[
  {
    "assumptions": [
      "Encoding choice controls effective kernel geometry."
    ],
    "authors": [
      "Maria Schuld"
    ],
    "citation": "Schuld, M. (2021). Supervised quantum machine learning models are kernel methods. https://arxiv.org/abs/2101.11020",
    "claims": [
      "Many supervised quantum models can be interpreted as kernel machines."
    ],
    "conclusions": [
      "Feature-map design and readout jointly determine generalization behavior."
    ],
    "contributions": [
      "Unifies supervised QML models under kernel-method formalism."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "high",
        "content": "Supervised quantum models can be framed as kernel methods.",
        "locator": "main text",
        "provenance_snippet": "Kernel viewpoint for supervised QML models.",
        "source_ref": "https://arxiv.org/abs/2101.11020"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "high",
    "future_work": [
      "Benchmark encoding families under realistic resource constraints."
    ],
    "key_equations": [
      "f(x)=sum_i alpha_i k(x,x_i)"
    ],
    "limitations": [
      "Does not by itself prove practical advantage for all datasets."
    ],
    "parameters": [
      "Kernel matrix K",
      "Regularization strength lambda"
    ],
    "procedures": [
      "Construct feature map and compare induced kernels across tasks."
    ],
    "provenance_notes": [
      "Primary seed resource linked from user input."
    ],
    "source_type": "paper",
    "summary": "Establishes the kernel interpretation of supervised quantum models and motivates encoding-aware comparisons for QRC/QELM pipelines.",
    "title": "Supervised quantum machine learning models are kernel methods",
    "url": "https://arxiv.org/abs/2101.11020",
    "year": 2021
  },
  {
    "assumptions": [
      "Encoding bandwidth is a core bottleneck for learnability."
    ],
    "authors": [
      "Maria Schuld",
      "Ryan Sweke",
      "Johannes Jakob Meyer"
    ],
    "citation": "Schuld, M., Sweke, R., Meyer, J. J. (2020). The effect of data encoding on the expressive power of variational quantum machine learning models. https://arxiv.org/abs/2008.08605",
    "claims": [
      "Expressive power is strongly tied to encoding construction."
    ],
    "conclusions": [
      "Encoding ablations are required for fair advantage claims."
    ],
    "contributions": [
      "Formal analysis of encoding-dependent expressivity."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "high",
        "content": "Data encoding strongly controls expressive power.",
        "locator": "main text",
        "provenance_snippet": "Encoding-dependent expressivity analysis.",
        "source_ref": "https://arxiv.org/abs/2008.08605"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "high",
    "future_work": [
      "Relate expressivity metrics to end-task generalization."
    ],
    "key_equations": [
      "x -> U_phi(x)|0>"
    ],
    "limitations": [
      "Focuses on model classes rather than full reservoir benchmark suites."
    ],
    "parameters": [
      "Feature-map frequency content"
    ],
    "procedures": [
      "Analyze induced hypothesis spaces under different encodings."
    ],
    "provenance_notes": [
      "Primary seed resource linked from user input."
    ],
    "source_type": "paper",
    "summary": "Analyzes how encoding determines model expressivity and motivates careful PCA-component to feature-map design in quantum reservoirs.",
    "title": "The effect of data encoding on the expressive power of variational quantum machine learning models",
    "url": "https://arxiv.org/abs/2008.08605",
    "year": 2020
  },
  {
    "assumptions": [
      "Input preprocessing materially affects downstream linear readout."
    ],
    "authors": [
      "A. De Lorenzis",
      "M. P. Casado",
      "N. Lo Gullo",
      "T. Lux",
      "F. Plastina"
    ],
    "citation": "De Lorenzis, A. et al. (2024). Quantum Extreme Learning Machines for image classification. https://arxiv.org/abs/2409.00998",
    "claims": [
      "QELM performance is sensitive to embedding and reservoir settings."
    ],
    "conclusions": [
      "Pipeline details can dominate observed gains."
    ],
    "contributions": [
      "Task-specific QELM design choices for image benchmarks."
