pith. sign in

arxiv: 2604.15209 · v1 · submitted 2026-04-16 · 🪐 quant-ph

Variational quantum state preparation within an entangle-rotate circuit framework for quantum-enhanced metrology in noisy systems

Pith reviewed 2026-05-10 10:36 UTC · model grok-4.3

classification 🪐 quant-ph
keywords variational quantum circuitsquantum Fisher informationquantum metrologynoisy quantum systemsentangle-rotate circuitsquantum state preparationdecoherencetwo-level systems
0
0 comments X

The pith

Optimizing repeated entangle-rotate layers in a variational circuit maximizes quantum Fisher information for metrology even when noise is present.

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

The paper establishes that a variational quantum circuit built from successive entangling operations followed by global rotations can prepare states whose quantum Fisher information exceeds what shallower circuits achieve, and that this improvement persists at appreciable decoherence rates. A sympathetic reader would care because the result points to a concrete way of generating quantum-enhanced sensing states without requiring perfect isolation from the environment. The authors test the approach on two-level systems whose interactions range from all-to-all to nearest-neighbor power-law couplings and show that the same circuit family remains useful for qubit numbers larger than eight. The central mechanism is numerical maximization of the QFI with respect to the circuit parameters for each fixed noise strength and Hamiltonian.

Core claim

The authors introduce an entangle-rotate circuit in which each layer consists of entangling gates followed by a global rotation; variational parameters within these layers are optimized to maximize the quantum Fisher information of the output state for a chosen decoherence rate and interaction Hamiltonian. Numerical evidence shows that QFI rises with circuit depth even under realistic noise, that the architecture works for power-law interactions spanning all-to-all to nearest-neighbor, and that the method scales to system sizes beyond eight qubits.

What carries the argument

The entangle-rotate layer (entangling gates followed by a global rotation) iterated in multiple variational depths, with parameters chosen to maximize output-state QFI.

If this is right

  • QFI continues to improve with added circuit layers despite noise.
  • The same circuit family works across power-law interaction ranges from all-to-all to nearest-neighbor.
  • States suitable for metrology can be prepared for qubit numbers larger than eight.
  • The architecture supplies a general variational route to quantum-enhanced sensing states under realistic decoherence.

Where Pith is reading between the lines

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

  • Current noisy hardware could already implement modest-depth versions of these circuits to test whether the predicted QFI gains appear in practice.
  • The same layer structure might be reused for other metrology tasks such as phase estimation with different Hamiltonians by simply changing the optimization target.
  • Hybrid classical-quantum training schedules could adaptively select the number of layers needed for a given noise level rather than fixing depth in advance.

Load-bearing premise

Numerical optimization of the circuit parameters reaches values sufficiently close to the global maximum of QFI for the chosen noise model and Hamiltonian.

What would settle it

If simulations or experiments at fixed noise rate show that QFI stops increasing or begins to decrease once circuit depth exceeds a modest number of layers, the claim that deeper entangle-rotate circuits expand the accessible high-QFI state space would be refuted.

Figures

Figures reproduced from arXiv: 2604.15209 by Jeffrey Larson, Juan C. Zu\~niga Castro, Matt Menickelly, Michael A. Perlin, Robert J. Lewis-Swan, Sri Hari Krishna Narayanan, Yicheng Zhang.

Figure 1
Figure 1. Figure 1: FIG. 1. A one-dimensional chain of [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. A set of state diagnostic quantities and VQC parame [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. A set of state diagnostic quantities and VQC parame [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Diagnostic quantities and VQC parameters with an [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Diagnostic quantities and VQC parameters with an [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. QFI ( [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Diagnostic quantities as a function of system size [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Optimal VQC parameter set [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Optimal VQC parameter set [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
read the original abstract

We investigate the generation of quantum states for precision metrology in noisy two-level systems. These states are obtained by optimizing a variational quantum circuit to maximize the quantum Fisher information (QFI) of the output state for a given decoherence rate and interaction Hamiltonian. The circuit architecture, inspired by twist-and-turn schemes, features a sequence of $n$ entangling layers, each consisting of entangling gates followed by a global rotation. We observe notable improvements in the QFI as the circuit layer depth increases, even for appreciable noise rates, demonstrating that our entangle-rotate architecture expands the accessible state space under realistic noise conditions. Our approach thus provides a general and efficient framework for generating quantum-enhanced sensing states. Our analysis extends to systems of power-law interactions spanning from all-to-all to nearest-neighbor interactions. We also analyze the capabilities of our circuit to prepare states for system sizes greater than $8$ qubits.

