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arxiv: 2605.12502 · v1 · submitted 2026-05-12 · 🪐 quant-ph · cond-mat.str-el

Recognition: 2 theorem links

· Lean Theorem

Scalable Measurement-Based Quantum Simulation Patterns for Benchmarking

V. W. Scarola

Pith reviewed 2026-05-13 03:42 UTC · model grok-4.3

classification 🪐 quant-ph cond-mat.str-el
keywords measurement-based quantum computingquantum simulationPauli-string unitariesmeasurement patternsquantum resource statesbenchmarkingquantum algorithmsunitary evolution
0
0 comments X

The pith

A library supplies scalable measurement patterns to execute Pauli-string unitaries for benchmarking quantum simulation on near-term hardware.

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

This paper introduces the QPatLib dataset of measurement patterns for executing quantum simulations through measurements on predefined resource states. It details a workflow to create patterns that carry out Pauli-string unitaries, which appear as building blocks across many quantum algorithms. Multiple conventions for sets of commuting Pauli strings are included so that pattern size and complexity can grow while staying within hardware limits. A reader would care because large-scale pattern optimization has been a practical barrier, and this resource offers a shared starting point to test routines, apply them directly, and gather data on what design choices work.

Core claim

The paper announces the release of the quantum measurement pattern library QPatLib in version 1.0, which contains patterns for measurement-based quantum simulation. The described workflow generates patterns that realize Pauli-string unitaries required by many quantum algorithms. Patterns are supplied under different conventions for commuting Pauli-string subsets, allowing the size and complexity of each pattern to scale. These serve as benchmarks for unitary evolution performed entirely by measurements, and the library is positioned to act as a testbed, a hardware-ready collection, a source of empirical design data, and an extensible archive for future patterns.

What carries the argument

Measurement patterns on quantum resource states that implement Pauli-string unitaries via a generation workflow supporting multiple conventions for commuting operator subsets to control scaling.

If this is right

  • The library acts as a standardized testbed for developing and comparing pattern-optimization protocols in measurement-based quantum simulation.
  • The patterns can be taken directly for implementation on current quantum hardware.
  • The dataset supplies concrete examples that can be used to test and refine empirical rules for pattern design.
  • The resource format supports ongoing addition of patterns for tasks outside quantum simulation.

Where Pith is reading between the lines

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

  • If the patterns remain efficient at larger sizes, they could lower the overhead of moving existing algorithms from gate-based to measurement-based descriptions on noisy devices.
  • The workflow structure suggests it could be adapted to generate patterns for operations beyond Pauli strings or on different resource states.
  • Systematic use of the library across multiple hardware platforms might surface recurring constraints that inform the next generation of quantum device specifications.
  • Embedding the patterns into existing quantum programming tools could allow automatic conversion of algorithm descriptions into measurement sequences.

Load-bearing premise

The generated patterns correctly and scalably perform the target Pauli-string unitaries when run under realistic hardware noise and software constraints.

What would settle it

Running one of the benchmark patterns on actual quantum hardware and finding that the measured output state deviates substantially from the state expected after the corresponding unitary evolution would indicate the patterns do not execute as claimed.

Figures

Figures reproduced from arXiv: 2605.12502 by V. W. Scarola.

Figure 1
Figure 1. Figure 1: Schematic of the workflow used to define v1.0 of [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A schematic of the measurement pattern for ex [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) An example measurement pattern that executes the unitary [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of total depth for the circuit and [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Top: The circles plot the total number of nodes [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The maximum number of measurement layers in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Left: A measurement pattern that produces the [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
read the original abstract

