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arxiv: 2605.07559 · v2 · pith:77CRX53Pnew · submitted 2026-05-08 · 🪐 quant-ph · cond-mat.str-el

svPITE: A Python package for the state-vector-based probabilistic imaginary-time evolution algorithm

Pascal Sievers , Satoshi Ejima This is my paper

Pith reviewed 2026-05-20 23:02 UTC · model grok-4.3

classification 🪐 quant-ph cond-mat.str-el
keywords probabilistic imaginary-time evolutionground-state preparationstate-vector simulationquantum many-body systemsPython packagespectral functionsreal-time evolution
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The pith

The svPITE package provides a state-vector implementation of probabilistic imaginary-time evolution for preparing quantum ground states.

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

This paper presents a Python package focused on the state-vector form of the probabilistic imaginary-time evolution algorithm for ground-state preparation in quantum systems. It includes tools for tuning initial parameters to explore performance systematically and supports both state-vector and shot-based simulations with direct benchmarking to exact diagonalisation. The package further enables interoperability with other quantum simulation tools to compute real-time evolution and spectral functions such as the spin-spin dynamical structure factor.

Core claim

The central claim is that a dedicated Python package for the state-vector-based probabilistic imaginary-time evolution algorithm makes ground-state preparation more accessible by supporting efficient parameter tuning, standard shot-based runs, exact-diagonalisation benchmarks, and seamless handoff to real-time and spectral calculations.

What carries the argument

The state-vector-based probabilistic imaginary-time evolution algorithm, which projects the quantum state onto the ground state by evolving in imaginary time with controlled acceptance probabilities.

If this is right

  • Users can systematically vary initial parameters to optimise the algorithm's success probability and convergence speed.
  • Prepared ground states can be directly passed to other packages for real-time evolution studies.
  • The same workflow yields spectral functions such as the spin-spin dynamical structure factor without re-deriving the ground state.
  • Shot-based and state-vector modes allow direct comparison of statistical errors against exact results on small systems.

Where Pith is reading between the lines

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

  • The package could serve as a testbed for applying the same algorithm to models too large for exact diagonalisation.
  • Interoperability features suggest it can be combined with tensor-network or variational methods for hybrid workflows.
  • The emphasis on parameter tuning may encourage systematic benchmarks across different quantum spin or fermion models.

Load-bearing premise

The probabilistic imaginary-time evolution procedure, in its state-vector form, converges reliably to the true ground state for the targeted quantum systems without excessive computational cost or sampling errors.

What would settle it

Running the package on a small exactly solvable model such as the transverse-field Ising chain and finding that the obtained ground-state energy deviates from the known exact value by more than the statistical error bar would falsify the convergence claim.

Figures

Figures reproduced from arXiv: 2605.07559 by Pascal Sievers, Satoshi Ejima.

Figure 1
Figure 1. Figure 1: Quantum circuit of the approximate PITE algorithm for a single imaginary [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Success probability P0 as a function of imaginary time steps nsteps for the state-vector-based (blue) and shot-based (red) implementations of the PITE algo￾rithm, obtained using the example code provided in this section. (b) Corresponding energy per site E0/L as a function of nsteps for the state-vector-based simulation, as well as the final energy estimate from the shot-based PITE simulation including… view at source ↗
Figure 3
Figure 3. Figure 3: State-vector PITE simulations for different values of [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) Success probability P0 and (b) approximate ground-state energy per site E0/L as functions of the number of steps, obtained using the shot-based PITE implementation with γ = 0.53 chosen from [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: shows the svPITE results in the model (6) using L = 4, i.e. N = 16 sites. By tuning the initial parameter γ to γ = 0.6 for fixed ∆τ = 0.1, the state evolved from a Néel initial state converges to the exact ground state [dashed line in panel (a)] as the number of imaginary￾time steps increases. Note that the ground-state energy per site of the 4×4 Heisenberg model 0 20 40 60 80 100 nsteps −0.6 −0.4 −0.2 0.0… view at source ↗
Figure 6
Figure 6. Figure 6: Dynamic structure factor S(q,ω) with L = 24 and PBC in the Heisenberg model, obtained using svPITE. Panels (a) and (b) show results obtained using the state-vector-based PITE algorithm with γ = 0.46 and 0.41, respectively, while panel (c) shows results obtained using the ED algorithm. 6 Future perspectives Several directions for future development could further broaden the applicability and perfor￾mance of… view at source ↗
Figure 7
Figure 7. Figure 7: (a) Runtime of state-vector PITE simulations using sequential and parallel [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: (a) Runtime of state-vector PITE simulations using parallel and sequen [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: (a) Runtime of state-vector PITE simulations using the custom evolution [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: (a) Runtime of state-vector PITE simulations using the custom evolution [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Runtime of shot-based PITE simulations for the [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
read the original abstract

We present a Python package for ground-state preparation based on the probabilistic imaginary-time evolution algorithm, with particular focus on its state-vector-based implementation. A standard shot-based simulation is also supported, and results can be benchmarked against exact diagonalisation via a dedicated wrapper. The package enables efficient tuning of initial parameters, facilitating systematic exploration and optimisation of the method's performance. Starting from the prepared ground state, the strong interoperability with other packages further enables real-time evolution and the computation of spectral functions, such as the spin-spin dynamical structure factor.