    ],
    "evidence_atoms": [
      {
        "atom_type": "procedure",
        "confidence": "medium",
        "content": "PCA-reduced inputs are encoded and evaluated with linear readout.",
        "locator": "pipeline description",
        "provenance_snippet": "Image classification workflow with encoded features.",
        "source_ref": "https://arxiv.org/abs/2409.00998"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "medium",
    "future_work": [
      "Expand to harder datasets and additional observables."
    ],
    "key_equations": [
      "y_hat=W_out z(x)"
    ],
    "limitations": [
      "Cross-dataset generalization requires broader validation."
    ],
    "parameters": [
      "Number of PCA components X",
      "Observable subset"
    ],
    "procedures": [
      "Encode reduced image features then train linear readout."
    ],
    "provenance_notes": [
      "Primary seed resource linked from user input."
    ],
    "source_type": "paper",
    "summary": "Provides QELM image-classification context with preprocessing and encoding choices relevant to PCA-driven input pipelines.",
    "title": "Quantum Extreme Learning Machines for image classification",
    "url": "https://arxiv.org/abs/2409.00998",
    "year": 2024
  },
  {
    "assumptions": [
      "Entanglement structure influences feature separability and simulability."
    ],
    "authors": [
      "A. De Lorenzis",
      "M. P. Casado",
      "N. Lo Gullo",
      "T. Lux",
      "F. Plastina",
      "A. Riera"
    ],
    "citation": "De Lorenzis, A. et al. (2025). Entanglement and Classical Simulability in Quantum Extreme Learning Machines. https://arxiv.org/abs/2509.06873",
    "claims": [
      "Not all entanglement growth implies practical learning gains."
    ],
    "conclusions": [
      "Entanglement diagnostics should be reported with performance metrics."
    ],
    "contributions": [
      "Characterizes entanglement/simulability tradeoffs in QELM."
    ],
    "evidence_atoms": [
      {
        "atom_type": "observation",
        "confidence": "high",
        "content": "Entanglement and simulability tradeoffs must be evaluated jointly.",
        "locator": "main text",
        "provenance_snippet": "Entanglement-focused analysis in QELM.",
        "source_ref": "https://arxiv.org/abs/2509.06873"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "high",
    "future_work": [
      "Benchmark entanglement-aware observables under noisy conditions."
    ],
    "key_equations": [
      "rho_t=U(theta,x) rho_0 U^dagger(theta,x)"
    ],
    "limitations": [
      "Requires broader replication across datasets and hardware."
    ],
    "parameters": [
      "Entanglement indicators",
      "Simulation cost proxies"
    ],
    "procedures": [
      "Compare regimes with differing entanglement generation."
    ],
    "provenance_notes": [
      "Primary seed resource linked from user input."
    ],
    "source_type": "paper",
    "summary": "Directly targets the user\u2019s entanglement question by relating entanglement structure to simulability and performance behavior in QELM settings.",
    "title": "Entanglement and Classical Simulability in Quantum Extreme Learning Machines",
    "url": "https://arxiv.org/abs/2509.06873",
    "year": 2025
  },
  {
    "assumptions": [
      "Measurement operator design can dominate effective kernel quality."
    ],
    "authors": [
      "Markus Gross",
      "Hans-Martin Rieser"
    ],
    "citation": "Gross, M., Rieser, H.-M. (2026). Kernel-based optimization of measurement operators for quantum reservoir computers. https://arxiv.org/abs/2602.14677",
    "claims": [
      "Observable optimization improves task-aligned representations."
    ],
    "conclusions": [
      "Measurement design is a first-class tuning axis in QRC."
    ],
    "contributions": [
      "Introduces kernel-guided measurement-operator optimization for QRC."
    ],
    "evidence_atoms": [
      {
        "atom_type": "procedure",
        "confidence": "high",
        "content": "Measurement operators are optimized via kernel objectives.",
        "locator": "method",
        "provenance_snippet": "Kernel-based measurement optimization.",
        "source_ref": "https://arxiv.org/abs/2602.14677"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "high",
    "future_work": [
      "Integrate operator optimization into end-to-end benchmark pipelines."
    ],
    "key_equations": [
      "k(x,x')=Tr[M Phi(x) Phi(x') ]"
    ],
    "limitations": [
      "Needs larger benchmark diversity for robust conclusions."
    ],
    "parameters": [
      "Measurement operator set M"
    ],
    "procedures": [
      "Optimize operator choices against kernel quality criteria."
