfect measurements.
Load-bearing premise
The noise model used in the simulations closely matches the error rates and correlations present on actual quantum hardware.
What would settle it
Running the Group-MV protocol on a physical quantum processor and measuring the prepared GHZ state fidelity to check if it exceeds the Line Dynamic method by a factor of approximately 2.4.
Figures
read the original abstract
Preparing high-fidelity Greenberger-Horne-Zeilinger (GHZ) states on noisy quantum hardware remains challenging due to cumulative gate errors and decoherence. We introduce Group-Majority-Voting (Group-MV), a dynamic-circuit protocol that partitions arbitrary coupling graphs, prepares local GHZ states in parallel, and fuses them via majority-voted mid-circuit measurements. The majority vote over redundant boundary links mitigates measurement errors that would otherwise propagate through classical feedforward. We evaluate Group-MV on simulated Heavy-hex and Grid topologies for 30 through 60 qubits under a realistic noise regime. Group-MV generalizes to arbitrary GHZ sizes on arbitrary coupling topologies, achieving 2.4x higher fidelity than the Line Dynamic method while tracking the unitary baseline within 3%.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces Group-Majority-Voting (Group-MV), a dynamic-circuit protocol that partitions arbitrary coupling graphs into subgraphs, prepares local GHZ states in parallel, and fuses them via majority-voted mid-circuit measurements on redundant boundary links to suppress measurement-error propagation. Simulations on Heavy-hex and Grid topologies for 30–60 qubits under a realistic noise regime report that Group-MV achieves 2.4× higher fidelity than the Line Dynamic method while remaining within 3% of the unitary baseline, with the abstract asserting generalization to arbitrary GHZ sizes and coupling topologies.
Significance. If the fidelity gains and generalization hold, the protocol would provide a practical route to higher-fidelity GHZ states on noisy hardware with diverse connectivities, reducing the impact of measurement errors in feedforward circuits and potentially enabling larger-scale entanglement generation without requiring perfect two-qubit gates.
major comments (3)
- [Abstract] Abstract: the central claim that Group-MV 'generalizes to arbitrary GHZ sizes on arbitrary coupling topologies' is not supported by the reported evidence, which is restricted to simulations on only two topologies (Heavy-hex and Grid) and a narrow qubit range (30–60); no scaling analysis, inductive argument, or additional graph families are presented to justify extrapolation beyond the tested cases.
- [Simulation results] Simulation results (implied Results section): fidelity numbers are given without error bars, without explicit values for the noise-model parameters (e.g., gate-error rates, measurement-error probabilities, decoherence times), and without any hardware-validation data, so the reported 2.4× improvement and 3% unitary tracking cannot be assessed for statistical significance or transferability to real devices.
- [Protocol description] Protocol description: the error-suppression mechanism of the majority-vote fusion step relies on boundary-link redundancy whose effectiveness can change on low-connectivity or irregular graphs; the manuscript provides no quantitative analysis of how the vote threshold or feedforward structure behaves when the tested Heavy-hex/Grid connectivity assumptions are relaxed.
minor comments (2)
- [Abstract] The abstract should state the precise noise-model parameters and the exact definition of 'realistic noise regime' used in the simulations.
- [Figures and tables] Figure captions and simulation tables would benefit from explicit labels for the number of shots, the precise fidelity metric (state fidelity vs. process fidelity), and the baseline unitary circuit depth.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback on our manuscript. We address each major comment below, indicating where revisions will be incorporated to improve clarity and support for the claims.
read point-by-point responses
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Referee: [Abstract] Abstract: the central claim that Group-MV 'generalizes to arbitrary GHZ sizes on arbitrary coupling topologies' is not supported by the reported evidence, which is restricted to simulations on only two topologies (Heavy-hex and Grid) and a narrow qubit range (30–60); no scaling analysis, inductive argument, or additional graph families are presented to justify extrapolation beyond the tested cases.
Authors: We agree that the reported simulations are limited to Heavy-hex and Grid topologies for 30-60 qubits. The protocol is constructed to apply to arbitrary graphs via subgraph partitioning and boundary majority voting, but we acknowledge the lack of explicit scaling analysis or additional families in the current version. We will revise the abstract to qualify the generalization claim, add an inductive argument in the methods section demonstrating how fidelity scales with group count, and include results from one additional topology (a random 4-regular graph) to better support broader applicability. revision: yes
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Referee: [Simulation results] Simulation results (implied Results section): fidelity numbers are given without error bars, without explicit values for the noise-model parameters (e.g., gate-error rates, measurement-error probabilities, decoherence times), and without any hardware-validation data, so the reported 2.4× improvement and 3% unitary tracking cannot be assessed for statistical significance or transferability to real devices.