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 presents svPITE, a Python package for ground-state preparation via the state-vector-based probabilistic imaginary-time evolution (PITE) algorithm. It supports a shot-based variant, includes a wrapper for benchmarking against exact diagonalization, enables efficient tuning of initial parameters for performance optimization, and provides interoperability hooks for real-time evolution and spectral functions such as the spin-spin dynamical structure factor.

Significance. If the implementation is numerically correct and the claimed interoperability functions as described, the package would provide a practical tool for systematic exploration of PITE methods in quantum many-body simulations. The emphasis on parameter tuning and post-ground-state applications to dynamics and spectra addresses a useful infrastructural need in the field.

minor comments (2)
  1. The abstract and introduction claim 'efficient tuning of initial parameters' without quantitative examples or metrics; adding a short illustrative benchmark (e.g., convergence vs. parameter choice) in §3 or §4 would strengthen the presentation.
  2. The manuscript would benefit from an explicit statement of code availability, installation instructions, and a minimal usage example in the main text or supplementary material to support reproducibility.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of the svPITE package and for recommending minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

No circularity: software package description with no derivation chain

full rationale

The manuscript is a description of a Python software package implementing an existing probabilistic imaginary-time evolution algorithm in state-vector and shot-based forms, with wrappers for benchmarking and interoperability hooks. No new mathematical derivations, predictions, or first-principles results are claimed or presented; the central content concerns code structure, parameter tuning utilities, and integration with other packages for real-time evolution and spectral functions. There are no equations that reduce to inputs by construction, no fitted parameters renamed as predictions, and no load-bearing self-citations that substitute for independent justification. The work is infrastructural and self-contained as a tool description against external benchmarks such as exact diagonalization.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The package rests on standard quantum mechanics for state evolution and measurement; no free parameters, ad-hoc axioms, or invented entities are introduced in the abstract description.

axioms (1)
  • standard math Standard postulates of quantum mechanics for state vectors, imaginary-time evolution, and projective measurements onto ground states.
    The probabilistic imaginary-time evolution algorithm relies on these background quantum principles.

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Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · 3 internal anchors

  1. [1]

    Imaginary-Time Evolution Using Forward and Backward Real-Time Evolution with a Single Ancilla:

    Kosugi, Taichi and Nishiya, Yusuke and Nishi, Hirofumi and Matsushita, Yu-ichiro , year = 2022, month = aug, journal =. Imaginary-Time Evolution Using Forward and Backward Real-Time Evolution with a Single Ancilla:

    work page 2022

  2. [2]

    Optimal scheduling in probabilistic imaginary-time evolution on a quantum computer , volume =. Phys. Rev. Research , author =. 2023 , pages =. doi:10.1103/PhysRevResearch.5.043048 , language =

    work page doi:10.1103/physrevresearch.5.043048 2023

  3. [3]

    Quantum computing with Qiskit

    Ali Javadi-Abhari and Matthew Treinish and Kevin Krsulich and Christopher J. Wood and Jake Lishman and Julien Gacon and Simon Martiel and Paul D. Nation and Lev S. Bishop and Andrew W. Cross and Blake R. Johnson and Jay M. Gambetta , title=. 2024 , doi=. arXiv:2405.08810 , archivePrefix=

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  4. [4]

    doi:10.48550/arxiv.2406.14792 , author =

    Qrisp: A Framework for Compilable High-Level Programming of Gate-Based Quantum Computers , eprint=. doi:10.48550/arxiv.2406.14792 , author =

    work page doi:10.48550/arxiv.2406.14792

  5. [5]

    doi:10.48550/arxiv.2111.02396 , author =

    Simulations of Quantum Circuits with Approximate Noise using qsim and Cirq , eprint =. doi:10.48550/arxiv.2111.02396 , author =

    work page doi:10.48550/arxiv.2111.02396

  6. [6]

    Suzuki, et al., Qulacs: a fast and versatile quantum circuit simulator for research purpose, Quantum 5 (2021) 559.doi:10.22331/q-2021-10-06-559

