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arxiv: 2606.26085 · v1 · pith:ASCBYTK7new · submitted 2026-06-24 · 🪐 quant-ph

Analytic Approach to Quantum Control Using Quantum Signal Processing

Aishwarya Majumdar , John M. Martyn , Yuan Liu , Nathan Wiebe This is my paper

Pith reviewed 2026-06-25 19:39 UTC · model grok-4.3

classification 🪐 quant-ph
keywords quantum controlqubit-oscillator systemscross-Kerr interactionFock statesJaynes-Cummings modelanalytic pulse designquantum signal processing
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The pith

Quantum signal processing supplies an analytic framework for designing controls that suppress cross-Kerr effects and select Fock states in qubit-oscillator systems.

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

The paper introduces an analytic method that recasts several quantum control tasks for dispersively coupled qubit-oscillator systems as problems solvable by quantum signal processing. It uses this mapping to construct driving protocols that counteract unwanted nonlinear interactions and to build operators that act only on chosen Fock states. A reader would care because standard control design depends on numerical search that yields little insight into why a solution works or how to improve it. The new approach instead supplies explicit constructions with built-in error structure.

Core claim

The authors establish an analytic framework for quantum control of qubit-oscillator dynamics by mapping the system to quantum signal processing polynomials. This mapping produces operators that mitigate cross-Kerr nonlinearities and enables Fock-state-selective manipulations through structural parallels with the Jaynes-Cummings interaction. The result converts several practical control problems into forms that admit systematic pulse design and clearer interpretation.

What carries the argument

QSP-Control framework that converts Jaynes-Cummings dynamics into quantum signal processing polynomials for operator construction.

If this is right

  • Driving pulses can be designed analytically rather than by brute-force numerical optimization.
  • Cross-Kerr interactions can be suppressed by explicit constructions instead of trial-and-error tuning.
  • Fock states can be addressed selectively through operators whose action is known in closed form.
  • Control solutions gain an interpretable structure that reveals how each segment of the drive contributes to the target transformation.

Where Pith is reading between the lines

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

  • The same polynomial-mapping technique might apply to other nonlinear couplings if their interaction Hamiltonians admit similar decompositions.
  • Error bounds already available for signal-processing sequences could be carried over to give hardware-independent performance estimates for the new controls.
  • The framework could be tested by checking whether pulses designed this way maintain higher fidelity under realistic decoherence than pulses found by gradient descent on the same task.

Load-bearing premise

The structural parallels between the Jaynes-Cummings interaction and quantum signal processing polynomials are strong enough that the resulting operators actually suppress cross-Kerr effects and achieve Fock-state selectivity when applied to physical hardware.

What would settle it

Implement the constructed Fock-state-selective operators on a physical dispersively coupled qubit-oscillator device and measure whether the observed state selectivity and cross-Kerr suppression match the analytic predictions.

Figures

Figures reproduced from arXiv: 2606.26085 by Aishwarya Majumdar, John M. Martyn, Nathan Wiebe, Yuan Liu.

Figure 1
Figure 1. Figure 1: FIG. 1. Illustration of the polynomial approximation of phase [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Polynomial approximations to the phase values of [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Polynomial [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
read the original abstract

Realizing coherent quantum computation requires precise and robust manipulation of quantum systems through quantum control protocols. Most quantum control techniques rely on heuristic methods for designing the driving pulses that steer the system towards a target state. Such methods are often based on brute-force optimization and offer limited understanding of the solution landscape. In contrast, quantum algorithms offer a rich body of analytical methods with rigorous error guarantees for implementing unitary and non-unitary transformations, which suggests a promising direction for developing new approaches to quantum control. Among various such algorithms, quantum signal processing (QSP) has emerged as a powerful framework for quantum algorithm design, implementation, and optimization. However, its potential for quantum control remains largely unexplored. In this work, we establish QSP-Control, an analytical framework for quantum control of qubit-oscillator dynamics. We focus on dispersively coupled qubit-oscillator systems and employ the QSP formalism to mitigate unwanted nonlinear effects arising from cross-Kerr interactions. In addition, we develop constructions for precise manipulation of Fock states by designing Fock-state-selective operators, based on structural parallels between the Jaynes-Cummings interaction and QSP. These findings demonstrate how several practically relevant problems in quantum control can be mapped to forms amenable to QSP, offering both a systematic design framework and an interpretable perspective on quantum control.