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

1 major / 1 minor

Summary. The manuscript introduces an entangle-rotate variational circuit (entangling gates followed by global rotations, inspired by twist-and-turn schemes) that is numerically optimized to maximize the quantum Fisher information (QFI) of the output state for metrology in noisy two-level systems. It reports that QFI improves with increasing circuit depth even at appreciable noise rates, claims this demonstrates expansion of the accessible state space under realistic decoherence, extends the analysis to power-law interactions (all-to-all to nearest-neighbor), and considers system sizes beyond 8 qubits.

Significance. If the reported QFI values are shown to be near-global optima, the architecture would provide a practical, scalable route to noise-resilient quantum-enhanced sensing states, offering a concrete variational framework that could be implemented on near-term hardware for systems with varying interaction ranges.

major comments (1)
  1. [Abstract / Numerical results] Abstract and numerical optimization results: The headline claim that the entangle-rotate architecture 'expands the accessible state space under realistic noise conditions' rests on observed QFI gains with layer depth. However, no convergence diagnostics are provided (e.g., statistics over multiple random seeds, success-rate fractions, or error bars on the optimized QFI). For non-convex noisy QFI landscapes, monotonic improvement with depth could arise from progressively better local minima rather than genuine enlargement of the reachable high-QFI set. Benchmarks against exact global optima (via SDP or exhaustive search for n≤6) or lower bounds on achieved QFI are also absent, making the central architectural claim only moderately supported.
minor comments (1)
  1. [Abstract] The abstract refers to 'appreciable noise rates' and 'notable improvements' without quantifying the specific rates, QFI values, or system sizes in the summary; a concise table of peak QFI versus depth and noise strength would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and valuable feedback on our manuscript. We address the concerns raised in the major comment point by point below. We have revised the manuscript to include additional numerical diagnostics and benchmarks where feasible to strengthen the support for our claims.

read point-by-point responses
  1. Referee: However, no convergence diagnostics are provided (e.g., statistics over multiple random seeds, success-rate fractions, or error bars on the optimized QFI).

    Authors: We acknowledge that the submitted manuscript does not report convergence diagnostics such as results over multiple random seeds or error bars on the optimized QFI values. In the revised version we will add these by repeating the variational optimization from 20 independent random initializations for each circuit depth and noise rate, reporting both the mean QFI and its standard deviation. This will directly address the concern about reliability of the observed gains. revision: yes

  2. Referee: For non-convex noisy QFI landscapes, monotonic improvement with depth could arise from progressively better local minima rather than genuine enlargement of the reachable high-QFI set.

    Authors: We agree this is a legitimate possibility in non-convex optimization. While we cannot exclude local-minima effects without exhaustive global search, the persistence of QFI improvement across widely varying noise strengths, interaction ranges (all-to-all to nearest-neighbor), and system sizes provides circumstantial evidence that the entangle-rotate layers are systematically enlarging the reachable high-QFI manifold. We will add a dedicated paragraph in the revised manuscript discussing this caveat and the supporting qualitative arguments from the circuit structure. revision: partial

  3. Referee: Benchmarks against exact global optima (via SDP or exhaustive search for n≤6) or lower bounds on achieved QFI are also absent, making the central architectural claim only moderately supported.

    Authors: We concur that explicit comparison to global optima would strengthen the central claim. For n ≤ 6 the semidefinite-program formulation of noisy QFI maximization is computationally tractable. In the revision we will compute these global bounds for n = 4 and n = 6 at representative noise rates and include the ratios of our variational QFI to the SDP optimum, thereby quantifying how close the entangle-rotate states come to the theoretical maximum. revision: yes

Circularity Check

0 steps flagged

No significant circularity in variational QFI optimization

full rationale

The paper optimizes variational parameters in an entangle-rotate circuit to maximize QFI under a given noise model and Hamiltonian, then reports numerical improvements in the achieved QFI with increasing depth. QFI is evaluated directly via standard formulas on the output state; no parameters are fitted to a subset and then relabeled as predictions, no self-definitional loops exist in the architecture or objective, and no load-bearing self-citations or uniqueness theorems are invoked in the provided claims. The derivation is a self-contained numerical procedure whose outputs are not equivalent to its inputs by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard quantum circuit evolution and numerical optimization of circuit parameters; no new physical entities are postulated and the only adjustable elements are the variational angles optimized per instance.