Measurement-based quantum computing uses measurement patterns on predefined quantum resource states to execute quantum logic. Quantum simulation offers an important use case on near-term devices. However, pattern optimization depends on the multivariable interplay between hardware and software constraints and is therefore use-dependent and highly non-trivial. Optimization of large-scale patterns under realistic assumptions remains a barrier. We announce the release of the quantum measurement pattern library QPatLib, a dataset that, in v1.0, presents patterns for use in measurement-based quantum simulation. We present the workflow for generating patterns that execute Pauli-string unitaries needed for many quantum algorithms. We provide benchmark patterns for measurement-based quantum unitary evolution. The measurement patterns are defined with different conventions for commuting Pauli-string subsets to allow scaling of pattern size and complexity. The purpose of the library is to (i) serve as a standardized testbed for pattern-optimization protocols for measurement-based quantum simulation routines, (ii) offer a suite of patterns for direct use on hardware, (iii) provide data to empirically justify pattern design principles, and (iv) provide a flexible resource for future storage and use of measurement-based patterns beyond quantum simulation.

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 / 0 minor

Summary. The manuscript announces the release of QPatLib v1.0, a dataset of measurement patterns for measurement-based quantum computing (MBQC) to execute Pauli-string unitaries. It describes a workflow for generating these patterns, including conventions for commuting subsets to enable scaling of pattern size and complexity, and positions the library as a standardized benchmark for pattern-optimization protocols, direct hardware use, empirical design justification, and future extensions beyond simulation.

Significance. If the patterns are shown to correctly implement the target unitaries (up to byproduct corrections), the library would supply a valuable, reproducible testbed for developing optimization methods under realistic hardware-software constraints, directly addressing the barrier noted for large-scale MBQC simulation patterns. The explicit scaling conventions and multi-purpose framing are strengths that support standardization and empirical studies.

major comments (1)
  1. [Abstract] The abstract states that the workflow generates patterns that 'execute Pauli-string unitaries needed for many quantum algorithms' and that the library provides 'benchmark patterns for measurement-based quantum unitary evolution,' but the manuscript supplies no explicit verification, small-case example, inductive argument, or error analysis confirming that the output patterns reproduce the desired evolution on the resource state. This is load-bearing for all four stated purposes of the library.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful review and constructive feedback on our manuscript. The comment identifies a substantive gap that we address directly below.

read point-by-point responses

  1. Referee: [Abstract] The abstract states that the workflow generates patterns that 'execute Pauli-string unitaries needed for many quantum algorithms' and that the library provides 'benchmark patterns for measurement-based quantum unitary evolution,' but the manuscript supplies no explicit verification, small-case example, inductive argument, or error analysis confirming that the output patterns reproduce the desired evolution on the resource state. This is load-bearing for all four stated purposes of the library.

    Authors: We agree that the absence of explicit verification in the original submission is a genuine limitation that undermines the claims made for the library. In the revised manuscript we have added Section 3.2, which contains (i) a fully worked small-case example for the Pauli string X_1 Z_2 on a three-qubit linear cluster state, including the explicit measurement sequence, outcome-dependent byproduct operators, and direct verification that the implemented unitary matches the target up to the expected Pauli corrections; (ii) a concise inductive outline showing how the commuting-subset conventions preserve correctness when patterns are concatenated, based on the standard MBQC composition rules; and (iii) a short error analysis in Appendix A that quantifies the effect of finite measurement precision on the generated patterns. These additions are now referenced from the abstract and from the four stated purposes of the library. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset release with external-value workflow description

full rationale

The paper is a library announcement and workflow description for generating MBQC measurement patterns that implement Pauli-string unitaries. No equations, fitted parameters, predictions, or first-principles derivations appear in the provided text. The central claim rests on the correctness of the (externally usable) generated patterns rather than any internal reduction of a result to its own inputs or self-citations. The workflow is presented as a constructive procedure whose validity is left for users or future verification; it does not invoke uniqueness theorems, rename known results, or smuggle ansatzes via self-citation. This is a standard non-circular dataset paper whose value is external.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields minimal ledger; primary domain assumption is that predefined resource states and measurement patterns can implement the needed unitaries for simulation.

axioms (1)
  • domain assumption Measurement patterns on predefined quantum resource states execute quantum logic for simulation tasks.
    Invoked in the opening sentences as the foundation for the library's purpose.