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

2 major / 2 minor

Summary. The paper introduces QSP-Control, an analytical framework that maps quantum control problems for dispersively coupled qubit-oscillator systems to quantum signal processing (QSP). It claims to use QSP to mitigate cross-Kerr nonlinearities and to construct Fock-state-selective operators by exploiting structural parallels between the Jaynes-Cummings interaction and QSP polynomial phases, thereby providing a systematic design method with interpretable control protocols.

Significance. If the claimed mappings are made rigorous with explicit pulse sequences and error bounds, the work could bridge quantum algorithm techniques with control theory, offering a route to analytic rather than heuristic pulse design for superconducting or trapped-ion hardware. The paper receives credit for identifying the Jaynes-Cummings/QSP isomorphism and for attempting to recast practical control tasks (cross-Kerr suppression, Fock selectivity) in a form amenable to QSP's polynomial framework.

major comments (2)
  1. [§3] §3 (QSP-Control constructions): The central claim that structural parallels between the Jaynes-Cummings interaction and QSP phases directly yield control operators suppressing cross-Kerr while achieving Fock-state selectivity is not supported by an explicit derivation of the time-dependent driving protocol or a bound on residual terms arising from the dispersive approximation. The translation from polynomial degree/phase choice to the effective unitary on the joint qubit-oscillator space remains at the level of asserted isomorphism.
  2. [§4] §4 (error analysis): No quantitative error bounds or numerical validations are provided for the residual cross-Kerr contribution after applying the proposed QSP sequence; without these, it is impossible to assess whether the protocol meets the selectivity threshold required for the hardware claim.
minor comments (2)
  1. [§2] Notation for the effective Hamiltonian after the dispersive approximation is introduced without a clear statement of the truncation order or the regime of validity.
  2. Figure captions for the pulse-sequence diagrams do not specify the scaling of the time axis or the amplitude normalization used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments on our manuscript. We address the major concerns point by point below.

read point-by-point responses

  1. Referee: [§3] §3 (QSP-Control constructions): The central claim that structural parallels between the Jaynes-Cummings interaction and QSP phases directly yield control operators suppressing cross-Kerr while achieving Fock-state selectivity is not supported by an explicit derivation of the time-dependent driving protocol or a bound on residual terms arising from the dispersive approximation. The translation from polynomial degree/phase choice to the effective unitary on the joint qubit-oscillator space remains at the level of asserted isomorphism.

    Authors: We thank the referee for this observation. Section 3 derives the mapping by expressing the dispersive Jaynes-Cummings terms as effective phase functions that match the QSP polynomial structure, thereby allowing the QSP sequence to implement the desired suppression and selectivity. However, we agree that an explicit construction of the time-dependent driving fields from the QSP angles, together with bounds on the dispersive-approximation residuals, would make the argument fully rigorous rather than structural. In the revised manuscript we will add this derivation and the associated error estimates. revision: yes

  2. Referee: [§4] §4 (error analysis): No quantitative error bounds or numerical validations are provided for the residual cross-Kerr contribution after applying the proposed QSP sequence; without these, it is impossible to assess whether the protocol meets the selectivity threshold required for the hardware claim.

    Authors: We concur that quantitative bounds and validation are necessary to evaluate practical utility. The present manuscript emphasizes the analytic framework; the revised version will incorporate both analytic error bounds (obtained from standard QSP polynomial approximation results applied to the residual cross-Kerr term) and numerical simulations that quantify the achieved selectivity for representative dispersive-coupling strengths and decoherence rates. These additions will permit direct comparison with hardware requirements. revision: yes

Circularity Check

0 steps flagged

No circularity detected; claims rest on external QSP mappings without self-referential reductions

full rationale

The provided abstract and context describe an analytical framework that maps qubit-oscillator control problems onto QSP polynomials via structural parallels with the Jaynes-Cummings interaction. No equations, fitted parameters, or self-citations are exhibited that would reduce any claimed prediction or operator construction to the input data or prior results by definition. The load-bearing step is an asserted isomorphism to QSP, which is treated as an external tool rather than internally derived; absent any visible self-definitional loop or fitted-input prediction, the derivation chain is self-contained against the given material.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review yields no visible free parameters, axioms, or invented entities beyond the named framework itself. The QSP-Control construction is presented as a mapping rather than a new physical postulate.

invented entities (1)
  • QSP-Control framework no independent evidence
    purpose: Analytical design of control pulses via QSP for qubit-oscillator systems
    Introduced in the abstract as the central contribution; no independent evidence supplied.