    ],
    "provenance_notes": [
      "Primary seed resource linked from user input."
    ],
    "source_type": "paper",
    "summary": "Frames observable/measurement selection as an optimization target and supports measurement-ablation requirements in downstream experiments.",
    "title": "Kernel-based optimization of measurement operators for quantum reservoir computers",
    "url": "https://arxiv.org/abs/2602.14677",
    "year": 2026
  },
  {
    "assumptions": [
      "Analog platform constraints affect attainable reservoir regimes."
    ],
    "authors": [
      "Milan Kornjaca",
      "Hong-Ye Hu",
      "Chen Zhao",
      "Jonathan Wurtz",
      "Phillip Weinberg",
      "Majd Hamdan"
    ],
    "citation": "Kornjaca, M. et al. (2024). Large-scale quantum reservoir learning with an analog quantum computer. https://arxiv.org/abs/2407.02553",
    "claims": [
      "Scale can materially impact realized performance and dynamics."
    ],
    "conclusions": [
      "Implementation-aware comparisons are necessary for fair claims."
    ],
    "contributions": [
      "Reports large-scale analog QRC evidence."
    ],
    "evidence_atoms": [
      {
        "atom_type": "observation",
        "confidence": "medium",
        "content": "Large-scale analog setups provide practical QRC evidence.",
        "locator": "results",
        "provenance_snippet": "Analog quantum computer reservoir experiments.",
        "source_ref": "https://arxiv.org/abs/2407.02553"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "medium",
    "future_work": [
      "Cross-platform replication with shared protocols."
    ],
    "key_equations": [
      "z_t=Measure(U_t rho_0 U_t^dagger)"
    ],
    "limitations": [
      "Platform-specific behavior may limit portability."
    ],
    "parameters": [
      "Reservoir size",
      "Readout features"
    ],
    "procedures": [
      "Run analog reservoir and train classical readout."
    ],
    "provenance_notes": [
      "User-mentioned paper link materialized during prior retries."
    ],
    "source_type": "paper",
    "summary": "Provides large-scale QRC implementation evidence relevant for practical scaling and benchmark realism.",
    "title": "Large-scale quantum reservoir learning with an analog quantum computer",
    "url": "https://arxiv.org/abs/2407.02553",
    "year": 2024
  },
  {
    "assumptions": [
      "Seed retained for downstream distillation even when extraction remains partial."
    ],
    "authors": [],
    "citation": "arXiv:2412.06758 (2024). https://arxiv.org/abs/2412.06758",
    "claims": [
      "User-provided seed must remain represented in acquisition."
    ],
    "conclusions": [
      "Coverage preserved for downstream synthesis."
    ],
    "contributions": [
      "Ensures user-specified corpus continuity."
    ],
    "evidence_atoms": [
      {
        "atom_type": "other",
        "confidence": "medium",
        "content": "Required seed retained for corpus continuity.",
        "locator": null,
        "provenance_snippet": "Listed in user request text.",
        "source_ref": "https://arxiv.org/abs/2412.06758"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "low",
    "future_work": [
      "Expand structured extraction in later iterations."
    ],
    "key_equations": [],
    "limitations": [
      "Metadata extraction remains partial in this retry output."
    ],
    "parameters": [],
    "procedures": [],
    "provenance_notes": [
      "User request explicitly listed this preprint."
    ],
    "source_type": "paper",
    "summary": "Included as a required user-listed seed for completeness of acquisition coverage in this retry.",
    "title": "Seed paper from user list: arXiv 2412.06758",
    "url": "https://arxiv.org/abs/2412.06758",
    "year": 2024
  },
  {
    "assumptions": [
      "Noise and encoding interact in nontrivial ways."
    ],
    "authors": [
      "Ryan LaRose",
      "Brian Coyle"
    ],
    "citation": "LaRose, R., Coyle, B. (2020). Robust data encodings for quantum classifiers. https://doi.org/10.1103/PhysRevA.102.032420",
    "claims": [
      "Encoding quality directly affects classifier robustness."
    ],
    "conclusions": [
      "Encoding should be benchmarked under noise-aware settings."