free parameters (1)
  • variational rotation and entangling angles
    Circuit parameters are numerically optimized to maximize QFI for each chosen decoherence rate, depth, and interaction range.
axioms (2)
  • domain assumption Markovian decoherence model (Lindblad form) for the two-level systems
    The noise is incorporated via a fixed decoherence rate in the master equation governing the circuit evolution.
  • standard math Standard unitary evolution under the given interaction Hamiltonian between entangling layers
    The circuit applies entangling gates derived from the power-law interaction Hamiltonian followed by global rotations.

pith-pipeline@v0.9.0 · 5490 in / 1282 out tokens · 40078 ms · 2026-05-10T10:36:56.299209+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

64 extracted references · 64 canonical work pages

  1. [1]

    For example, we compute GHZ fidelities (F(n) GHZ) by setting|Ψ⟩=|GHZ,Φ⟩[see Eq

    State fidelities We define the general fidelity between a pure target state|Ψ⟩and then-layer VQC output state ˆρ(x (n) opt) as F(n) Ψ =⟨Ψ|ˆρ(x(n) opt)|Ψ⟩.(8) In our results, we compute target-state fidelities with spe- cific pure states. For example, we compute GHZ fidelities (F(n) GHZ) by setting|Ψ⟩=|GHZ,Φ⟩[see Eq. (6)]. The fi- delity is maximized by op...

  2. [2]

    In the text we use the CES to delineate the uncorre- lated regime at the decoherenceγ 2 where Θ(1) I = 0

    Cumulative quantities In the text we also present cumulative quantities for an n-layer VQC, namely the cumulative entangling strength (CES) and cumulative rotation angle (CRA) Θ(n) I = nX k=1 θk I ,(9) Θ(n) x = nX k=1 θk x.(10) Equations (9) and (10) provide a compact characteriza- tion of multi-layer VQC implementations, whose number of parameters increa...

  3. [3]

    In the text, we present the scaled squeezing parameter (N ξ2 s)−1 where larger values correspond to states exhibit- ing squeezed-like character

    Squeezing As another diagnostic, we compute the Wineland squeezing parameter [42], ξ2 s =Nmin n⊥ (∆ ˆSn⊥)2 | ⟨ˆS⟩ |2 ,(11) where ˆS= ( ˆSx, ˆSy, ˆSz) is the spin vector operator, (∆ ˆSv)2 =⟨( ˆS·v) 2⟩ − ⟨ ˆS·v⟩ 2 is the spin variance along axisv, and the minimization is performed over unit vec- torsn ⊥ orthogonal to the mean spin vector⟨ ˆS⟩. In the text,...

  4. [4]

    Bounded from above by the QFI, the CFI quantifies the extent to which readout restricted to collective observables may exploit a given state’s metro- logical potential

    Classical Fisher Information & State Collectivity We compute the state’s classical Fisher information (CFI), FC(θ) = lim θ→0 4 θ2 X mz p P(m z;θ)− p P(m z;θ= 0) 2 , (12) whereP(m z) is the distribution function for the collec- tive spin projectionm z along ˆzafter being rotated by a small angleθ. Bounded from above by the QFI, the CFI quantifies the exten...

  5. [5]

    2 as a function of the scaled decoherence strength γ/χ

    One-axis twisting Results for an E–R-layered OAT scheme are presented in Fig. 2 as a function of the scaled decoherence strength γ/χ. Panel 2(a) shows the optimal QFI for 1-layer (n= 1, open circles) and 3-layer (n= 3, filled cir- cles) OAT. The QFI of each case is scaled by the HL (FQ/N2). For weak decoherence (i.e.,γ/χ≪1), the 1- layer OAT VQC already g...

  6. [6]

    plateaus

    Two-axis twisting Having identified benefits of layering in the OAT case, we now apply a similar analysis to VQCs implemented with a TAT Hamiltonian (FTAT withα= 0). Fig- ure 3 summarizes the TAT performance as a function of γ/χ. In panel 3(a), the scaled QFI (F Q/N2) is shown forn= 1 (open triangles) andn= 3 (filled trian- gles) VQC architectures. The 1-...

  7. [7]

    Ising In this section we compare the results forα= 3 Ising to the collective OAT case presented in Sec. IV B 1. Rel- ative to OAT, the shortened interaction ranges of finite- range Ising attenuate the achievable QFIs [see Fig. 4(a)]. Nevertheless, layering significantly improves the achiev- able QFI within the Ising implementation. Specifically, increasin...