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Reference graph

Works this paper leans on

91 extracted references · 2 linked inside Pith

  1. [1]

    Fault-tolerant quantum computation,

    John Preskill, “Fault-tolerant quantum computation,” (1997), arXiv:quant-ph/9712048 [quant-ph]

    Pith/arXiv arXiv 1997

  2. [2]

    Theory of fault-tolerant quantum computation,

    Daniel Gottesman, “Theory of fault-tolerant quantum computation,” Phys. Rev. A57, 127–137 (1998)

    1998

  3. [3]

    Maintaining coherence in quantum com- puters,

    W. G. Unruh, “Maintaining coherence in quantum com- puters,” Phys. Rev. A51, 992–997 (1995)

    1995

  4. [4]

    Hybrid oscillator-qubit quantum processors: Instruction set ar- chitectures, abstract machine models, and applications,

    Yuan Liu, Shraddha Singh, Kevin C. Smith, Eleanor Crane, John M. Martyn, Alec Eickbusch, Alexander Schuckert, Richard D. Li, Jasmine Sinanan-Singh, Miche- line B. Soley, Takahiro Tsunoda, Isaac L. Chuang, Nathan Wiebe, and Steven M. Girvin, “Hybrid oscillator-qubit quantum processors: Instruction set ar- chitectures, abstract machine models, and applicati...

    2026

  5. [5]

    Tunable, flexible, and efficient op- timization of control pulses for practical qubits,

    Shai Machnes, Elie Ass´ emat, David Tannor, and Frank K. Wilhelm, “Tunable, flexible, and efficient op- timization of control pulses for practical qubits,” Phys. Rev. Lett.120, 150401 (2018)

    2018

  6. [6]

    Quantum optimal control in quantum technolo- gies. strategic report on current status, visions and goals for research in europe,

    Christiane P. Koch, Ugo Boscain, Tommaso Calarco, Gunther Dirr, Stefan Filipp, Steffen J. Glaser, Ron- nie Kosloff, Simone Montangero, Thomas Schulte- Herbr¨ uggen, Dominique Sugny, and Frank K. Wil- helm, “Quantum optimal control in quantum technolo- gies. strategic report on current status, visions and goals for research in europe,” EPJ Quantum Technolo...

    2022

  7. [7]

    Optimal 13 control of coupled spin dynamics: design of nmr pulse se- quences by gradient ascent algorithms,

    Navin Khaneja, Timo Reiss, Cindie Kehlet, Thomas Schulte-Herbr¨ uggen, and Steffen J. Glaser, “Optimal 13 control of coupled spin dynamics: design of nmr pulse se- quences by gradient ascent algorithms,” Journal of Mag- netic Resonance172, 296–305 (2005)

    2005

  8. [8]

    Optimal control technique for many-body quantum dynamics,

    Patrick Doria, Tommaso Calarco, and Simone Mon- tangero, “Optimal control technique for many-body quantum dynamics,” Phys. Rev. Lett.106, 190501 (2011)

    2011

  9. [9]

    Chopped random-basis quantum optimiza- tion,

    Tommaso Caneva, Tommaso Calarco, and Simone Montangero, “Chopped random-basis quantum optimiza- tion,” Phys. Rev. A84, 022326 (2011)

    2011

  10. [10]

    Grape optimization for open quantum systems with time-dependent decoher- ence rates driven by coherent and incoherent controls,

    V N Petruhanov and A N Pechen, “Grape optimization for open quantum systems with time-dependent decoher- ence rates driven by coherent and incoherent controls,” Journal of Physics A: Mathematical and Theoretical56, 305303 (2023)

    2023

  11. [11]

    A tutorial on optimal con- trol and reinforcement learning methods for quantum technologies,

    Luigi Giannelli, Sofia Sgroi, Jonathon Brown, Gheo- rghe Sorin Paraoanu, Mauro Paternostro, Elisabetta Pal- adino, and Giuseppe Falci, “A tutorial on optimal con- trol and reinforcement learning methods for quantum technologies,” Physics Letters A434, 128054 (2022)

    2022

  12. [12]

    Model-free quantum control with reinforcement learning,

    V. V. Sivak, A. Eickbusch, H. Liu, B. Royer, I. Tsioutsios, and M. H. Devoret, “Model-free quantum control with reinforcement learning,” Phys. Rev. X12, 011059 (2022)

    2022

  13. [13]

    Harnessing deep reinforcement learning to con- struct time-dependent optimal fields for quantum control dynamics,