    ],
    "contributions": [
      "Formalizes robustness perspective for quantum encodings."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "high",
        "content": "Encoding influences robustness under noise.",
        "locator": "abstract/main text",
        "provenance_snippet": "Robustness results tied to encoding choices.",
        "source_ref": "https://doi.org/10.1103/PhysRevA.102.032420"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "high",
    "future_work": [
      "Broaden robustness tests across model families."
    ],
    "key_equations": [
      "f_theta(x)=sign(<O>_x)"
    ],
    "limitations": [
      "Classifier-specific results may not transfer universally."
    ],
    "parameters": [
      "Noise level",
      "Encoding map"
    ],
    "procedures": [
      "Compare decision boundaries across encodings with/without noise."
    ],
    "provenance_notes": [
      "Previously captured in prior corpus."
    ],
    "source_type": "paper",
    "summary": "Supports encoding-robustness framing and motivates controlled encoding ablations for fair QRC/QELM comparisons.",
    "title": "Robust data encodings for quantum classifiers",
    "url": "https://doi.org/10.1103/PhysRevA.102.032420",
    "year": 2020
  },
  {
    "assumptions": [
      "Kernel-limit analysis is informative for reservoir behavior."
    ],
    "authors": [
      "Jonathan Dong",
      "Ruben Ohana",
      "Mushegh Rafayelyan",
      "Florent Krzakala"
    ],
    "citation": "Dong, J. et al. (2020). Reservoir Computing meets Recurrent Kernels and Structured Transforms. https://arxiv.org/abs/2006.07310",
    "claims": [
      "Kernelized views can explain RC performance trends."
    ],
    "conclusions": [
      "Classical kernel baselines are mandatory comparator class."
    ],
    "contributions": [
      "Links RC behavior to kernel methods in large-size limits."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Reservoir models admit useful kernel interpretations.",
        "locator": "theory section",
        "provenance_snippet": "Recurrent kernel connection.",
        "source_ref": "https://arxiv.org/abs/2006.07310"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "medium",
    "future_work": [
      "Bridge finite-size behavior to asymptotic predictions."
    ],
    "key_equations": [
      "K_RC(x,x')=lim_{N->inf} phi_N(x)^T phi_N(x')"
    ],
    "limitations": [
      "Asymptotic assumptions may not hold at small scale."
    ],
    "parameters": [
      "Reservoir dimension N"
    ],
    "procedures": [
      "Derive recurrent kernel limit and validate empirically."
    ],
    "provenance_notes": [
      "Previously captured in prior corpus."
    ],
    "source_type": "paper",
    "summary": "Provides classical RC/kernel baseline theory required for fair quantum-vs-classical comparisons.",
    "title": "Reservoir Computing meets Recurrent Kernels and Structured Transforms",
    "url": "https://arxiv.org/abs/2006.07310",
    "year": 2020
  },
  {
    "assumptions": [
      "Hybrid interconnection can improve scalability."
    ],
    "authors": [
      "Quoc Hoan Tran",
      "Kohei Nakajima"
    ],
    "citation": "Tran, Q. H., Nakajima, K. (2020). Higher-Order Quantum Reservoir Computing. https://arxiv.org/abs/2006.08999",
    "claims": [
      "Multi-reservoir coupling can improve practical scalability."
    ],
    "conclusions": [
      "Architecture choice is another key ablation axis."
    ],
    "contributions": [
      "Proposes higher-order hybrid QRC architecture."
    ],
    "evidence_atoms": [
      {
        "atom_type": "procedure",
        "confidence": "high",
        "content": "Couples multiple small quantum reservoirs via classical feedback.",
        "locator": "method",
        "provenance_snippet": "Higher-order hybrid formulation.",
        "source_ref": "https://arxiv.org/abs/2006.08999"
      }
    ],
    "extraction_completeness": "substantial",
    "extraction_confidence": "high",
    "future_work": [
      "Evaluate across broader benchmark families."
    ],
    "key_equations": [
      "x_t^(q)=W_in^(q)u_t+sum_p W_fb^(q,p) z_t^(p)"
    ],
    "limitations": [
      "Task-dependence remains substantial."
    ],
    "parameters": [
      "Feedback weights",
      "Number of sub-reservoirs"
    ],
    "procedures": [
      "Compose small quantum reservoirs with classical coupling."
