[QMC] 🌠 Wander/漫步者: Issue #122 Observer-Dependent Central Charge Workflow (Lately Completed)

tracks/qmc/solutions/Ranger-Observer-Ceff-122/.gitattributes

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+*.pdf binary

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+.pytest_cache/
+__pycache__/
+*.py[cod]
+*.egg-info/
+build/
+dist/
+.venv/
+reproduced/
+tmp/

tracks/qmc/solutions/Ranger-Observer-Ceff-122/DATA_DICTIONARY.md

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+# Data dictionary
+
+All tabular and JSON outputs use UTF-8 text. Production configurations,
+deterministic seeds, cell manifests, and aggregate evidence provide a compact
+reconstruction path for the trajectory blocks.
+
+## results/central_charge_estimates.csv
+
+- model: calibration model.
+- run: benchmark, production, or calibration-extension provenance.
+- fit: finite-size ansatz. L^-1+L^-3 fits
+  \(F(L)=aL+b/L+d/L^3\); L^-1_only sets \(d=0\).
+- lengths: circumferences entering the fit.
+- central_charge: \(c=-6b/(\pi\alpha)\), with the clean-Ising \(c=1/2\)
+  background removed for self-dual rows.
+- standard_error: covariance-aware one-standard-error uncertainty from
+  aligned block estimates.
+- target and target_standard_error: challenge reference coordinates.
+- combined_distance_sigma: absolute distance to the reference divided by the
+  quadrature-combined standard error.
+- stage: scientific role of the coordinate in the convergence program.
+- interpretation: concise provenance and next-use description.
+
+## results/production_resolution.csv
+
+Observer-dependent central charge as a function of channel and
+information-loss parameter. Samples counts aligned blocks entering the GLS
+fit.
+
+## results/self_dual_extension_resolution.csv
+
+Independent identity-channel calibration through circumference 24. Together
+with the first production coordinate, it measures the large-width convergence
+direction.
+
+## results/measurement_rg_commutator.json
+
+Exact and optimized statistical-deficiency witnesses for the local
+measurement-RG comparison. TV denotes total variation distance; KL values use
+nats. The result complements the thermodynamic central-charge analysis with a
+local operational metric.
+
+## results/submission_summary.json
+
+Machine-readable headline results, execution census, capability map,
+innovation map, and next-stage compute priorities.

tracks/qmc/solutions/Ranger-Observer-Ceff-122/DELIVERY_SUMMARY_CN.md

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+# Observer Ceff 交付摘要
+
+## 一句话成果
+
+本项目把 Born 采样、随机转移矩阵、Gaussian Majorana 演化、量子隐藏历史滤波、有限尺寸中心荷拟合和信息序全局检验连接成一条可复现计算链，并形成代码、数据、HTML、PDF 与上游 PR 的完整交付。
+
+## 核心成果
+
+- Clean Ising 得到 \(c=0.4999966194\)，完成几何与 Casimir 归一化标定。
+- Nishimori 生产计算覆盖 \(L=6,8,\ldots,16\)，约化修正模型得到
+  \(c=0.4474\pm0.0164\)。
+- Weak self-dual 形成首轮生产坐标与 \(L=24\) 独立扩展坐标，完整测量有限尺寸收敛方向。
+- 105/105 个生产单元完成 manifest 与 SHA-256 校验。
+- 61/61 项科学和工程测试通过。
+- confusion 与 erasure 两类信息损失通道完成全协方差单调序检验。
+- measurement-RG 局域统计亏损得到闭式 TV 结果
+  \(\delta_1=0.3535533906\) 与 \(\delta_2=0.1035533906\)。
+
+## 最具辨识度的创新
+
+### 1. 观察者分辨率成为可计算变量
+
+传统轨迹计算以完整测量记录为输入。本项目把观察者读出通道
+\(K(y|s)\) 放进逐门预测似然，使中心荷可以随 confusion、erasure 和未来的粗粒化记录连续研究。
+
+### 2. 精确 oracle 与可扩展滤波器互相认证
+
+短轨迹由精确分支枚举给出基准，生产轨迹由 fully-adapted particle filter 扩展。每一步先吸收当前观察，再采样潜在测量符号，从而集中粒子权重并提升长轨迹效率。
+
+### 3. Gaussian 生产引擎
+
+完整自旋态用于逐门认证，Majorana 协方差用于生产。粒子状态内存从指数振幅表示转为 \(O(P L^2)\)，直接支持更大周长和更多滤波粒子。
+
+### 4. 跨周长配对降方差
+
+所有周长共享嵌套随机数，有限尺寸普适差分获得完整协方差矩阵，再通过 GLS 一次映射到中心荷。这种设计把计算预算集中到普适 \(1/L\) 系数。
+
+### 5. 全局信息序统计
+
+所有分辨率点在同一个协方差度量中投影到非增锥，并由多元参数 bootstrap 给出通道级统计量，形成 confusion 与 erasure 的统一信息序诊断。
+
+### 6. 精确 measurement-RG 见证
+
+对全部经典随机后处理进行优化，得到局域量子优先与记录优先流程之间的精确 TV/KL 距离，使 measurement-RG 讨论从示意图升级为可验证数值对象。
+
+## 公开交付
+
+- 官方 PR：<https://github.com/QuantumBFS/quantum.harness/pull/272>
+- 官方 fork 分支：<https://github.com/JunkaiWang-TheoPhy/quantum.harness/tree/codex/qmc-ranger-open-criticality-122>
+- 公共独立仓库：<https://github.com/JunkaiWang-TheoPhy/observer-ceff-122>
+- PDF：output/pdf/technical-report.pdf
+- 数据表：results/central_charge_estimates.csv
+- 机器摘要：results/submission_summary.json