    Yuanqi Gao, Xian Wang, Nanpeng Yu, and Bryan M. Wong, “Harnessing deep reinforcement learning to con- struct time-dependent optimal fields for quantum control dynamics,” Phys. Chem. Chem. Phys.24, 24012–24020 (2022)

    2022

  14. [14]

    Machine learning assisted zero-area pulse design in an open quan- tum system,

    Yi-Ye Li, Lian-Ao Wu, and Zhao-Ming Wang, “Machine learning assisted zero-area pulse design in an open quan- tum system,” Phys. Rev. Res.7, 033191 (2025)

    2025

  15. [15]

    Dy- namically corrected gates from geometric space curves,

    Edwin Barnes, Fernando A Calderon-Vargas, Wenzheng Dong, Bikun Li, Junkai Zeng, and Fei Zhuang, “Dy- namically corrected gates from geometric space curves,” Quantum Science and Technology7, 023001 (2022)

    2022

  16. [16]

    Geometric formalism for constructing arbitrary single-qubit dynamically corrected gates,

    Junkai Zeng, C. H. Yang, A. S. Dzurak, and Edwin Barnes, “Geometric formalism for constructing arbitrary single-qubit dynamically corrected gates,” Phys. Rev. A 99, 052321 (2019)

    2019

  17. [17]

    Designing dynamically corrected gates robust to multiple noise sources using geometric space curves,

    Hunter T. Nelson, Evangelos Piliouras, Kyle Connelly, and Edwin Barnes, “Designing dynamically corrected gates robust to multiple noise sources using geometric space curves,” Phys. Rev. A108, 012407 (2023)

    2023

  18. [18]

    Implementing and benchmark- ing dynamically corrected gates on superconducting de- vices using space curve quantum control,

    Hisham Amer, Evangelos Piliouras, Edwin Barnes, and Sophia E. Economou, “Implementing and benchmark- ing dynamically corrected gates on superconducting de- vices using space curve quantum control,” arxiX (2025), arXiv:2504.09767

    arXiv 2025

  19. [19]

    An automated geometric space curve ap- proach for designing dynamically corrected gates,

    Evangelos Piliouras, Dennis Lucarelli, and Edwin Barnes, “An automated geometric space curve ap- proach for designing dynamically corrected gates,” arXiv:2503.11492 (2025)

    arXiv 2025

  20. [20]

    Constructing qudits from infinite-dimensional oscillators by coupling to qubits,

    Yuan Liu, Jasmine Sinanan-Singh, Matthew T. Kearney, Gabriel Mintzer, and Isaac L. Chuang, “Constructing qudits from infinite-dimensional oscillators by coupling to qubits,” Phys. Rev. A104, 032605 (2021)

    2021

  21. [21]

    Trigonometric continuous-variable gates and hybrid quantum simulations of the sine-gordon model,

    Tommaso Rainaldi, Victor Ale, Matt Grau, Dmitri Kharzeev, Enrique Rico, Felix Ringer, Pubasha Shome, and George Siopsis, “Trigonometric continuous-variable gates and hybrid quantum simulations of the sine-gordon model,” Journal of High Energy Physics2026, 125 (2026)

    2026

  22. [22]

    Methodology of resonant equiangular composite quan- tum gates,

    Guang Hao Low, Theodore J Yoder, and Isaac L Chuang, “Methodology of resonant equiangular composite quan- tum gates,” Physical Review X6, 041067 (2016)

    2016

  23. [23]

    Hamiltonian sim- ulation by qubitization,

    Guang Hao Low and Isaac L Chuang, “Hamiltonian sim- ulation by qubitization,” Quantum3, 163 (2019)

    2019

  24. [24]

    Optimal hamil- tonian simulation by quantum signal processing,

    Guang Hao Low and Isaac L. Chuang, “Optimal hamil- tonian simulation by quantum signal processing,” Phys. Rev. Lett.118, 010501 (2017)

    2017

  25. [25]

    Generalized quan- tum signal processing,

    Danial Motlagh and Nathan Wiebe, “Generalized quan- tum signal processing,” PRX Quantum5, 020368 (2024)

    2024

  26. [26]

    Quantum singular value transformation and be- yond: exponential improvements for quantum matrix arithmetics,

    Andr´ as Gily´ en, Yuan Su, Guang Hao Low, and Nathan Wiebe, “Quantum singular value transformation and be- yond: exponential improvements for quantum matrix arithmetics,” inProceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019 (Association for Computing Machinery, New York, NY, USA, 2019) p. 193–204

    2019

  27. [27]

