Pith. sign in

REVIEW 2 major objections 5 minor 57 references

GPU-Accelerated Host-Aware Dead-Measurement Detection in Hybrid Quantum--Classical Programs: Full Version

T0 review · 2 major / 5 minor · reviewed 2026-07-14 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A sound host-side analysis finds measurement outcomes that do not affect a hybrid program's return, enabling removal of gates circuit optimizers miss.

desk verdict Solid host-side abstract interpretation for semantic dead measurements, with real proofs and orthogonal gate savings; the ~38%/30% numbers are recovery of planted host deadness more than spontaneous organic opportunity. read the letter →

arxiv 2607.09976 v1 pith:YHW2BWWB submitted 2026-07-10 quant-ph cs.ETcs.PL

Yanbin Chen , Qunyou Liu , Yu Wang , Christian B. Mendl , Helmut Seidl This is my paper · ORCID
classification quant-phcs.ETcs.PL
keywords hybridquantum-classicalprogramsdead-measurementdetectionabstractinterpretationdeadgateeliminationhost-sidestaticanalysislevelizedSSAGPUaccelerationcircuitoptimization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Hybrid quantum-classical programs feed measurement bits into a classical host that computes a final answer. Some of those bits are read in the source yet never change what the host returns, so the gates that only shape those bits are wasted. Circuit-only optimizers cannot see this host-level irrelevance. The paper gives a sound abstract-interpretation analysis of the host that tracks exact polynomial forms when possible and conservative dependence sets otherwise, then marks measurement inputs that do not reach the return. On 24 application-faithful workloads from chemistry, optimization, machine learning, and finance, the analysis finds more than four times as many dead measurements as ordinary syntactic liveness and, by itself, removes about 38 percent of total gates on average. Even after Qiskit, t|ket, or PyZX have already optimized the same circuits, the same host information still removes more than 30 percent of the remaining gates. To keep the analysis practical, the host is lowered to a levelized SSA form whose independent levels run on a GPU, preserving the analysis result and yielding up to 6.53 times speedup over a sequential baseline.

What carries the argument

Abstract interpretation over a polynomial-or-dependence domain: each host value is either an exact multivariate polynomial over initial measurement symbols or a conservative symbol set, with special rules for truncation, random, pure calls, and control merges; non-contributory measurements are those whose symbols never appear in the final return or control-dependency set.

What would settle it

Take any of the 24 application workloads, replace the host with one that actually depends on a measurement the analysis declared non-contributory, re-run the analysis-plus-DGE pipeline, and check whether the returned distribution changes or the claimed gate reduction disappears.

Watch

Extended reading notes

Core claim

A sound host-side abstract interpretation can identify measurement outcomes that are syntactically used but semantically non-contributory to the host return; feeding those outcomes into dead-gate elimination removes gates that circuit-local optimizers leave behind, both alone and after Qiskit, t|ket, and PyZX, and the same result is preserved by an SSA-levelized GPU backend.

Load-bearing premise

The classical host is assumed to be a loop-free structured program with pure side-effect-free calls and measurement-independent random bounds; hosts with loops, recursion, or impure library code fall outside the implemented analysis.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper presents a host-side abstract interpretation that identifies measurement outcomes which are syntactically used by a classical host but semantically non-contributory to the host return, then uses that dead set to drive dead-gate elimination on the quantum circuit. The analysis tracks polynomial and dependence-form abstract values plus a control-contribution set C_ctrl, is proved sound via a description relation and transfer lemmas (Thms A.1–A.2, Lemmas A.1–A.2), and is shown to preserve its result under a levelized SSA lowering for GPU execution (Prop. A.1). On 24 application-faithful hybrid workloads the method finds >4× as many non-contributory measurements as syntactic liveness, removes 37.98% of total gates standalone, and still removes >30% after Qiskit, t|ket⟩, and PyZX (Table 1; §3.1). A CUDA backend yields up to 6.53× speedup once structural parallelism is exposed.