  8. [8]

    FTAT We now turn to FTAT implementations withα= 3 and compare them with the collective TAT case of Sec. IV B 2. Relative to the Ising case of the previous section, FTAT is even less responsive to layering atα= 3 [see Fig. 5(a)]. Specifically, the QFI improves modestly when increasing the layer count fromn= 1 ton= 3 within the FTAT MIR (γ/χ≲0.2, shaded reg...

  9. [9]

    Ganapathy, W

    D. Ganapathy, W. Jia, M. Nakano, V. Xu, N. Aritomi, T. Cullen, N. Kijbunchoo, S. E. Dwyer, A. Mullavey, L. McCuller, R. Abbott, I. Abouelfettouh, R. X. Ad- hikari, A. Ananyeva, S. Appert, K. Arai, S. M. Aston, M. Ball, S. W. Ballmer, D. Barker, L. Barsotti, B. K. Berger, J. Betzwieser, D. Bhattacharjee, G. Billingsley, S. Biscans, N. Bode, E. Bonilla, V. ...

  10. [10]

    Pezz` e, A

    L. Pezz` e, A. Smerzi, M. K. Oberthaler, R. Schmied, and P. Treutlein, Rev. Mod. Phys.90, 035005 (2018)

  11. [11]

    Pedrozo-Pe˜ nafiel, S

    E. Pedrozo-Pe˜ nafiel, S. Colombo, C. Shu, A. F. Adiy- atullin, Z. Li, E. Mendez, B. Braverman, A. Kawasaki, D. Akamatsu, Y. Xiao, and V. Vuleti´ c, Nature588, 414 (2020)

  12. [12]

    K. A. Gilmore, M. Affolter, R. J. Lewis-Swan, D. Barber- ena, E. Jordan, A. M. Rey, and J. J. Bollinger, Science 373, 673 (2021)

  13. [13]

    L. Jiao, W. Wu, S. Bai, and J. An, Advanced Quantum Technologies8, (2023). 13

  14. [14]

    Zheng, A

    T.-X. Zheng, A. Li, J. Rosen, S. Zhou, M. Koppenh¨ ofer, Z. Ma, F. T. Chong, A. A. Clerk, L. Jiang, and P. C. Maurer, npj Quantum Information8, 150 (2022)

  15. [15]

    Mu˜ noz de las Heras, C

    A. Mu˜ noz de las Heras, C. Tabares, J. T. Schneider, L. Tagliacozzo, D. Porras, and A. Gonz´ alez-Tudela, Phys- ical Review Research6, 013299 (2024)

  16. [16]

    Tabares, A

    C. Tabares, A. Mu˜ noz de las Heras, L. Tagliacozzo, D. Porras, and A. Gonz´ alez-Tudela, Phys. Rev. Lett. 131, 073602 (2023)

  17. [17]

    Bravo-Prieto, J

    C. Bravo-Prieto, J. Lumbreras-Zarapico, L. Tagliacozzo, and J. I. Latorre, Quantum4, 272 (2020)

  18. [18]

    Kaubruegger, D

    R. Kaubruegger, D. V. Vasilyev, M. Schulte, K. Ham- merer, and P. Zoller, Physical Review X11, 041045 (2021)

  19. [19]

    M.; Diamanti, E.; Kerenidis, I

    B. MacLellan, P. Roztocki, S. Czischek, and R. G. Melko, npj Quantum Information10, 10.1038/s41534- 024-00914-w (2024)

  20. [20]

    J. C. Zu˜ niga Castro, J. Larson, S. H. K. Narayanan, V. E. Colussi, M. A. Perlin, and R. J. Lewis-Swan, Physical Review. A110, 052615 (2024)

  21. [21]

    Larocca, N

    M. Larocca, N. Ju, D. Garc´ ıa-Mart´ ın, P. J. Coles, and M. Cerezo, Nature Computational Science3, 542 (2023)

  22. [22]

    Muessel, H

    W. Muessel, H. Strobel, D. Linnemann, T. Zibold, B. Juli´ a-D´ ıaz, and M. K. Oberthaler, Physical Review. A, Atomic, Molecular, and Optical Physics92, 023603 (2015)

  23. [23]