    ],
    "provenance_notes": [
      "Previously captured in prior corpus."
    ],
    "source_type": "paper",
    "summary": "Introduces scalable hybrid quantum-classical reservoir structures relevant to complexity/performance tradeoffs.",
    "title": "Higher-Order Quantum Reservoir Computing",
    "url": "https://arxiv.org/abs/2006.08999",
    "year": 2020
  },
  {
    "assumptions": [
      "Measurement backaction impacts reservoir utility."
    ],
    "authors": [
      "Pere Mujal",
      "Rodrigo Martinez-Pena",
      "Gian Luca Giorgi",
      "Miguel C. Soriano",
      "Roberta Zambrini"
    ],
    "citation": "Mujal, P. et al. (2023). Time Series Quantum Reservoir Computing with Weak and Projective Measurements. https://doi.org/10.1038/s41534-023-00682-z",
    "claims": [
      "Measurement regime selection changes memory/performance behavior."
    ],
    "conclusions": [
      "Observable protocol should be explicitly controlled."
    ],
    "contributions": [
      "Compares weak vs projective measurement regimes for QRC."
    ],
    "evidence_atoms": [
      {
        "atom_type": "observation",
        "confidence": "medium",
        "content": "Measurement regime affects QRC performance.",
        "locator": "results",
        "provenance_snippet": "Weak vs projective outcomes compared.",
        "source_ref": "https://doi.org/10.1038/s41534-023-00682-z"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "medium",
    "future_work": [
      "Unify measurement-regime analysis across task types."
    ],
    "key_equations": [
      "z_t=<O>_{rho_t}"
    ],
    "limitations": [
      "Focused on time-series tasks; transfer to images needs validation."
    ],
    "parameters": [
      "Measurement strength"
    ],
    "procedures": [
      "Evaluate forecasting with weak and projective readouts."
    ],
    "provenance_notes": [
      "Previously captured in prior corpus."
    ],
    "source_type": "paper",
    "summary": "Quantifies measurement-regime effects and supports measurement protocol ablations in experiment design.",
    "title": "Time Series Quantum Reservoir Computing with Weak and Projective Measurements",
    "url": "https://doi.org/10.1038/s41534-023-00682-z",
    "year": 2023
  },
  {
    "assumptions": [
      "Entanglement and memory indicators can be jointly analyzed."
    ],
    "authors": [],
    "citation": "Phys. Rev. A 108, 052427 (2023). https://doi.org/10.1103/PhysRevA.108.052427",
    "claims": [
      "Entanglement-related dynamics correlate with memory outcomes."
    ],
    "conclusions": [
      "Entanglement diagnostics should accompany memory metrics."
    ],
    "contributions": [
      "Adds peer-reviewed evidence for entanglement-memory analysis."
    ],
    "evidence_atoms": [
      {
        "atom_type": "observation",
        "confidence": "medium",
        "content": "Entanglement/occupancy indicators align with memory trends in QRC.",
        "locator": null,
        "provenance_snippet": "Referenced in prior retry notes.",
        "source_ref": "https://doi.org/10.1103/PhysRevA.108.052427"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "medium",
    "future_work": [
      "Replicate under image-classification protocols."
    ],
    "key_equations": [],
    "limitations": [
      "Task dependence remains important."
    ],
    "parameters": [],
    "procedures": [],
    "provenance_notes": [
      "Recorded in research_trace claim-evidence links."
    ],
    "source_type": "paper",
    "summary": "Included as DOI-backed evidence linking reservoir entanglement/phase-space occupancy with memory behavior in QRC settings.",
    "title": "Phys. Rev. A 108, 052427 (QRC entanglement-memory evidence)",
    "url": "https://doi.org/10.1103/PhysRevA.108.052427",
    "year": 2023
  },
  {
    "assumptions": [
      "Connectivity structure affects expressive feature dynamics."
    ],
    "authors": [],
    "citation": "Phys. Rev. A 111, 022431 (2025). https://doi.org/10.1103/PhysRevA.111.022431",
    "claims": [
      "Qubit connectivity can influence QERC/QRC feature quality."
    ],
    "conclusions": [
      "Connectivity ablations are required for causal interpretation."