Significance. If the results hold, the paper supplies a missing host-semantic layer for hybrid quantum–classical compilation: circuit-local optimizers cannot see that a measured outcome is neutralized by classical post-processing, and the paper both formalizes that notion and shows large residual gate reductions after SOTA circuit optimizers. Strengths include machine-checkable-style soundness proofs for the abstract interpretation and the SSA lowering, a clear syntactic-liveness baseline, control workloads for false-positive hygiene, and a reproducible GPU acceleration path with measured speedups. The work is therefore a solid methods contribution for hybrid-program optimization, provided the evaluation’s representativeness is stated carefully.

major comments (2)
  1. §3.1 and Appendix A.3: the headline reductions (Table 1: 37.98% standalone, >30% post-optimizer) rest on 24 hosts whose deadness is largely planted by upstream artifacts (screened Hamiltonian tails, frozen orbitals, zero QUBO coefficients, zero readout weights, zero exposures, filtered scenarios, etc.). These are realistic application patterns, but they are also exactly the cancellations the abstract domain is engineered to catch. The paper therefore measures recovery of designed host-semantic deadness more than spontaneous deadness in uncurated hybrid code. The central claim should be rephrased as opportunity under these application patterns, or the evaluation should include at least a small set of organic/unmodified host programs so the production-facing percentages are not overstated.
  2. §2 Scope and assumptions: the implemented host language is a loop-free structured fragment (skip, assignment, return, sequencing, if-then-else) with total pure side-effect-free calls and measurement-independent random bounds. Real hybrid hosts often contain loops, recursion, mutable state, or impure library calls; the paper only sketches CFG fixpoints/unrolling as future work. Effectiveness claims in §3.1 and the abstract therefore do not transfer to that broader host class. This restriction should be stated as a hard limitation of the evaluation, not only as an implementation note.
minor comments (5)
  1. Remark 1 notes that branch-local constraints are not propagated; a short discussion of how often this causes extra conservatism on the 24 workloads would help readers gauge precision loss.
  2. Table 2 aggregates semantic-only dead measurements by domain; a per-workload breakdown (or appendix table) would make the 4× claim easier to audit.
  3. Figure 5 reports GPU speedups including CPU-side lowering; stating the fraction of time spent in lowering vs. kernel execution would clarify when the backend is worthwhile.
  4. The abstract and §3.1 use both 37.98% and “around 38%”; pick one form for consistency.
  5. Minor typos: “by by either” (p. 5), “thehost-sidestaticanalysis” spacing issues in the introduction, and occasional missing spaces after commas in the appendix.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: soundness, non-contribution, and measured gate reductions are independent of fitted inputs or load-bearing self-citation chains.

full rationale

This is a methods/evaluation paper whose central claims do not reduce to their inputs by construction. Semantic non-contribution is defined independently (Obs/Mnc from abstract return support and C_ctrl; §2.4), and soundness is proved by a standard abstract-interpretation argument (description relation Δ, transfer soundness AI-S, Theorems A.1–A.2 in §A.4) without importing a uniqueness theorem or fitted parameter. Empirical percentages (37.98% standalone, >30% post-Qiskit/t|ket⟩/PyZX; Table 1) are measured reductions after DGE pruning, not predictions forced by a fit. The syntactic-liveness baseline and control workloads (Tables 2–3) are external checks, not self-referential. Self-citations to the authors’ prior DGE [11] and quantum constant propagation [12] appear only as related work or as the circuit-pruning backend that consumes an already-computed dead set; they do not justify the host analysis itself. The SSA lowering preservation (Proposition A.1) is a simulation proof, not a renaming of a known empirical pattern. Workload construction from upstream screening artifacts may affect representativeness, but that is an evaluation-scope concern, not circularity of the derivation chain. Score 0 with empty steps is the honest finding.

Assumptions & free parameters 2 free parameters · 6 assumptions · 2 invented entities

The central claims rest on a restricted host semantics, standard abstract-interpretation soundness methodology, purity of calls, and author-constructed hybrid workloads. There are essentially no fitted physical constants; free knobs appear only in synthetic GPU microbenchmarks, not in the effectiveness percentages. Invented constructs are analysis artifacts (abstract domain, C_ctrl, levelized SSA), not new physical entities.