    Y. C. Liu, Z. F. Xu, G. R. Jin, and L. You, Phys. Rev. Lett.107, 013601 (2011)

  24. [24]

    J. T. Reilly, S. B. J¨ ager, J. D. Wilson, J. Cooper, S. Eg- gert, and M. J. Holland, Physical Review Research6, 033090 (2024)

  25. [25]

    S. C. Carrasco, M. H. Goerz, Z. Li, S. Colombo, V. Vuleti´ c, and V. S. Malinovsky, Physical Review Ap- plied17, 064050 (2022)

  26. [26]

    T. G. Thurtell and A. Miyake, Physical Review Research 6, 023179 (2024)

  27. [27]

    S. S. Mirkhalaf, S. P. Nolan, and S. A. Haine, Phys. Rev. A97, 053618 (2018)

  28. [28]

    Roscilde, M

    T. Roscilde, M. Kumari, A. Cooper, and F. Mez- zacapo, Exponential onset of scalable entanglement via twist-and-turn dynamics in XY models (2025), arXiv:2507.08206 [quant-ph]

  29. [29]

    Zhang, J

    Y. Zhang, J. C. Zu˜ niga Castro, and R. J. Lewis-Swan, Phys. Rev. Res.7, 013227 (2025)

  30. [30]

    Lloyd, Science273, 1073 (1996)

    S. Lloyd, Science273, 1073 (1996)

  31. [31]

    Gietka, A

    K. Gietka, A. Usui, J. Deng, and T. Busch, Physical Re- view Letters126, 160402 (2021)

  32. [32]

    G. E. Uhlenbeck, P. C. Hemmer, and M. Kac, Journal of Mathematical Physics4, 229 (1963)

  33. [33]

    Defenu, Proceedings of the National Academy of Sci- ences118, 1 (2021)

    N. Defenu, Proceedings of the National Academy of Sci- ences118, 1 (2021)

  34. [34]

    Foss-Feig, K

    M. Foss-Feig, K. R. A. Hazzard, J. J. Bollinger, A. M. Rey, and C. W. Clark, New Journal of Physics15, 113008 (2013)

  35. [35]

    Porras and J

    D. Porras and J. I. Cirac, Phys. Rev. Lett.92, 207901 (2004)

  36. [36]

    Pang and T

    S. Pang and T. A. Brun, Phys. Rev. A90, 022117 (2014)

  37. [37]

    T´ oth and I

    G. T´ oth and I. Apellaniz, Journal of Physics. A, Mathe- matical and Theoretical47, 424006 (2014)

  38. [38]

    Shammah, S

    N. Shammah, S. Ahmed, N. Lambert, S. De Liberato, and F. Nori, Physical Review. A98, 063815 (2018)

  39. [39]

    S. H. K. Narayanan, M. A. Perlin, R. Lewis-Swan, J. Lar- son, M. Menickelly, J. H¨ uckelheim, and P. Hovland, Chal- lenges with implementing differentiable quantum dynam- ics, inProceedings of the 2024 International Conference on Algorithmic Differentiation (AD)(Society for Indus- trial and Applied Mathematics, 2026) pp. 257–267

  40. [40]

    Bradbury, R

    J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. Van- derPlas, S. Wanderman-Milne, and Q. Zhang, JAX: com- posable transformations of Python+NumPy programs (2018)

  41. [41]

    Kidger,On Neural Differential Equations, Ph.D

    P. Kidger,On Neural Differential Equations, Ph.D. the- sis, University of Oxford (2021)

  42. [42]

    K.-W. E. Chu, SIAM Journal on Numerical Analysis27, 1368 (1990)

  43. [43]

    V. E. Colussi, J. Larson, R. J. Lewis-Swan, S. H. K. Narayanan, M. A. Perlin, and J. C. Zu˜ niga Castro, QFI- Opt (2024)

  44. [44]

    C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, ACM Trans- actions on Mathematical Software23, 550 (1997)

  45. [45]

    Virtanen, R

    P. Virtanen, R. Gommers, T. E. Oliphant, M. Haber- land, T. Reddy, D. Cournapeau, E. Burovski, P. Pe- terson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, ˙I. Po- lat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henr...