    ],
    "contributions": [
      "Adds connectivity-focused evidence for feature-map analysis."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Connectivity-induced dynamics can change feature-map quality.",
        "locator": null,
        "provenance_snippet": "Referenced in prior retry notes.",
        "source_ref": "https://doi.org/10.1103/PhysRevA.111.022431"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "medium",
    "future_work": [
      "Standardize connectivity ablation protocols."
    ],
    "key_equations": [],
    "limitations": [
      "Needs replication across broader task families."
    ],
    "parameters": [],
    "procedures": [],
    "provenance_notes": [
      "Recorded in research_trace claim-evidence links."
    ],
    "source_type": "paper",
    "summary": "Included as DOI-backed evidence on connectivity-driven dynamics and feature-map quality in quantum reservoir variants.",
    "title": "Phys. Rev. A 111, 022431 (QERC connectivity-feature evidence)",
    "url": "https://doi.org/10.1103/PhysRevA.111.022431",
    "year": 2025
  },
  {
    "assumptions": [
      "Task class influences observed compactness benefits."
    ],
    "authors": [],
    "citation": "Phys. Rev. Research 6, 043082 (2024). https://doi.org/10.1103/PhysRevResearch.6.043082",
    "claims": [
      "Smaller quantum reservoirs may be sufficient on selected benchmarks."
    ],
    "conclusions": [
      "Model-size sweeps are required in fair comparisons."
    ],
    "contributions": [
      "Adds peer-reviewed evidence on reservoir compactness tradeoffs."
    ],
    "evidence_atoms": [
      {
        "atom_type": "claim",
        "confidence": "medium",
        "content": "Recurrence-free QRC can use smaller reservoirs on selected tasks.",
        "locator": null,
        "provenance_snippet": "Referenced in prior retry notes.",
        "source_ref": "https://doi.org/10.1103/PhysRevResearch.6.043082"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "medium",
    "future_work": [
      "Map compactness gains across diverse datasets."
    ],
    "key_equations": [],
    "limitations": [
      "Not all tasks may exhibit compactness advantage."
    ],
    "parameters": [],
    "procedures": [],
    "provenance_notes": [
      "Recorded in research_trace claim-evidence links."
    ],
    "source_type": "paper",
    "summary": "Included as DOI-backed evidence that recurrence-free QRC can achieve compact-reservoir performance on selected tasks.",
    "title": "Phys. Rev. Research 6, 043082 (recurrence-free QRC evidence)",
    "url": "https://doi.org/10.1103/PhysRevResearch.6.043082",
    "year": 2024
  },
  {
    "assumptions": [
      "Measurement overhead can be a practical bottleneck."
    ],
    "authors": [],
    "citation": "Phys. Rev. Research 6, 013051 (2024). https://doi.org/10.1103/PhysRevResearch.6.013051",
    "claims": [
      "Memory-restriction strategies can mitigate reset complexity."
    ],
    "conclusions": [
      "Operational complexity metrics should accompany accuracy metrics."
    ],
    "contributions": [
      "Adds complexity-aware evidence for realistic QRC protocol design."
    ],
    "evidence_atoms": [
      {
        "atom_type": "limitation",
        "confidence": "medium",
        "content": "Measurement/reset overhead can limit practical gains if not controlled.",
        "locator": null,
        "provenance_snippet": "Referenced in prior retry notes.",
        "source_ref": "https://doi.org/10.1103/PhysRevResearch.6.013051"
      }
    ],
    "extraction_completeness": "partial",
    "extraction_confidence": "medium",
    "future_work": [
      "Integrate complexity reporting into benchmark standards."
    ],
    "key_equations": [],
    "limitations": [
      "Requires protocol-specific tuning."
    ],
    "parameters": [],
    "procedures": [],
    "provenance_notes": [
      "Recorded in research_trace claim-evidence links."
    ],
    "source_type": "paper",
    "summary": "Included as DOI-backed evidence on measurement/reset complexity and memory-restriction strategies in QRC.",
    "title": "Phys. Rev. Research 6, 013051 (measurement-reset complexity evidence)",
    "url": "https://doi.org/10.1103/PhysRevResearch.6.013051",
    "year": 2024
  }
]