    Suzuki, Yasunari and Kawase, Yoshiaki and Masumura, Yuya and Hiraga, Yuria and Nakadai, Masahiro and Chen, Jiabao and Nakanishi, Ken M. and Mitarai, Kosuke and Imai, Ryosuke and Tamiya, Shiro and Yamamoto, Takahiro and Yan, Tennin and Kawakubo, Toru and Nakagawa, Yuya O. and Ibe, Yohei and Zhang, Youyuan and Yamashita, Hirotsugu and Yoshimura, Hikaru and ...

    work page doi:10.22331/q-2021-10-06-559 2021

  7. [7]

    PennyLane: Automatic differentiation of hybrid quantum-classical computations

    PennyLane: Automatic differentiation of hybrid quantum-classical computations , eprint =. doi:10.48550/arxiv.1811.04968 , author =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1811.04968

  8. [8]

    Weinberg, Phillip and Bukov, Marin , year = 2017, month = feb, journal =

    work page 2017

  9. [9]

    Probabilistic imaginary-time evolution in state-vector-based and shot-based simulations and on quantum devices , author =. Phys. Rev. Research , volume =. 2025 , month =. doi:10.1103/2s2p-kvcx , url =

    work page doi:10.1103/2s2p-kvcx 2025

  10. [10]

    , year =

    Sandvik, Anders W. , year =. Finite-size scaling of the ground-state parameters of the two-dimensional. Phys. Rev. B , publisher =. doi:10.1103/physrevb.56.11678 , number =

    work page doi:10.1103/physrevb.56.11678

  11. [11]

    High-precision ground state parameters of the two-dimensional spin-1/2 Heisenberg model on the square lattice

    High-precision ground state parameters of the two-dimensional spin-1/2. doi:10.48550/arxiv.2601.20189 , author =. arXiv:2601.20189 , year =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2601.20189

  12. [12]

    1967 , issn =

    Integration of the Schrödinger equation in imaginary time , journal =. 1967 , issn =. doi:10.1016/0021-9991(67)90049-6 , author =

    work page doi:10.1016/0021-9991(67)90049-6 1967

  13. [13]

    Foulkes, W. M. C. and Mitas, L. and Needs, R. J. and Rajagopal, G. , journal =. 2001 , month =

    work page 2001

  14. [14]

    Schollw \"o ck ,\ 10.1016/j.aop.2010.09.012 journal journal Ann

    The density-matrix renormalization group in the age of matrix product states , journal =. 2011 , issn =. doi:10.1016/j.aop.2010.09.012 , author =

    work page doi:10.1016/j.aop.2010.09.012 2011

  15. [15]

    Variational ansatz-based quantum simulation of imaginary time evolution

    Variational Ansatz-Based Quantum Simulation of Imaginary Time Evolution , author =. 2019 , month = sep, volume =. doi:10.1038/s41534-019-0187-2 , journal =

    work page doi:10.1038/s41534-019-0187-2 2019

  16. [16]

    Variational quantum algorithms for discovering Hamiltonian spectra , author =. Phys. Rev. A , volume =. 2019 , month =

    work page 2019

  17. [17]

    doi:10.22331/q-2019-10-07-191 , url =

    Theory of variational quantum simulation , author =. doi:10.22331/q-2019-10-07-191 , url =

    work page doi:10.22331/q-2019-10-07-191 2019

  18. [18]

    Motta, Mario and Sun, Chong and Tan, Adrian T. K. and O’Rourke, Matthew J. and Ye, Erika and Minnich, Austin J. and Brandão, Fernando G. S. L. and Chan, Garnet Kin-Lic , year =. Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution , volume =. Nat. Phys. , publisher =. doi:10.1038/s41567-019-0704-4 , number =

    work page doi:10.1038/s41567-019-0704-4

  19. [19]

    and Smith, Adam and Pollmann, Frank , year =

    Lin, Sheng-Hsuan and Dilip, Rohit and Green, Andrew G. and Smith, Adam and Pollmann, Frank , year =. Real- and. PRX Quantum , volume =

    work page

  20. [20]

    Trotter, H. F. , year =. On the product of semi-groups of operators , volume =. Proc. Amer. Math. Soc. , publisher =. doi:10.1090/s0002-9939-1959-0108732-6 , number =

    work page doi:10.1090/s0002-9939-1959-0108732-6 1959

  21. [21]

    , year =

    Suzuki, M. , year =. Relationship between d-Dimensional Quantal Spin Systems and (d+1)-Dimensional. Prog. Theor. Phys. , publisher =. doi:10.1143/ptp.56.1454 , number =

    work page doi:10.1143/ptp.56.1454