free parameters (2)
  • structural_parallelism_p and cross-chain_χ (GPU synthetic hosts)
    Used only to generate synthetic host programs for GPU speedup curves (§3.2); not inputs to the 37.98%/~31% effectiveness claims.
  • polynomial size N and dependence-set size |D| bounds
    Complexity corollaries assume max(N,|V|,|D|) constant for O(n) analysis; these are modeling bounds, not data fits, but they condition the linear-time claim.
assumptions (6)
  • domain assumption Host programs are in the loop-free fragment S ::= skip | x:=e | return e | S;S | if(e) then S else S, with ⊙ ∈ {+,-,×,÷,%}, int toward zero, and random(L,U) with measurement-independent constant bounds.
    Stated in §2 Scope; loops/recursion out of implementation scope.
  • domain assumption Function calls in expressions are total, pure, and side-effect free; return depends only on arguments.
    §2 Scope and function-call transfer rule; enables conservative dependency union.
  • domain assumption Measurement-bound variables in M are binary with bounds [0,1] at initialization.
    InitAbsState (Alg A.3); used for Dom/B and int-carrier examples.
  • standard math Abstract interpretation over-approximates concrete measurement dependencies via description relation Δ; reported M_nc are therefore non-contributory.
    Standard AI soundness pattern; proved in A.4 (Thm A.1–A.2).
  • domain assumption A gate whose forward influence reaches only qubits mapped to non-contributory measurements can be removed without changing host-observable behavior (DGE).
    Integration §2.7 and citation to prior DGE [11]; host analysis supplies the dead set DGE needs.
  • ad hoc to paper Application-faithful workloads (screened Hamiltonians, QUBO presolve, sparse readouts, zero exposures, etc.) are representative enough to support the mean reduction claims.
    §3.1 and A.3 construct hosts from upstream-style artifacts on MQTBench kernels; not production traces.
invented entities (2)
  • Polynomial/dependence abstract value domain with C_ctrl control-contribution set and Obs/M_nc
    purpose: Track exact symbolic contribution when possible and conservative dependencies otherwise; decide non-contributory measurements.
    Core analysis design (§2.1–2.4); standard AI style, not a physical postulate. independent_evidence false as it is an analysis construct validated only by proofs and author workloads.
  • Levelized SSA IR for host analysis (levels L_k with earliest-ready scheduling)
    purpose: Expose level-wise independent abstract-transfer tasks for CUDA SIMT execution while preserving analysis results.
    §2.9 and Prop A.1; engineering representation for GPU, not an external entity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GPU-Accelerated Host-Aware Dead-Measurement Detection in Hybrid Quantum--Classical Programs: Full Version." pith.science (2026). https://pith.science/paper/YHW2BWWB

@misc{pith2026260709976,
  author       = {Pith},
  title        = {Pith review of: GPU-Accelerated Host-Aware Dead-Measurement Detection in Hybrid Quantum--Classical Programs: Full Version},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YHW2BWWB}},
  note         = {Machine review of arXiv:2607.09976}
}
abstract

Hybrid programs combine a quantum circuit with a classical host program that consumes measurement outcomes. In such programs, an outcome may be syntactically read by the host but semantically non-contributory: changing the outcome cannot change the returned value. Such outcomes obscure gates that are dead only relative to the host semantics, and are therefore invisible to circuit-local optimizers. We present a semantics-aware host-side static analysis that identifies non-contributory measurement outcomes by abstract interpretation, and prove its soundness. We implement the analysis and evaluate it on $24$ application-faithful hybrid workloads across quantum chemistry, optimization, quantum machine learning, and quantum finance. Compared with a syntactic liveness baseline, our analysis identifies more than $4\times$ as many non-contributory measurements, and it standalone enables the removal of $37.98\%$ of total gates on average. Even after the state-of-the-art optimizers like Qiskit, t|ket$\rangle$, and PyZX have already optimized the circuits, our analysis still enables removal of more than $30\%$ of the post-optimized gates, showing that the host-semantic opportunities exposed by our analysis are not subsumed by circuit-local optimization. To scale our analysis, we further lower host programs to an SSA-style levelized intermediate representation that exposes level-wise parallelism for GPU execution, and implement a CUDA backend. We prove that this lowering preserves the analysis result, and the evaluation shows speedups of up to $6.53\times$ over a sequential baseline as structural parallelism increases.

Figures

Figures reproduced from arXiv: 2607.09976 by the authors.