pith-pipeline@v0.9.0 · 5492 in / 1065 out tokens · 109882 ms · 2026-05-13T03:42:10.573132+00:00 · methodology

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Reference graph

Works this paper leans on

69 extracted references · 69 canonical work pages · 2 internal anchors

  1. [1]

    A One-Way Quan- tum Computer

    Robert Raussendorf. “A One-Way Quan- tum Computer”. Physical Review Letters86, 5188 (2001)

    work page 2001

  2. [2]

    Measurement-based quantum computation on cluster states

    Robert Raussendorf, Daniel E. Browne, and Hans J. Briegel. “Measurement-based quantum computation on cluster states”. Physical Review A68, 022312 (2003)

    work page 2003

  3. [3]

    Measurement-Based Quantum Computation

    Tzu-Chieh Wei. “Measurement-Based Quantum Computation”. In Brian Foster, editor, Oxford Research Encyclopedia of Physics. Oxford Uni- versity PressNew York, NY (2021). 1 edition

    work page 2021

  4. [4]

    Entangle- ment in Graph States and its Applications

    M. Hein, W. Dür, J. Eisert, R. Raussendorf, M. Van den Nest, and H. J. Briegel. “Entangle- ment in Graph States and its Applications”. Pro- ceedings of the International School of Physics "Enrico Fermi"162, 115 (2006)

    work page 2006

  5. [5]

    Strategies for quantum comput- ing molecular energies using the unitary coupled cluster ansatz

    Jonathan Romero, Ryan Babbush, Jarrod R Mc- Clean, Cornelius Hempel, Peter J Love, and Alán Aspuru-Guzik. “Strategies for quantum comput- ing molecular energies using the unitary coupled cluster ansatz”. Quantum Science and Technol- ogy4, 014008 (2019)

    work page 2019

  6. [6]

    Variational quantum algorithms

    M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles. “Variational quantum algorithms”. Nature Reviews Physics3, 625 (2021)

    work page 2021

  7. [7]

    Noisy intermediate- scale quantum algorithms

    Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhi- nav Anand, Matthias Degroote, Hermanni Hei- monen, Jakob S. Kottmann, Tim Menke, Wai- Keong Mok, Sukin Sim, Leong-Chuan Kwek, and Alán Aspuru-Guzik. “Noisy intermediate- scale quantum algorithms”. Reviews of Modern Physics94, 015004 (2022)

    work page 2022

  8. [8]

    Quantum measurements and the Abelian Stabilizer Problem

    A. Yu. Kitaev. “Quantum measurements and the Abelian Stabilizer Problem” (1995). arXiv: quant-ph/9511026

    work page internal anchor Pith review arXiv 1995

  9. [9]

    Quantum Algorithm Providing Exponential Speed Increase for Finding Eigenvalues and Eigenvectors

    Daniel S. Abrams and Seth Lloyd. “Quantum Algorithm Providing Exponential Speed Increase for Finding Eigenvalues and Eigenvectors”. Phys- ical Review Letters83, 5162 (1999)

    work page 1999

  10. [10]

    Quantum algorithms for fermionic simulations

    G. Ortiz, J. E. Gubernatis, E. Knill, and R. Laflamme. “Quantum algorithms for fermionic simulations”. Physical Review A64, 022319 (2001)

    work page 2001

  11. [11]

    Simulating physical phenom- ena by quantum networks

    R. Somma, G. Ortiz, J. E. Gubernatis, E. Knill, and R. Laflamme. “Simulating physical phenom- ena by quantum networks”. Physical Review A 65, 042323 (2002)

    work page 2002

  12. [12]

    Simulated Quantum Computation of Molecular Energies

    Alán Aspuru-Guzik, Anthony D. Dutoi, Peter J. Love, and Martin Head-Gordon. “Simulated Quantum Computation of Molecular Energies”. Science309, 1704 (2005)

    work page 2005

  13. [13]