Authors: We will add error bars derived from 100 independent simulation runs with varied random seeds to all reported fidelities. A new table will explicitly list all noise parameters used (gate error rates, measurement error probabilities, T1/T2 times). The study is simulation-based under a realistic noise model calibrated to current hardware; we will clarify the absence of experimental validation data while emphasizing the model's relevance to superconducting devices. revision: partial
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Referee: [Protocol description] Protocol description: the error-suppression mechanism of the majority-vote fusion step relies on boundary-link redundancy whose effectiveness can change on low-connectivity or irregular graphs; the manuscript provides no quantitative analysis of how the vote threshold or feedforward structure behaves when the tested Heavy-hex/Grid connectivity assumptions are relaxed.
Authors: We will add a quantitative analysis subsection examining the majority-vote mechanism on lower-connectivity graphs, including a linear chain and an irregular tree topology. This will report fidelity as a function of vote threshold and number of redundant boundary links, showing how error suppression degrades gracefully when connectivity assumptions are relaxed. revision: yes
Circularity Check
No significant circularity detected
full rationale
The paper defines the Group-MV protocol explicitly via graph partitioning, local GHZ preparation, and majority-voted boundary measurements. Reported fidelities are presented as direct simulation outputs on Heavy-hex and Grid topologies for 30-60 qubits; no parameters are fitted to the target performance metrics, no equations reduce the claimed generalization to the simulation inputs by construction, and no self-citations are invoked as load-bearing uniqueness theorems. The generalization assertion is an extrapolation from the evaluated cases rather than a self-referential derivation.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Standard quantum mechanics and device noise models govern the simulated Heavy-hex and Grid topologies.
Forward citations
Cited by 1 Pith paper
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MCMit: Mid-Circuit Measurement Error Mitigation
MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error...
Reference graph
Works this paper leans on
-
[1]
P. Høyer and R. ˇSpalek, “Quantum Fan-out is Powerful,”Theory of Computing, vol. 1, pp. 81–103, Aug. 2005, number: 5. [Online]. Available: https://theoryofcomputing.org/articles/v001a005/
work page 2005
-
[2]
A 2D nearest-neighbor quantum architecture for factoring in polylogarithmic depth,
P. Pham and K. M. Svore, “A 2D nearest-neighbor quantum architecture for factoring in polylogarithmic depth,”Quantum Information and Computation, vol. 13, no. 11&12, pp. 937–962, Nov. 2013. [Online]. Available: http://www.rintonpress.com/journals/doi/QIC13.11-12-3.html
work page 2013
-
[3]
Quantum Computing in the NISQ era and beyond
J. Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, Aug. 2018, arXiv:1801.00862 [cond-mat, physics:quant-ph]. [Online]. Available: http://arxiv.org/abs/1801.00862
work page internal anchor Pith review Pith/arXiv arXiv 2018
-
[4]
Decoherence benchmarking of superconducting qubits,
J. J. Burnett, A. Bengtsson, M. Scigliuzzo, D. Niepce, M. Kudra, P. Delsing, and J. Bylander, “Decoherence benchmarking of superconducting qubits,”npj Quantum Information, vol. 5, no. 1, p. 54, Jun. 2019. [Online]. Available: https://www.nature.com/articles/ s41534-019-0168-5
work page 2019
-
[5]
D. M. Greenberger, M. A. Horne, and A. Zeilinger, “Going Beyond Bell’s Theorem,” inBell’s Theorem, Quantum Theory and Conceptions of the Universe, M. Kafatos, Ed. Dordrecht: Springer Netherlands, 1989, pp. 69–72. [Online]. Available: http: //link.springer.com/10.1007/978-94-017-0849-4 10
-
[6]
Verifying multipartite entangled Greenberger-Horne-Zeilinger states via multiple quantum coherences,
K. X. Wei, I. Lauer, S. Srinivasan, N. Sundaresan, D. T. McClure, D. Toyli, D. C. McKay, J. M. Gambetta, and S. Sheldon, “Verifying multipartite entangled Greenberger-Horne-Zeilinger states via multiple quantum coherences,”Physical Review A, vol. 101, no. 3, Mar. 2020. [Online]. Available: http://dx.doi.org/10.1103/PhysRevA.101.032343