Figure 1
Figure 1. A hybrid program where the host variable [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overview of our optimization pass. Given a hybrid quantum program, we [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Per-workload total-gate reduction for the main effectiveness workloads. [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Illustration of active chains in input programs. [PITH_FULL_IMAGE:figures/full_fig_p021_4.png]
Figure 5
Figure 5. Figure 5: GPU acceleration results for host-side static analysis. [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]
Figure 6
Figure 6. Figure 6: Diagnostic workload one: a toy hybrid program similar to the Fig. 1. [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]
Figure 7
Figure 7. Figure 7: Diagnostic workload two: a VQE-style estimator/update kernel. The [PITH_FULL_IMAGE:figures/full_fig_p024_7.png]
Figure 8
Figure 8. Figure 8: Diagnostic workload three: a QPE kernel where the counting register [PITH_FULL_IMAGE:figures/full_fig_p024_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

57 extracted references · 4 canonical work pages

  1. [1]

    github.io/qiskit-nature/stubs/qiskit_nature.second_q.transformers

    Qiskit Nature: Freezecoretransformer.https://qiskit-community. github.io/qiskit-nature/stubs/qiskit_nature.second_q.transformers. FreezeCoreTransformer.html(2024)

    2024

  2. [2]

    Quantum7, 989 (Apr 2023).https://doi.org/10.22331/q-2023-04-27-989,https://doi.org/ 10.22331/q-2023-04-27-989

    Abedi, E., Beigi, S., Taghavi, L.: Quantum Lazy Training. Quantum7, 989 (Apr 2023).https://doi.org/10.22331/q-2023-04-27-989,https://doi.org/ 10.22331/q-2023-04-27-989

    work page doi:10.22331/q-2023-04-27-989 2023

  3. [3]

    Pearson, 2 edn

    Aho, A.V., Lam, M.S., Sethi, R., Ullman, J.D.: Compilers: Principles, Techniques, and Tools. Pearson, 2 edn. (2007)

    2007

  4. [4]

    ACM Transactions on Programming Languages and Systems36(1) (2014).https://doi.org/10.1145/2579080

    Barthe, G., Demange, D., Pichardie, D.: Formal verification of an SSA-based middle-end for CompCert. ACM Transactions on Programming Languages and Systems36(1) (2014).https://doi.org/10.1145/2579080

    work page doi:10.1145/2579080 2014

  5. [5]

    Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Ahmed, S., Ajith, V., Alam, M.S., Alonso-Linaje, G., AkashNarayanan, B., Asadi, A., Arrazola, J.M., Azad, U., Ban- ning, S., Blank, C., Bromley, T.R., Cordier, B.A., Ceroni, J., Delgado, A., Matteo, O.D., Dusko, A., Garg, T., Guala, D., Hayes, A., Hill, R., Ijaz, A., Isacsson, T., Ittah, D., Jahangiri, S.,...

    arXiv 2022

  6. [6]

    In: 2023 IEEE International Conference on Quan- tum Computing and Engineering (QCE)

    Bermot, E., Zoufal, C., Grossi, M., Schuhmacher, J., Tacchino, F., Vallecorsa, S., Tavernelli, I.: Quantum Generative Adversarial Networks For Anomaly Detec- tion In High Energy Physics . In: 2023 IEEE International Conference on Quan- tum Computing and Engineering (QCE). pp. 331–341. IEEE Computer Society, Los Alamitos, CA, USA (Sep 2023).https://doi.org...

    doi:10.1109/qce57702.2023 2023

  7. [7]

    Bouland, A., van Dam, W., Joorati, H., Kerenidis, I., Prakash, A.: Prospects and challenges of quantum finance (2020),https://arxiv.org/abs/2011.06492

    arXiv 2020

  8. [8]

    arXiv preprint arXiv:1701.08213 (2017).https: //doi.org/10.48550/arXiv.1701.08213

    Bravyi, S., Gambetta, J.M., Mezzacapo, A., Temme, K.: Tapering off qubits to simulate fermionic hamiltonians. arXiv preprint arXiv:1701.08213 (2017).https: //doi.org/10.48550/arXiv.1701.08213

    arXiv doi:10.48550/arxiv.1701.08213 2017

Show all 57 references
  1. [9]

    Broughton, M., Verdon, G., McCourt, T., Martinez, A.J., Yoo, J.H., Isakov, S.V., Massey, P., Halavati, R., Niu, M.Y., Zlokapa, A., Peters, E., Lockwood, O., Skolik, A., Jerbi, S., Dunjko, V., Leib, M., Streif, M., Dollen, D.V., Chen, H., Cao, S., Wiersema, R., Huang, H.Y., McC...