    Quantum Algorithm for Spectral Measurement with a Lower Gate Count

    David Poulin, Alexei Kitaev, Damian S. Steiger, Matthew B. Hastings, and Matthias Troyer. “Quantum Algorithm for Spectral Measurement with a Lower Gate Count”. Physical Review Let- ters121, 010501 (2018)

    work page 2018

  14. [14]

    Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution

    Mario Motta, Chong Sun, Adrian T. K. Tan, Matthew J. O’Rourke, Erika Ye, Austin J. Min- nich, Fernando G. S. L. Brandão, and Garnet Kin-Lic Chan. “Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution”. Nature Physics16, 205 (2020)

    work page 2020

  15. [15]

    Real-Time Krylov Theory for Quantum Computing Algorithms

    Yizhi Shen, Katherine Klymko, James Sud, David B. Williams-Young, Wibe A. De Jong, and Norm M. Tubman. “Real-Time Krylov Theory for Quantum Computing Algorithms”. Quantum 7, 1066 (2023)

    work page 2023

  16. [16]

    Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization

    Jeffery Yu, Javier Robledo Moreno, Joseph T. Io- sue, Luke Bertels, Daniel Claudino, Bryce Fuller, Peter Groszkowski, Travis S. Humble, Petar Jurcevic, William Kirby, Thomas A. Maier, Mario Motta, Bibek Pokharel, Alireza Seif, Amir Shehata, Kevin J. Sung, Minh C. Tran, Vinay Tripathi, Antonio Mezzacapo, and Ku- nal Sharma. “Quantum-Centric Algorithm for S...

    work page arXiv 2025

  17. [17]

    Quantum- assisted simulator

    Kishor Bharti and Tobias Haug. “Quantum- assisted simulator”. Physical Review A104, 042418 (2021)

    work page 2021

  18. [18]

    Generalized quantum assisted simulator

    Tobias Haug and Kishor Bharti. “Generalized quantum assisted simulator”. Quantum Science and Technology7, 045019 (2022)

    work page 2022

  19. [19]

    Hybrid quantum-gap-estimation algorithm us- ing a filtered time series

    Woo-Ram Lee, Ryan Scott, and V. W. Scarola. “Hybrid quantum-gap-estimation algorithm us- ing a filtered time series”. Physical Review A 109, 052403 (2024)

    work page 2024

  20. [20]

    En- tanglementacceleratesquantumsimulation

    Qi Zhao, You Zhou, and Andrew M. Childs. “En- tanglementacceleratesquantumsimulation”. Na- ture Physics21, 1338 (2025)

    work page 2025

  21. [21]

    Noise-resilient and resource-efficient hybrid algorithm for robust quantum gap esti- mation

    Woo-Ram Lee, Nathan M. Myers, and V. W. Scarola. “Noise-resilient and resource-efficient hybrid algorithm for robust quantum gap esti- mation”. Physical Review A111, 022421 (2025)

    work page 2025

  22. [22]

    Measurement-based time evolution for quan- tum simulation of fermionic systems

    Woo-Ram Lee, Zhangjie Qin, Robert Raussendorf, Eran Sela, and V. W. Scarola. “Measurement-based time evolution for quan- tum simulation of fermionic systems”. Physical Review Research4, L032013 (2022)

    work page 2022

  23. [23]

    Measurement-Based Variational Quantum Eigensolver

    R.R. Ferguson, L. Dellantonio, A. Al Balushi, K. Jansen, W. Dür, and C.A. Muschik. “Measurement-Based Variational Quantum Eigensolver”. Physical Review Letters126, 220501 (2021)

    work page 2021

  24. [24]

    Applications and resource reductions in measurement-based variational quantum eigen- solvers

    FrederikKofoedMarqversenandNikolajThomas Zinner. “Applications and resource reductions in measurement-based variational quantum eigen- solvers”. Quantum Science and Technology8, 045001 (2023). 14

    work page 2023

  25. [25]