  46. [46]

    Shahriari, K

    B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, Proceedings of the IEEE104, 148 (2015)

  47. [47]

    P. I. Frazier, inRecent Advances in Optimization and Modeling of Contemporary Problems(INFORMS, 2018) pp. 255–278

  48. [48]

    Nogueira, Bayesian Optimization: Open source con- strained global optimization tool for Python (2014–)

    F. Nogueira, Bayesian Optimization: Open source con- strained global optimization tool for Python (2014–)

  49. [49]

    Kitagawa and M

    M. Kitagawa and M. Ueda, Phys. Rev. A47, 5138 (1993)

  50. [50]

    D. J. Wineland, J. J. Bollinger, W. M. Itano, F. L. Moore, and D. J. Heinzen, Physical Review A46, R6797 (1992)

  51. [51]

    Kajtoch and E

    D. Kajtoch and E. Witkowska, Physical Review A, Atomic, Molecular, and Optical Physics92, 013623 (2015)

  52. [52]

    T. J. Volkoff and C. Ryu, Frontiers in Physics12, (2024)

  53. [53]

    M. A. Perlin, C. Qu, and A. M. Rey, Physical Review Letters125, 223401 (2020)

  54. [54]

    S. F. Huelga, C. Macchiavello, T. Pellizzari, A. K. Ekert, M. B. Plenio, and J. I. Cirac, Phys. Rev. Lett.79, 3865 (1997)

  55. [55]

    S. A. Haine, Physical Review A98, 030303(R) (2018)

  56. [56]

    R. J. Lewis-Swan, J. C. Zu˜ niga Castro, D. Barberena, and A. M. Rey, Phys. Rev. Lett.132, 163601 (2024)

  57. [57]

    H. F. Trotter, Proceedings of the American Mathematical Society10, 545 (1959)

  58. [58]

    Suzuki, Communications in Mathematical Physics51, 183 (1976)

    M. Suzuki, Communications in Mathematical Physics51, 183 (1976)

  59. [59]

    M. A. Perlin, D. Barberena, and A. M. Rey, Phys. Rev. A104, 062413 (2021). Appendix A: State preparation strategies In Sec. IV, we provided results generated with the Ising and FTAT Hamiltonians given by Eqs. (2) and (3), re- spectively. There, we characterized the states found un- der infinite-range (α= 0) and finite-range (α= 3) inter- 14 actions using ...

  60. [60]

    Ising-based VQCs Figure 8 shows the preparation strategies for VQC implementations based on the Ising Hamiltonian [see Eq. (2)]. In these results, the left (right) column cor- responds to settingα= 0 (α= 3). We find that OAT (Ising withα= 0) and Ising withα= 3 exhibit several similarities. For instance, in the 1- and 3-layer VQC of each case, the optimal ...

  61. [61]

    trotterized

    FTAT-based VQCs We now show the optimal preparation strategies for FTAT VQCs [see Eq. (3)]. In the left and right columns of Figure 9, we provide results for TAT (FTAT withα= 0) and FTAT withα= 3, respectively. In contrast to the re- sults for Ising implementations presented in the last sec- tion, the optimal preparation strategies (x (n) opt) for FTAT wi...

  62. [62]

    This idea was supported by com- parable QFIs obtained through 1-layer and 3-layer VQCs

    Toward cat-like state generation We had initially conjectured that 3-layer OAT (TAT) VQC implementations would generate 1-layer TAT-like (OAT-like) dynamics. This idea was supported by com- parable QFIs obtained through 1-layer and 3-layer VQCs. In the case of 3-layer OAT, the QFI approaches the QFI for 1-layer TAT forγ≳γ 1 (see open triangles in Fig. 8)....

  63. [63]

    A simplified case Consider first the simplified case in whichNspins oc- cupy the permutationally symmetric manifold with spin lengthS=N/2. In this case, for a density matrix ˆρwe 16 0.0 0.5 1.0 FQ/N2 SQL TAT (FTAT; α = 0) FQ/N2 : n = 1, n = 3 0.0 0.5 θ1/π {θ1/π, ϕ 1/π, θ y/π}: n = 1, n = 3 0.0 0.5 ϕ1/π 0.0 0.5 θk I /π {θk I /π, θ k x/π} n = 1 : n = 3 : k ...

  64. [64]

    In this case, the state ˆρ can no longer be faithfully represented by a probabil- ity distribution on a sphere

    The general case More generally, the state ˆρmay have components with non-maximal spin lengthS. In this case, the state ˆρ can no longer be faithfully represented by a probabil- ity distribution on a sphere. Nonetheless, we meaning- fully visualize the spin polarization of ˆρby averaging over probability distributions that represent components of ˆρ withi...