    2021 arXiv

  2. [10]

    In: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE)

    Chen, T.F., Jiang, J.H.R., Hsieh, M.H.: Partial equivalence checking of quantum circuits. In: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE). p. 594–604. IEEE (Sep 2022).https://doi.org/10.1109/ qce53715.2022.00082,http://dx.doi.org/10.1109/QCE53...

    2022 doi

  3. [11]

    In: Lees, M.H., Cai, W., Cheong, S.A., Su, Y., Abramson, D., Dongarra, J.J., Sloot, P.M.A

    Chen, Y., Mendl, C.B., Seidl, H.: Dead gate elimination. In: Lees, M.H., Cai, W., Cheong, S.A., Su, Y., Abramson, D., Dongarra, J.J., Sloot, P.M.A. (eds.) Computational Science – ICCS 2025. pp. 135–150. Springer Nature Switzerland, Cham (2025) 42 Y. Chen et al

    2025

  4. [12]

    In: Hermenegildo, M.V., Morales, J.F

    Chen, Y., Stade, Y.: Quantum constant propagation. In: Hermenegildo, M.V., Morales, J.F. (eds.) Static Analysis. pp. 164–189. Springer Na- ture Switzerland, Cham (2023),https://link.springer.com/chapter/10.1007/ 978-3-031-44245-2_9

    2023

  5. [13]

    Machine Learning20, 273–297 (1995).https://doi.org/10.1007/BF00994018

    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning20, 273–297 (1995).https://doi.org/10.1007/BF00994018

    1995 doi

  6. [14]

    ACM Transactions on Programming Languages and Systems13(4), 451–490 (1991)

    Cytron, R., Ferrante, J., Rosen, B.K., Wegman, M.N., Zadeck, F.K.: Efficiently computing static single assignment form and the control dependence graph. ACM Transactions on Programming Languages and Systems13(4), 451–490 (1991). https://doi.org/10.1145/115372.115320

    1991 doi

  7. [15]

    Microprocessors and Microsystems66, 90–101 (2019)

    van Dijk, J.P., Charbon, E., Sebastiano, F.: The electronic interface for quantum processors. Microprocessors and Microsystems66, 90–101 (2019)

    2019

  8. [16]

    IEEE Transactions on Computers70(12), 2136–2145 (2021)

    Egger, D.J., Gutiérrez, R.G., Mestre, J.C., Woerner, S.: Credit risk analysis using quantum computers. IEEE Transactions on Computers70(12), 2136–2145 (2021). https://doi.org/10.1109/TC.2020.3038063

    2021 doi

  9. [17]

    Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization al- gorithm (2014),https://arxiv.org/abs/1411.4028

    2014 arXiv

  10. [18]

    4OR17, 335–371 (2019).https://doi.org/ 10.1007/s10288-019-00424-y

    Glover, F., Kochenberger, G., Du, Y.: Quantum bridge analytics I: A tutorial on formulating and using QUBO models. 4OR17, 335–371 (2019).https://doi.org/ 10.1007/s10288-019-00424-y

    2019 doi

  11. [19]

    Na- ture567, 209–212 (2019).https://doi.org/10.1038/s41586-019-0980-2

    Havlíček, V., Córcoles, A.D., Temme, K., Harrow, A.W., Kandala, A., Chow, J.M., Gambetta, J.M.: Supervised learning with quantum-enhanced feature spaces. Na- ture567, 209–212 (2019).https://doi.org/10.1038/s41586-019-0980-2

    2019 doi

  12. [20]

    Jojo, J., Khandelwal, A., Chandra, M.G.: Quantum algorithms for tensor-svd (2024),https://arxiv.org/abs/2405.19485

    2024 arXiv

  13. [21]

    In: 2023 IEEE International Con- ference on Quantum Computing and Engineering (QCE)

    Kaul, M., Küchler, A., Banse, C.: A uniform representation of classical and quantum source code for static code analysis. In: 2023 IEEE International Con- ference on Quantum Computing and Engineering (QCE). p. 1013–1019. IEEE (Sep 2023).https://doi.org/10.1109/qce57702.2023.00...