    Measurement-Based Infused Circuits for Vari- ational Quantum Eigensolvers

    Albie Chan, Zheng Shi, Luca Dellantonio, Wolfgang Dür, and Christine A. Muschik. “Measurement-Based Infused Circuits for Vari- ational Quantum Eigensolvers”. Physical Review Letters132, 240601 (2024)

    work page 2024

  26. [26]

    Mapping quantum circuits to shallow- depth measurement patterns based on graph states

    Thierry N Kaldenbach and Matthias Heller. “Mapping quantum circuits to shallow- depth measurement patterns based on graph states”. Quantum Science and Technology10, 015010 (2025)

    work page 2025

  27. [27]

    Efficient Preparation of Resource States for Hamiltonian Simulation and Universal Quantum Computation

    Thierry N. Kaldenbach, Isaac D. Smith, Hen- drik Poulsen Nautrup, Matthias Heller, and Hans J. Briegel. “Efficient Preparation of Resource States for Hamiltonian Simulation and Universal Quantum Computation” (2025). arXiv:2509.05404 [quant-ph]

    work page arXiv 2025

  28. [28]

    Simulation of electronic struc- ture Hamiltonians using quantum computers

    James D. Whitfield, Jacob Biamonte, and Alán Aspuru-Guzik. “Simulation of electronic struc- ture Hamiltonians using quantum computers”. Molecular Physics109, 735 (2011)

    work page 2011

  29. [29]

    Com- putational Complexity and Fundamental Lim- itations to Fermionic Quantum Monte Carlo Simulations

    Matthias Troyer and Uwe-Jens Wiese. “Com- putational Complexity and Fundamental Lim- itations to Fermionic Quantum Monte Carlo Simulations”. Physical Review Letters94, 170201 (2005)

    work page 2005

  30. [30]

    Quantum computational chemistry

    Sam McArdle, Suguru Endo, Alán Aspuru- Guzik, Simon C. Benjamin, and Xiao Yuan. “Quantum computational chemistry”. Reviews of Modern Physics92, 015003 (2020)

    work page 2020

  31. [31]

    Practical quantum advantage in quantum simulation

    Andrew J. Daley, Immanuel Bloch, Chris- tian Kokail, Stuart Flannigan, Natalie Pearson, Matthias Troyer, and Peter Zoller. “Practical quantum advantage in quantum simulation”. Na- ture607, 667 (2022)

    work page 2022

  32. [32]

    Efficient algorithm to recognize the local Clifford equivalence of graph states

    Maarten Van Den Nest, Jeroen Dehaene, and Bart De Moor. “Efficient algorithm to recognize the local Clifford equivalence of graph states”. Physical Review A70, 034302 (2004)

    work page 2004

  33. [33]

    Paralleliz- ingquantumcircuits

    Anne Broadbent and Elham Kashefi. “Paralleliz- ingquantumcircuits”. TheoreticalComputerSci- ence410, 2489 (2009)

    work page 2009

  34. [34]

    Rewrit- ingMeasurement-BasedQuantumComputations with Generalised Flow

    Ross Duncan and Simon Perdrix. “Rewrit- ingMeasurement-BasedQuantumComputations with Generalised Flow”. In David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Klein- berg, Friedemann Mattern, John C. Mitchell, Moni Naor, Oscar Nierstrasz, C. Pandu Ran- gan, Bernhard Steffen, Madhu Sudan, Demetri Terzopoulos, Doug Tygar, Moshe Y. Vardi, Gerhard Weikum, Sa...

    work page 2010

  35. [35]

    Computational depth complexity of measurement-based quantum computation

    Dan Browne, Elham Kashefi, and Simon Per- drix. “Computational depth complexity of measurement-based quantum computation”. In Wim van Dam, Vivien M. Kendon, and Simone Severini, editors, Theory of quantum computa- tion, communication, andcryptography. Page35. Berlin, Heidelberg (2011). Springer Berlin Hei- delberg

    work page 2011

  36. [36]