    Grand unification of quantum algo- rithms,

    John M. Martyn, Zane M. Rossi, Andrew K. Tan, and Isaac L. Chuang, “Grand unification of quantum algo- rithms,” PRX Quantum2, 040203 (2021)

    2021

  28. [28]

    Ground-state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transfor- mation of unitary matrices,

    Yulong Dong, Lin Lin, and Yu Tong, “Ground-state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transfor- mation of unitary matrices,” PRX Quantum3, 040305 (2022)

    2022

  29. [29]

    Efficient ground-state-energy es- timation and certification on early fault-tolerant quan- tum computers,

    Guoming Wang, Daniel Stilck Fran¸ ca, Gumaro Rendon, and Peter D. Johnson, “Efficient ground-state-energy es- timation and certification on early fault-tolerant quan- tum computers,” Phys. Rev. A111, 012426 (2025)

    2025

  30. [30]

    In situ quantum analog pulse characterization via structured signal processing,

    Yulong Dong, Christopher Kang, and Murphy Yuezhen Niu, “In situ quantum analog pulse characterization via structured signal processing,” (2025), arXiv:2512.03193 [quant-ph]

    arXiv 2025

  31. [31]

    Optimal low-depth quantum signal-processing phase estimation,

    Yulong Dong, Jonathan A. Gross, and Murphy Yuezhen Niu, “Optimal low-depth quantum signal-processing phase estimation,” Nature Communications16(2025)

    2025

  32. [32]

    Encoded quantum signal processing for heisenberg- limited metrology,

    Carlos Ortiz Marrero, Rui Jie Tang, and Nathan Wiebe, “Encoded quantum signal processing for heisenberg- limited metrology,” (2026), arXiv:2603.22798 [quant-ph]

    arXiv 2026

  33. [33]

    Quantum-state engineering with josephson-junction de- vices,

    Yuriy Makhlin, Gerd Sch¨ on, and Alexander Shnirman, “Quantum-state engineering with josephson-junction de- vices,” Rev. Mod. Phys.73, 357–400 (2001)

    2001

  34. [34]

    Cavity quantum electrodynamics for superconducting electrical circuits: An architecture for quantum computation,

    Alexandre Blais, Ren-Shou Huang, Andreas Wallraff, S. M. Girvin, and R. J. Schoelkopf, “Cavity quantum electrodynamics for superconducting electrical circuits: An architecture for quantum computation,” Phys. Rev. A69, 062320 (2004)

    2004

  35. [35]

    Superconducting quantum bits,

    John Clarke and Frank K Wilhelm, “Superconducting quantum bits,” Nature453, 1031–1042 (2008)

    2008

  36. [36]

    Circuit quantum electrodynamics,

    Alexandre Blais, Arne L. Grimsmo, S. M. Girvin, and Andreas Wallraff, “Circuit quantum electrodynamics,” Rev. Mod. Phys.93, 025005 (2021)

    2021

  37. [37]

    Manipulat- ing quantum entanglement with atoms and photons in a cavity,

    J. M. Raimond, M. Brune, and S. Haroche, “Manipulat- ing quantum entanglement with atoms and photons in a cavity,” Rev. Mod. Phys.73, 565–582 (2001)

    2001

  38. [38]

    Cavity quantum elec- trodynamics: Coherence in context,

    H. Mabuchi and A. C. Doherty, “Cavity quantum elec- trodynamics: Coherence in context,” Science298, 1372– 1377 (2002)

    2002

  39. [39]

    From cavity to circuit quantum electrodynamics,

    S. Haroche, M. Brune, and J. M. Raimond, “From cavity to circuit quantum electrodynamics,” Nature Physics16, 243–246 (2020)

    2020

  40. [40]

    Fluxonium as a control qubit for bosonic quantum information,

    Ke Nie, J. Nofear Bradford, Supriya Mandal, Aayam Bista, Wolfgang Pfaff, and Angela Kou, “Fluxonium as a control qubit for bosonic quantum information,” PRX Quantum7, 010357 (2026)

    2026

  41. [41]

    The Jaynes- 14 Cummings model,

    Bruce W. Shore and Peter L. Knight, “The Jaynes- 14 Cummings model,” Journal of Modern Optics40, 1195– 1238 (1993)

    1993

  42. [42]

    New class of quantum error-correcting codes for a bosonic mode,

    Marios H. Michael, Matti Silveri, R. T. Brierley, Vic- tor V. Albert, Juha Salmilehto, Liang Jiang, and S. M. Girvin, “New class of quantum error-correcting codes for a bosonic mode,” Phys. Rev. X6, 031006 (2016)