    2023 doi

  14. [22]

    arXiv preprint arXiv:2205.06851 (2022)

    Khammassi, N., Morris, R.W., Premaratne, S., Luthi, F., Borjans, F., Suzuki, S., Flory, R., Ibarra, L.P.O., Lampert, L., Matsuura, A.Y.: A scalable microarchi- tecture for efficient instruction-driven signal synthesis and coherent qubit control. arXiv preprint arXiv:2205.06851 (2022)

    2022 arXiv

  15. [23]

    In: Proceedings of the 1st annual ACM SIGACT-SIGPLAN symposium on Principles of program- ming languages

    Kildall, G.A.: A unified approach to global program optimization. In: Proceedings of the 1st annual ACM SIGACT-SIGPLAN symposium on Principles of program- ming languages. pp. 194–206 (1973)

    1973

  16. [24]

    arXiv preprint arXiv:1904.04735 (2019)

    Kissinger, A., van de Wetering, J.: Pyzx: Large scale automated diagrammatic reasoning. arXiv preprint arXiv:1904.04735 (2019)

    1904 arXiv

  17. [25]

    Quantum6, 774 (Aug 2022).https://doi.org/10.22331/q-2022-08-11-774, https://doi.org/10.22331/q-2022-08-11-774

    LaRose, R., Mari, A., Kaiser, S., Karalekas, P.J., Alves, A.A., Czarnik, P., El Man- douh, M., Gordon, M.H., Hindy, Y., Robertson, A., Thakre, P., Wahl, M., Samuel, D., Mistri, R., Tremblay, M., Gardner, N., Stemen, N.T., Shammah, N., Zeng, W.J.: Mitiq: A software package for ...

    2022 doi

  18. [26]

    Proceedings of the ACMonProgrammingLanguages7(POPL),1895–1924(2023).https://doi.org/ 10.1145/3571258

    Lemerre, M.: SSA translation is an abstract interpretation. Proceedings of the ACMonProgrammingLanguages7(POPL),1895–1924(2023).https://doi.org/ 10.1145/3571258

    1924 doi

  19. [27]

    In: Host-Aware Dead-Measurement Detection 43 2024ACM/IEEE51stAnnualInternationalSymposiumonComputerArchitecture (ISCA)

    Li, P., Liu, J., Gonzales, A., Saleem, Z.H., Zhou, H., Hovland, P.: Qutracer: Mit- igating quantum gate and measurement errors by tracing subsets of qubits. In: Host-Aware Dead-Measurement Detection 43 2024ACM/IEEE51stAnnualInternationalSymposiumonComputerArchitecture (ISCA). ...

    2024 doi

  20. [28]

    Quantum4, 257 (2020)

    Maciejewski, F.B., Zimborás, Z., Oszmaniec, M.: Mitigation of readout noise in near-term quantum devices by classical post-processing based on detector tomog- raphy. Quantum4, 257 (2020)

    2020

  21. [29]

    In: 2023 IEEE Inter- national Conference on Quantum Computing and Engineering (QCE)

    Matondo-Mvula, N., Elleithy, K.: Advances in quantum medical image analysis using machine learning: Current status and future directions. In: 2023 IEEE Inter- national Conference on Quantum Computing and Engineering (QCE). vol. 01, pp. 367–377 (2023).https://doi.org/10.1109/QC...

    2023 doi

  22. [30]

    Reviews of Modern Physics92(1), 015003 (2020).https: //doi.org/10.1103/RevModPhys.92.015003

    McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S.C., Yuan, X.: Quantum computational chemistry. Reviews of Modern Physics92(1), 015003 (2020).https: //doi.org/10.1103/RevModPhys.92.015003

    2020 doi

  23. [31]

    In: 2018 IEEE International Conference on Rebooting Computing (ICRC)

    McCaskey, A., Dumitrescu, E., Liakh, D., Humble, T.: Hybrid programming for near-term quantum computing systems. In: 2018 IEEE International Conference on Rebooting Computing (ICRC). pp. 1–12 (2018).https://doi.org/10.1109/ ICRC.2018.8638598

    2018

  24. [32]

    New Journal of Physics18(2), 023023 (2016).https://doi.org/10.1088/1367-2630/18/2/023023