    Trans- forming graph states using single-qubit opera- tions

    Axel Dahlberg and Stephanie Wehner. “Trans- forming graph states using single-qubit opera- tions”. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineer- ing Sciences376, 20170325 (2018)

    work page 2018

  37. [37]

    Local Equivalence of Stabilizer States: A Graphical Characterisation

    Nathan Claudet and Simon Perdrix. “Local Equivalence of Stabilizer States: A Graphical Characterisation”. LIPIcs, Volume 327, STACS 2025327, 27:1 (2025)

    work page 2025

  38. [38]

    Minimising the number of edges in LC-equivalent graph states

    Hemant Sharma, Kenneth Goodenough, Jo- hannes Borregaard, Filip Rozpędek, and Jonas Helsen. “Minimising the number of edges in LC-equivalent graph states”. Quantum10, 2001 (2026)

    work page 2001

  39. [39]

    Trapped-ion quantum computing: Progress and challenges

    ColinD.Bruzewicz, JohnChiaverini, RobertMc- Connell, and Jeremy M. Sage. “Trapped-ion quantum computing: Progress and challenges”. Applied Physics Reviews6, 021314 (2019)

    work page 2019

  40. [40]

    Su- perconducting Qubits: Current State of Play

    Morten Kjaergaard, Mollie E. Schwartz, Jochen Braumüller, Philip Krantz, Joel I.-J. Wang, Si- mon Gustavsson, and William D. Oliver. “Su- perconducting Qubits: Current State of Play”. Annual Review of Condensed Matter Physics11, 369 (2020)

    work page 2020

  41. [41]

    Pro- posal for Pulsed On-Demand Sources of Photonic Cluster State Strings

    Netanel H. Lindner and Terry Rudolph. “Pro- posal for Pulsed On-Demand Sources of Photonic Cluster State Strings”. Physical Review Letters 103, 113602 (2009)

    work page 2009

  42. [42]

    Deterministic generation of a clus- ter state of entangled photons

    I. Schwartz, D. Cogan, E. R. Schmidgall, Y. Don, L. Gantz, O. Kenneth, N. H. Lindner, and D. Gershoni. “Deterministic generation of a clus- ter state of entangled photons”. Science354, 434 (2016)

    work page 2016

  43. [43]

    Sequential generation of linear cluster states from a single photon emitter

    D. Istrati, Y. Pilnyak, J. C. Loredo, C. Antón, N. Somaschi, P. Hilaire, H. Ollivier, M. Esmann, L. Cohen, L. Vidro, C. Millet, A. Lemaître, I. Sagnes, A. Harouri, L. Lanco, P. Senellart, and H. S. Eisenberg. “Sequential generation of linear cluster states from a single photon emitter”. Na- ture Communications11, 5501 (2020)

    work page 2020

  44. [44]

    A fault-tolerant one-way quantum computer

    R. Raussendorf, J. Harrington, and K. Goyal. “A fault-tolerant one-way quantum computer”. An- nals of Physics321, 2242 (2006)

    work page 2006

  45. [45]

    Fault- Tolerant Quantum Computation with High Threshold in Two Dimensions

    Robert Raussendorf and Jim Harrington. “Fault- Tolerant Quantum Computation with High Threshold in Two Dimensions”. Physical Review Letters98, 190504 (2007)

    work page 2007

  46. [46]

    Optimized Lie–Trotter–Suzuki decompositions for two and three non-commuting terms

    Thomas Barthel and Yikang Zhang. “Optimized Lie–Trotter–Suzuki decompositions for two and three non-commuting terms”. Annals of Physics 418, 168165 (2020)

    work page 2020

  47. [47]

    $O(N^3)$ Measurement Cost for Variational Quantum Eigensolver on Molecular Hamiltoni- ans

    Pranav Gokhale, Olivia Angiuli, Yongshan Ding, Kaiwen Gui, Teague Tomesh, Martin Suchara, 15 Margaret Martonosi, and Frederic T. Chong. “$O(N^3)$ Measurement Cost for Variational Quantum Eigensolver on Molecular Hamiltoni- ans”. IEEE Transactions on Quantum Engineer- ing1, 1 (2020)

    work page 2020

  48. [48]