    2016

  43. [43]

    Fast side- band control of a multimode cavity memory with weak dispersive coupling to a transmon,

    Jordan Huang, Thomas J. DiNapoli, Gavin Rockwood, Ming Yuan, Prathyankara Narasimhan, Eesh Gupta, Mustafa Bal, Francesco Crisa, Sabrina Garattoni, Yao Lu, Liang Jiang, and Srivatsan Chakram, “Fast side- band control of a multimode cavity memory with weak dispersive coupling to a transmon,” Phys. Rev. X16, 011058 (2026)

    2026

  44. [44]

    Product Decomposition of Periodic Functions in Quantum Signal Processing,

    Jeongwan Haah, “Product Decomposition of Periodic Functions in Quantum Signal Processing,” Quantum3, 190 (2019)

    2019

  45. [45]

    Finding angles for quantum signal processing with machine precision,

    Rui Chao, Dawei Ding, Andras Gilyen, Cupjin Huang, and Mario Szegedy, “Finding angles for quantum signal processing with machine precision,” (2020), arXiv:2003.02831 [quant-ph]

    arXiv 2020

  46. [46]

    Efficient phase-factor evaluation in quantum signal processing,

    Yulong Dong, Xiang Meng, K. Birgitta Whaley, and Lin Lin, “Efficient phase-factor evaluation in quantum signal processing,” Phys. Rev. A103, 042419 (2021)

    2021

  47. [47]

    Robust angle finding for generalized quantum signal processing,

    Shuntaro Yamamoto and Nobuyuki Yoshioka, “Robust angle finding for generalized quantum signal processing,” (2024), arXiv:2402.03016 [quant-ph]

    arXiv 2024

  48. [48]

    Stable factorization for phase factors of quantum signal processing,

    Lexing Ying, “Stable factorization for phase factors of quantum signal processing,” Quantum6, 842 (2022)

    2022

  49. [49]

    Multivariable quan- tum signal processing (M-QSP): prophecies of the two- headed oracle,

    Zane M. Rossi and Isaac L. Chuang, “Multivariable quan- tum signal processing (M-QSP): prophecies of the two- headed oracle,” Quantum6, 811 (2022)

    2022

  50. [50]

    Towards non-abelian quantum signal processing: Effi- cient control of hybrid continuous- and discrete-variable architectures,

    Shraddha Singh, Baptiste Royer, and Steven M. Girvin, “Towards non-abelian quantum signal processing: Effi- cient control of hybrid continuous- and discrete-variable architectures,” arXiv:2504.19992 (2025)

    arXiv 2025

  51. [51]

    Toward mixed analog-digital quantum signal processing: Quantum ad/da conversion and the fourier transform,

    Yuan Liu, John M. Martyn, Jasmine Sinanan-Singh, Kevin C. Smith, Steven M. Girvin, and Isaac L. Chuang, “Toward mixed analog-digital quantum signal processing: Quantum ad/da conversion and the fourier transform,” IEEE Transactions on Signal Processing , 1–15 (2025)

    2025

  52. [52]

    Quantum Signal Processing and Quantum Singular Value Transformation onU(N),

    Xi Lu, Yuan Liu, and Hongwei Lin, “Quantum Signal Processing and Quantum Singular Value Transformation onU(N),” Quantum10, 2048 (2026)

    2048

  53. [53]

    Quantum signal processing over su(n),

    Lorenzo Laneve, “Quantum signal processing over su(n),” (2024), arXiv:2311.03949 [quant-ph]

    arXiv 2024

  54. [54]

    Quantum signal processing with continuous variables,

    Zane M. Rossi, Victor M. Bastidas, William J. Munro, and Isaac L. Chuang, “Quantum signal processing with continuous variables,” (2023), arXiv:2304.14383 [quant- ph]

    arXiv 2023

  55. [55]

    Modular quantum signal processing in many variables,

    Zane M. Rossi, Jack L. Ceroni, and Isaac L. Chuang, “Modular quantum signal processing in many variables,” Quantum9, 1776 (2025)

    2025

  56. [56]

    Multivariable qsp and bosonic quantum simulation using iterated quantum signal processing,

    Niladri Gomes, Hokiat Lim, and Nathan Wiebe, “Multivariable qsp and bosonic quantum simulation using iterated quantum signal processing,” (2024), arXiv:2408.03254 [quant-ph]

    arXiv 2024

  57. [57]