    McClean, J.R., Romero, J., Babbush, R., Aspuru-Guzik, A.: The theory of varia- tional hybrid quantum-classical algorithms. New Journal of Physics18(2), 023023 (2016).https://doi.org/10.1088/1367-2630/18/2/023023

    2016 doi

  25. [33]

    Møller, A., Schwartzbach, M.I.: Static program analysis (October 2018), depart- ment of Computer Science, Aarhus University,http://cs.au.dk/˜amoeller/spa/

    2018

  26. [34]

    Proceedings of the Royal Society A471(2181), 20150301 (2015).https://doi.org/10.1098/rspa

    Montanaro, A.: Quantum speedup of monte carlo methods. Proceedings of the Royal Society A471(2181), 20150301 (2015).https://doi.org/10.1098/rspa. 2015.0301

    2015 doi

  27. [35]

    npj Quantum Information7, 83 (2021).https://doi.org/10.1038/ s41534-021-00416-z

    Motta, M., Ye, E., McClean, J.R., Li, Z., Minnich, A.J., Babbush, R., Chan, G.K.L.: Low rank representations for quantum simulation of electronic struc- ture. npj Quantum Information7, 83 (2021).https://doi.org/10.1038/ s41534-021-00416-z

    2021

  28. [36]

    npj Quantum Information6(1), 84 (2020)

    Nachman, B., Urbanek, M., de Jong, W.A., Bauer, C.W.: Unfolding quantum computer readout noise. npj Quantum Information6(1), 84 (2020)

    2020

  29. [37]

    https://doi.org/10.1007/978-3-662-03811-6

    Nielson, F., Nielson, H., Hankin, C.: Principles of Program Analysis (01 1999). https://doi.org/10.1007/978-3-662-03811-6

    1999 doi

  30. [38]

    NVIDIA: Cuda c++ best practices guide.https://docs.nvidia.com/cuda/ cuda-c-best-practices-guide/(2026), accessed 2026-04-14

    2026

  31. [39]

    NVIDIA: Cuda c++ programming guide.https://docs.nvidia.com/cuda/ cuda-programming-guide/(2026), accessed 2026-04-14

    2026

  32. [40]

    Proceedings of the ACM on Software Engineer- ing1(FSE), 2144–2166 (Jul 2024).https://doi.org/10.1145/3660802,http: //dx.doi.org/10.1145/3660802

    Paltenghi, M., Pradel, M.: Analyzing quantum programs with lintq: A static analysis framework for qiskit. Proceedings of the ACM on Software Engineer- ing1(FSE), 2144–2166 (Jul 2024).https://doi.org/10.1145/3660802,http: //dx.doi.org/10.1145/3660802

    2024 doi

  33. [41]

    Nature Communications5, 4213 (2014).https://doi.org/ 10.1038/ncomms5213

    Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.H., Zhou, X.Q., Love, P.J., Aspuru-Guzik, A., O’Brien, J.L.: A variational eigenvalue solver on a photonic quantum processor. Nature Communications5, 4213 (2014).https://doi.org/ 10.1038/ncomms5213

    2014 doi

  34. [42]

    Quantum2, 79 (Aug 2018).https://doi.org/10.22331/q-2018-08-06-79,http://dx.doi.org/ 10.22331/q-2018-08-06-79

    Preskill, J.: Quantum computing in the nisq era and beyond. Quantum2, 79 (Aug 2018).https://doi.org/10.22331/q-2018-08-06-79,http://dx.doi.org/ 10.22331/q-2018-08-06-79

    2018 doi

  35. [43]

    Péter, R.: H. g. rice. classes of recursively enumerable sets and their decision problems. transactions of the american mathematical society, vol. 74 (1953) pp. 44 Y. Chen et al. 358–366. Journal of Symbolic Logic19(2), 121–122 (1954).https://doi.org/10. 2307/2268870

    1953

  36. [44]

    Qiskit contributors: Qiskit: An open-source framework for quantum computing (2023).https://doi.org/10.5281/zenodo.2573505

    2023 doi

  37. [45]

    Quantum7, 1062 (2023)

    Quetschlich, N., Burgholzer, L., Wille, R.: MQT Bench: Benchmarking software and design automation tools for quantum computing. Quantum7, 1062 (2023). https://doi.org/10.22331/q-2023-07-20-1062