    Opti- mally Generating su ( 2 N ) Using Pauli Strings

    Isaac D. Smith, Maxime Cautrès, David T. Stephen, and Hendrik Poulsen Nautrup. “Opti- mally Generating su ( 2 N ) Using Pauli Strings”. Physical Review Letters134, 200601 (2025)

    work page 2025

  49. [49]

    HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hard- ware

    Nicolas PD Sawaya, Daniel Marti-Dafcik, Yang Ho, Daniel P. Tabor, David E. Bernal Neira, Ali- cia B. Magann, Shavindra Premaratne, Pradeep Dubey, Anne Matsuura, Nathan Bishop, Wibe A. de Jong, Simon Benjamin, Ojas Parekh, Norm Tubman, Katherine Klymko, and Daan Camps. “HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hard- ware”. ...

    work page 2024

  50. [50]

    Quantum computing with Qiskit

    Ali Javadi-Abhari, Matthew Treinish, Kevin Kr- sulich, Christopher J. Wood, Jake Lishman, Julien Gacon, Simon Martiel, Paul D. Nation, Lev S. Bishop, Andrew W. Cross, Blake R. John- son, and Jay M. Gambetta. “Quantum com- puting with Qiskit” (2024). arXiv:2405.08810 [quant-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  51. [51]

    Graphix: optimizing and simulating measurement-based quantum computation on local-Clifford decorated graph

    Shinichi Sunami and Masato Fukushima. “Graphix: optimizing and simulating measurement-based quantum computation on local-Clifford decorated graph” (2022). arXiv:2212.11975 [quant-ph]

    work page arXiv 2022

  52. [52]

    STMC: Small-Tile Multiple-Copy Compilation for Reli- able Measurement-Based Quantum Computing

    Rongchao Dong, Zewei Mo, Yingheng Li, Aditya Pawar, Jun Yang, and Xulong Tang. “STMC: Small-Tile Multiple-Copy Compilation for Reli- able Measurement-Based Quantum Computing”. In2025IEEE/ACMInternationalConferenceOn Computer Aided Design (ICCAD). Page 1. Mu- nich, Germany (2025). IEEE

    work page 2025

  53. [53]

    Reinforcement Learning-Guided Graph State Generation in Photonic Quantum Comput- ers

    Yingheng Li, Yue Dai, Aditya Pawar, Rongchao Dong, Jun Yang, Youtao Zhang, and Xulong Tang. “Reinforcement Learning-Guided Graph State Generation in Photonic Quantum Comput- ers”. In Proceedings of the 52nd Annual Inter- national Symposium on Computer Architecture. Page 1598. Tokyo Japan (2025). ACM

    work page 2025

  54. [54]

    Effi- cient quantum circuits for quantum computa- tional chemistry

    Yordan S. Yordanov, David R. M. Arvidsson- Shukur, and Crispin H. W. Barnes. “Effi- cient quantum circuits for quantum computa- tional chemistry”. Physical Review A102, 062612 (2020)

    work page 2020

  55. [55]

    The measurement calculus

    Vincent Danos, Elham Kashefi, and Prakash Panangaden. “The measurement calculus”. Jour- nal of the ACM54, 8 (2007)

    work page 2007

  56. [56]

    Determin- ism in the one-way model

    Vincent Danos and Elham Kashefi. “Determin- ism in the one-way model”. Physical Review A 74, 052310 (2006)

    work page 2006

  57. [57]

    Generalized flow and deter- minism in measurement-based quantum compu- tation

    Daniel E Browne, Elham Kashefi, Mehdi Mhalla, and Simon Perdrix. “Generalized flow and deter- minism in measurement-based quantum compu- tation”. New Journal of Physics9, 250 (2007)

    work page 2007

  58. [58]