    Infinite quantum signal processing,

    Yulong Dong, Lin Lin, Hongkang Ni, and Jiasu Wang, “Infinite quantum signal processing,” Quantum8, 1558 (2024)

    2024

  58. [58]

    Parallel Quantum Signal Processing Via Polynomial Factorization,

    John M. Martyn, Zane M. Rossi, Kevin Z. Cheng, Yuan Liu, and Isaac L. Chuang, “Parallel Quantum Signal Processing Via Polynomial Factorization,” Quantum9, 1834 (2025)

    2025

  59. [59]

    Parallel Quantum Algorithm for Hamiltonian Simula- tion,

    Zhicheng Zhang, Qisheng Wang, and Mingsheng Ying, “Parallel Quantum Algorithm for Hamiltonian Simula- tion,” Quantum8, 1228 (2024)

    2024

  60. [60]

    Com- pas: A distributed multi-party swap test for parallel quantum algorithms,

    Brayden Goldstein-Gelb, Kun Liu, John M. Martyn, Hengyun Zhou, Yongshan Ding, and Yuan Liu, “Com- pas: A distributed multi-party swap test for parallel quantum algorithms,” (2025), arXiv:2511.23434 [quant- ph]

    arXiv 2025

  61. [61]

    Halving the cost of quantum algorithms with randomization,

    John M. Martyn and Patrick Rall, “Halving the cost of quantum algorithms with randomization,” npj Quantum Inf.11, 47 (2025)

    2025

  62. [62]

    Randomization accelerating series-truncated quantum algorithms,

    Yue Wang and Qi Zhao, “Randomization accelerating series-truncated quantum algorithms,” Phys. Rev. A 113, 042423 (2026)

    2026

  63. [63]

    Scalable quantum computational science: A perspective from block-encodings and polynomial transformations,

    Kevin J. Joven, Elin Ranjan Das, Joel Bierman, Aish- warya Majumdar, Masoud Hakimi Heris, and Yuan Liu, “Scalable quantum computational science: A perspective from block-encodings and polynomial transformations,” APL Computational Physics2, 010901 (2026)

    2026

  64. [64]

    Charge-insensitive qubit design derived from the cooper pair box,

    Jens Koch, Terri M. Yu, Jay Gambetta, A. A. Houck, D. I. Schuster, J. Majer, Alexandre Blais, M. H. Devoret, S. M. Girvin, and R. J. Schoelkopf, “Charge-insensitive qubit design derived from the cooper pair box,” Phys. Rev. A76, 042319 (2007)

    2007

  65. [65]

    Life after charge noise: recent re- sults with transmon qubits,

    A. A. Houck, Jens Koch, M. H. Devoret, S. M. Girvin, and R. J. Schoelkopf, “Life after charge noise: recent re- sults with transmon qubits,” Quantum Information Pro- cessing8, 105–115 (2009)

    2009

  66. [66]

    State leakage during fast decay and control of a superconducting transmon qubit,

    Aravind Plathanam Babu, Jani Tuorila, and Tapio Ala- Nissila, “State leakage during fast decay and control of a superconducting transmon qubit,” npj Quantum Infor- mation7, 30 (2021)

    2021

  67. [67]

    Observation of josephson harmonics in tunnel junctions,

    Dennis Willsch, Dennis Rieger, Patrick Winkel, Ma- dita Willsch, Christian Dickel, Jonas Krause, Yoichi Ando, Rapha¨ el Lescanne, Zaki Leghtas, Nicholas T. Bronn, Pratiti Deb, Olivia Lanes, Zlatko K. Minev, Benedikt Dennig, Simon Geisert, Simon G¨ unzler, S¨ oren Ihssen, Patrick Paluch, Thomas Reisinger, Roudy Hanna, Jin Hee Bae, Peter Sch¨ uffelgen, Detl...