    2023 doi

  38. [46]

    Quetschlich, N., Forster, T., Osterwind, A., Helms, D., Wille, R.: Towards equiv- alence checking of classical circuits using quantum computing (2024),https: //arxiv.org/abs/2408.14539

    2024 arXiv

  39. [47]

    Remme, L., Weinert, A., Waschk, A.: Optimization of hybrid quantum-classical algorithms (2025),https://arxiv.org/abs/2505.12853

    2025 arXiv

  40. [48]

    122.040504

    Schuld,M.,Killoran,N.:Quantummachinelearninginfeaturehilbertspaces.Phys- ical Review Letters122, 040504 (2019).https://doi.org/10.1103/PhysRevLett. 122.040504

    2019 doi

  41. [49]

    Springer (2012)

    Seidl, H., Wilhelm, R., Hack, S.: Compiler Design: Analysis and Transformation. Springer (2012)

    2012

  42. [50]

    Quantum Science and Technology 6(1), 014003 (nov 2020).https://doi.org/10.1088/2058-9565/ab8e92,https: //dx.doi.org/10.1088/2058-9565/ab8e92

    Sivarajah, S., Dilkes, S., Cowtan, A., Simmons, W., Edgington, A., Duncan, R.: t|ket〉: a retargetable compiler for nisq devices. Quantum Science and Technology 6(1), 014003 (nov 2020).https://doi.org/10.1088/2058-9565/ab8e92,https: //dx.doi.org/10.1088/2058-9565/ab8e92

    2020 doi

  43. [51]

    Quantum4, 291 (2020).https: //doi.org/10.22331/q-2020-07-06-291

    Stamatopoulos, N., Egger, D.J., Sun, Y., Zoufal, C., Iten, R., Shen, N., Woerner, S.: Option pricing using quantum computers. Quantum4, 291 (2020).https: //doi.org/10.22331/q-2020-07-06-291

    2020 doi

  44. [52]

    Quantum2, 49 (Jan 2018).https://doi.org/10.22331/ q-2018-01-31-49,https://doi.org/10.22331/q-2018-01-31-49

    Steiger, D.S., Häner, T., Troyer, M.: ProjectQ: an open source software framework for quantum computing. Quantum2, 49 (Jan 2018).https://doi.org/10.22331/ q-2018-01-31-49,https://doi.org/10.22331/q-2018-01-31-49

    2018 doi

  45. [53]

    Journal of the Royal Statistical Society: Series B58(1), 267–288 (1996).https://doi.org/10

    Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B58(1), 267–288 (1996).https://doi.org/10. 1111/j.2517-6161.1996.tb02080.x

    1996

  46. [54]

    The Journal of Chemical Physics152(12), 124114 (2020).https://doi.org/10.1063/1.5141458

    Verteletskyi,V.,Yen,T.C.,Izmaylov,A.F.:Measurementoptimizationinthevaria- tional quantum eigensolver using a minimum clique cover. The Journal of Chemical Physics152(12), 124114 (2020).https://doi.org/10.1063/1.5141458

    2020 doi

  47. [55]

    npj Quantum Information5, 15 (2019).https://doi.org/10.1038/s41534-019-0130-6

    Woerner, S., Egger, D.J.: Quantum risk analysis. npj Quantum Information5, 15 (2019).https://doi.org/10.1038/s41534-019-0130-6

    2019 doi

  48. [56]

    SIGPLAN Not.48(6), 175–186 (Jun 2013).https://doi.org/10.1145/2499370.2462164,https://doi.org/10.1145/ 2499370.2462164

    Zhao, J., Nagarakatte, S., Martin, M.M., Zdancewic, S.: Formal verification of ssa-based optimizations for llvm. SIGPLAN Not.48(6), 175–186 (Jun 2013).https://doi.org/10.1145/2499370.2462164,https://doi.org/10.1145/ 2499370.2462164

    2013 doi

  49. [57]

    Zhao, P., Wu, X., Li, Z., Zhao, J.: Qchecker: Detecting bugs in quantum programs via static analysis (2023),https://arxiv.org/abs/2304.04387

    2023 arXiv

Pith tools

Reviewed July 14, 2026 · model on record in the stance chip above.