    There and back again: A circuit ex- traction tale

    Miriam Backens, Hector Miller-Bakewell, Gio- vanni de Felice, Leo Lobski, and John van de Wetering. “There and back again: A circuit ex- traction tale”. Quantum5, 421 (2021)

    work page 2021

  59. [59]

    Relating Measurement Patterns to Circuits via Pauli Flow

    Will Simmons. “Relating Measurement Patterns to Circuits via Pauli Flow”. Electronic Pro- ceedings in Theoretical Computer Science343, 50 (2021)

    work page 2021

  60. [60]

    Shadow Pauli Flow: Characteris- ing Determinism in MBQCs involving Pauli Mea- surements

    Mehdi Mhalla, Simon Perdrix, and Luc Sanselme. “Shadow Pauli Flow: Characteris- ing Determinism in MBQCs involving Pauli Mea- surements” (2025). arXiv:2207.09368 [quant-ph]

    work page arXiv 2025

  61. [61]

    An al- gebraic formulation of Pauli flow, leading to faster flow-finding algorithms

    Piotr Mitosek and Miriam Backens. “An al- gebraic formulation of Pauli flow, leading to faster flow-finding algorithms”. Journal of Physics A: Mathematical and Theoretical59, 035301 (2026)

    work page 2026

  62. [62]

    Finding Optimal Flows Efficiently

    Mehdi Mhalla and Simon Perdrix. “Finding Optimal Flows Efficiently”. In Luca Aceto, Ivan Damgård, Leslie Ann Goldberg, Mag- nús M. Halldórsson, Anna Ingólfsdóttir, and Igor Walukiewicz, editors, Automata, Languages and Programming. Volume 5125, page 857. Springer Berlin Heidelberg, Berlin, Heidelberg (2008)

    work page 2008

  63. [63]

    Optimisingtheinformationflowofone- way quantum computations

    Einar Pius, Raphael Dias Da Silva, and Elham Kashefi. “Optimisingtheinformationflowofone- way quantum computations”. Quantum Informa- tion and Computation15, 853 (2015)

    work page 2015

  64. [64]

    Über das Paulis- che Äquivalenzverbot

    P. Jordan and E. Wigner. “Über das Paulis- che Äquivalenzverbot”. Zeitschrift für Physik47, 631 (1928)

    work page 1928

  65. [65]

    On the product of semi-groups of operators

    H. F. Trotter. “On the product of semi-groups of operators”. Proceedings of the American Mathe- matical Society10, 545 (1959)

    work page 1959

  66. [66]

    Generalized Trotter’s formula and systematic approximants of exponential op- erators and inner derivations with applications to many-body problems

    Masuo Suzuki. “Generalized Trotter’s formula and systematic approximants of exponential op- erators and inner derivations with applications to many-body problems”. Communications in Mathematical Physics51, 183 (1976)

    work page 1976

  67. [67]

    Theory of Trotter Error with Commutator Scaling

    Andrew M. Childs, Yuan Su, Minh C. Tran, Nathan Wiebe, and Shuchen Zhu. “Theory of Trotter Error with Commutator Scaling”. Phys- ical Review X11, 011020 (2021)

    work page 2021

  68. [68]

    Exploring Network Structure, Dynam- ics, and Function using NetworkX

    Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart. “Exploring Network Structure, Dynam- ics, and Function using NetworkX”. In Python in Science Conference. Page 11. Pasadena, Cali- fornia (2008)

    work page 2008

  69. [69]

    Orchestrating Measurement-Based Quantum Computation over Photonic Quantum Processors

    Yingheng Li, Aditya Pawar, Mohadeseh Azari, Yanan Guo, Youtao Zhang, Jun Yang, Kaushik Parasuram Seshadreesan, and Xulong Tang. “Orchestrating Measurement-Based Quantum Computation over Photonic Quantum Processors”. In 2023 60th ACM/IEEE Design Automation Conference (DAC). Page 1. San Francisco, CA, USA (2023). IEEE. 16

    work page 2023