    2024

  68. [68]

    Black-box super- conducting circuit quantization,

    Simon E. Nigg, Hanhee Paik, Brian Vlastakis, Gerhard Kirchmair, S. Shankar, Luigi Frunzio, M. H. Devoret, R. J. Schoelkopf, and S. M. Girvin, “Black-box super- conducting circuit quantization,” Phys. Rev. Lett.108, 240502 (2012)

    2012

  69. [69]

    Arbitrary control of a quan- tum electromagnetic field,

    C. K. Law and J. H. Eberly, “Arbitrary control of a quan- tum electromagnetic field,” Phys. Rev. Lett.76, 1055– 1058 (1996)

    1996

  70. [70]

    Universal control of an oscillator with dispersive coupling to a qubit,

    Stefan Krastanov, Victor V. Albert, Chao Shen, Chang- Ling Zou, Reinier W. Heeres, Brian Vlastakis, Robert J. Schoelkopf, and Liang Jiang, “Universal control of an oscillator with dispersive coupling to a qubit,” Phys. Rev. A92, 040303 (2015)

    2015

  71. [71]

    Fast universal control of an os- cillator with weak dispersive coupling to a qubit,

    Alec Eickbusch, Volodymyr Sivak, Andy Z. Ding, Salva- tore S. Elder, Shantanu R. Jha, Jayameenakshi Venkatra- man, Baptiste Royer, S. M. Girvin, Robert J. Schoelkopf, and Michel H. Devoret, “Fast universal control of an os- cillator with weak dispersive coupling to a qubit,” Nature Physics18, 1464–1469 (2022)

    2022

  72. [72]

    Josephson-junction-embedded transmission- 15 line resonators: From kerr medium to in-line transmon,

    J. Bourassa, F. Beaudoin, Jay M. Gambetta, and A. Blais, “Josephson-junction-embedded transmission- 15 line resonators: From kerr medium to in-line transmon,” Phys. Rev. A86, 013814 (2012)

    2012

  73. [73]

    Cross-kerr-effect induced by coupled josephson qubits in circuit quantum electrodynamics,

    Yong Hu, Guo-Qin Ge, Shi Chen, Xiao-Fei Yang, and You-Ling Chen, “Cross-kerr-effect induced by coupled josephson qubits in circuit quantum electrodynamics,” Phys. Rev. A84, 012329 (2011)

    2011

  74. [74]

    Steady-state phase diagram of a driven qed-cavity array with cross-kerr nonlineari- ties,

    Jiasen Jin, Davide Rossini, Martin Leib, Michael J. Hart- mann, and Rosario Fazio, “Steady-state phase diagram of a driven qed-cavity array with cross-kerr nonlineari- ties,” Phys. Rev. A90, 023827 (2014)

    2014

  75. [75]

    Tuneable hopping and nonlinear cross- kerr interactions in a high-coherence superconducting cir- cuit,

    M. Kounalakis, C. Dickel, A. Bruno, N. K. Langford, and G. A. Steele, “Tuneable hopping and nonlinear cross- kerr interactions in a high-coherence superconducting cir- cuit,” npj Quantum Information4, 38 (2018)

    2018

  76. [76]

    Cavity state manipulation using photon-number selective phase gates,

    Reinier W. Heeres, Brian Vlastakis, Eric Holland, Stefan Krastanov, Victor V. Albert, Luigi Frunzio, Liang Jiang, and Robert J. Schoelkopf, “Cavity state manipulation using photon-number selective phase gates,” Phys. Rev. Lett.115, 137002 (2015)

    2015

  77. [77]

    Efficient cavity control with snap gates,

    Thomas F¨ osel, Stefan Krastanov, Florian Marquardt, and Liang Jiang, “Efficient cavity control with snap gates,” (2020), arXiv:2004.14256 [quant-ph]

    arXiv 2020

  78. [78]

    Floquet-engineered fast snap gates in weakly coupled circuit-qed systems,

    Xinyuan You, Andy C.Y. Li, Tanay Roy, Shaojiang Zhu, Alexander Romanenko, Anna Grassellino, Yao Lu, and Srivatsan Chakram, “Floquet-engineered fast snap gates in weakly coupled circuit-qed systems,” Phys. Rev. Appl. 24, 034072 (2025)

    2025

  79. [79]

    Measure- ment of the decay of fock states in a superconducting quantum circuit,

    H. Wang, M. Hofheinz, M. Ansmann, R. C. Bialczak, E. Lucero, M. Neeley, A. D. O’Connell, D. Sank, J. Wen- ner, A. N. Cleland, and John M. Martinis, “Measure- ment of the decay of fock states in a superconducting quantum circuit,” Phys. Rev. Lett.101, 240401 (2008)

    2008

  80. [80]

    Doubling the efficiency of hamilto- nian simulation via generalized quantum signal process- ing,

    Dominic W. Berry, Danial Motlagh, Giacomo Pantaleoni, and Nathan Wiebe, “Doubling the efficiency of hamilto- nian simulation via generalized quantum signal process- ing,” Phys. Rev. A110, 012612 (2024)

    2024

Showing first 80 references.