-
[7]
Efficient Long-Range Entanglement Using Dynamic Circuits,
E. B ¨aumer, V. Tripathi, D. S. Wang, P. Rall, E. H. Chen, S. Majumder, A. Seif, and Z. K. Minev, “Efficient Long-Range Entanglement Using Dynamic Circuits,”PRX Quantum, vol. 5, no. 3, Aug. 2024. [Online]. Available: http://dx.doi.org/10.1103/PRXQuantum.5.030339
-
[8]
Decoherence of up to 8-qubit entangled states in a 16-qubit superconducting quantum processor,
A. Ozaeta and P. L. McMahon, “Decoherence of up to 8-qubit entangled states in a 16-qubit superconducting quantum processor,”Quantum Science and Technology, vol. 4, no. 2, p. 025015, Apr. 2019. [Online]. Available: http://dx.doi.org/10.1088/2058-9565/ab13e5
-
[9]
Generation and Distribution of GHZ States in Quantum Networks,
M. Ghaderibaneh, H. Gupta, and C. Ramakrishnan, “Generation and Distribution of GHZ States in Quantum Networks,” in2023 IEEE International Conference on Quantum Computing and Engineering (QCE). Bellevue, WA, USA: IEEE, Sep. 2023, pp. 1120–1131. [Online]. Available: https://ieeexplore.ieee.org/document/10313795/
-
[10]
Enhancing Noisy Quantum Sensing by GHZ State Partitioning,
A. Zang, T.-X. Zheng, P. C. Maurer, F. T. Chong, M. Suchara, and T. Zhong, “Enhancing Noisy Quantum Sensing by GHZ State Partitioning,” 2025
work page 2025
-
[11]
G. J. Mooney, G. A. L. White, C. D. Hill, and L. C. L. Hollenberg, “Generation and verification of 27-qubit Greenberger-Horne-Zeilinger states in a superconducting quantum computer,”Journal of Physics Communications, vol. 5, no. 9, p. 095004, Sep. 2021. [Online]. Available: http://dx.doi.org/10.1088/2399-6528/ac1df7
-
[12]
Characterization of entanglement on superconducting quantum computers of up to 414 qubits,
J. F. Kam, H. Kang, C. D. Hill, G. J. Mooney, and L. C. L. Hollenberg, “Characterization of entanglement on superconducting quantum computers of up to 414 qubits,”Physical Review Research, vol. 6, no. 3, Aug. 2024. [Online]. Available: http://dx.doi.org/10.1103/PhysRevResearch.6.033155
-
[13]
Efficient Quantum Algorithms for GHZ andW States, and Implementation on the IBM Quantum Computer,
D. Cruz, R. Fournier, F. Gremion, A. Jeannerot, K. Komagata, T. Tosic, J. Thiesbrummel, C. L. Chan, N. Macris, M. e. Dupertuis, and C. e. JaverzacGaly, “Efficient Quantum Algorithms for GHZ andW States, and Implementation on the IBM Quantum Computer,”Advanced Quantum Technologies, vol. 2, no. 5-6, Apr. 2019. [Online]. Available: http://dx.doi.org/10.1002/...
-
[14]
Extracting GHZ states from linear cluster states,
J. de Jong, F. Hahn, N. Tcholtchev, M. Hauswirth, and A. Pappa, “Extracting GHZ states from linear cluster states,”Physical Review Research, vol. 6, no. 1, Mar. 2024. [Online]. Available: http: //dx.doi.org/10.1103/PhysRevResearch.6.013330
-
[15]
S. S. Chelluri, S. Khatri, and P. van Loock, “A resource- and computationally-efficient protocol for multipartite entanglement distribution in Bell-pair networks,” Dec. 2024. [Online]. Available: https://doi.org/10.48550/arXiv.2412.04252
-
[16]
B. Parhami, “Voting algorithms,”IEEE Transactions on Reliability, vol. 43, no. 4, pp. 617–629, Dec. 1994. [Online]. Available: http://ieeexplore.ieee.org/document/370218/
work page 1994
-
[17]
An Efficient Heuristic Procedure for Partitioning Graphs,
B. W. Kernighan and S. Lin, “An Efficient Heuristic Procedure for Partitioning Graphs,”Bell System Technical Journal, vol. 49, no. 2, pp. 291–307, Feb. 1970. [Online]. Available: https://ieeexplore.ieee.org/ document/6771089
-
[18]
Detecting Genuine Multipartite Entanglement with Two Local Measurements,
G. T ´oth and O. G¨ uhne, “Detecting Genuine Multipartite Entanglement with Two Local Measurements,”Physical Review Letters, vol. 94, no. 6, p. 060501, Feb. 2005. [Online]. Available: https://link.aps.org/doi/10. 1103/PhysRevLett.94.060501
work page 2005
-
[19]
Scalable Mitigation of Measurement Errors on Quantum Computers,
P. D. Nation, H. Kang, N. Sundaresan, and J. M. Gambetta, “Scalable Mitigation of Measurement Errors on Quantum Computers,”PRX Quantum, vol. 2, no. 4, p. 040326, Nov. 2021. [Online]. Available: https://link.aps.org/doi/10.1103/PRXQuantum.2.040326
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