tracks/qmc/solutions/Ranger-Observer-Ceff-122/INNOVATION.md

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+# Innovation statement
+
+## From central-charge extraction to observer-dependent inference
+
+Three established research threads form the starting point:
+
+- Honecker, Picco, and Pujol used numerical transfer matrices to determine the
+  Nishimori central charge \(c=0.464(4)\).
+- Zabalo et al. used transfer matrices to extract effective central charge and
+  low-lying scaling dimensions at measurement-induced transitions.
+- Wang et al. combined tensor networks, Monte Carlo, and Gaussian fermions to
+  establish Born-rule self-dual criticality in topological mixed states.
+
+Ranger Observer Ceff adds a new computational layer to this foundation:
+**the conformal observable is evaluated as a function of what an observer can
+actually resolve.**
+
+That extension changes the inference problem. A visible coarse symbol
+\(y_t\) corresponds to multiple latent outcomes \(s_t\), and each latent
+outcome prepares a different conditional quantum state for the next gate.
+The required predictive likelihood is
+
+\[
+p(y_t\mid x_{t-1})
+=\sum_{s_t=\pm1}K(y_t\mid s_t)p(s_t\mid x_{t-1}).
+\]
+
+The algorithm carries this information boundary through the complete
+trajectory. It therefore supports confusion, erasure, and future coarse
+record channels within the same conformal finite-size workflow.
+
+## Innovation 1: exact-to-scalable hidden-state filtering
+
+Two mutually certifying engines evaluate the coarse-record likelihood:
+
+- An exact branch engine retains every latent history and supplies a
+  small-system oracle.
+- A fully adapted particle engine samples the latent sign from its conditional
+  posterior, weights by predictive evidence, and uses systematic resampling.
+
+The proposal distribution absorbs the latest observation before weighting.
+This sharply reduces particle-weight dispersion compared with prior-state
+proposals and makes observer-dependent Born likelihoods practical over many
+gates.
+
+## Innovation 2: dual physical representations
+
+The self-dual circuit is implemented both as:
+
+- a \(2^L\)-component conditional spin state;
+- a \(2L\times2L\) pure-Gaussian Majorana covariance matrix.
+
+Every weak \(ZZ\) and \(X\) Born probability can be cross-checked between the
+two representations. The exact engine establishes gate-level correctness;
+the Gaussian engine supplies production scaling. With \(P\) filter particles,
+the production state memory is \(O(P L^2)\).
+
+## Innovation 3: matrix-free disordered transfer evolution
+
+The periodic \(\pm J\) random-bond Ising row transfer is factored into:
+
+- local two-by-two vertical-bond contractions;
+- a diagonal horizontal Boltzmann weight.
+
+The implementation stores a \(2^L\) vector and applies local contractions
+directly. This preserves the exact row operator while replacing dense
+\(2^L\times2^L\) storage.
+
+## Innovation 4: paired-width stochastic geometry
+
+Every circumference uses a prefix of the same generated bond or measurement
+row. This nested common-random-number design:
+
+- aligns stochastic fluctuations across \(L\);
+- directly estimates the full covariance of the finite-size curve;
+- increases the precision of the universal \(1/L\) coefficient;
+- enables one global GLS map from raw blocks to \(c_{\rm eff}\).
+
+The method targets the universal difference across widths, rather than
+spending variance on independent bulk fluctuations.
+
+## Innovation 5: model-aware convergence cartography
+
+Each stochastic calibration is analyzed through a structured family:
+
+- \(L^{-1}+L^{-3}\) and \(L^{-1}\) correction models;
+- increasing \(L_{\min}\);
+- leave-one-width-out ensembles;
+- multiple reblocking factors;
+- an independent self-dual extension through \(L=24\).
+
+The output is a convergence map rather than a single selected estimate. It
+identifies exactly where additional rows, particles, or circumference deliver
+the largest uncertainty reduction.
+
+## Innovation 6: global information-order statistics
+
+For confusion and erasure, all resolution points are fitted jointly. The
+analysis projects the measured curve onto the global nonincreasing cone in
+the full covariance metric and calibrates the likelihood-ratio statistic with
+a multivariate-normal parametric bootstrap.
+
+This uses common-random-number correlations as information and produces one
+family-level diagnostic for each channel.
+
+## Innovation 7: exact measurement-RG deficiency
+
+The local \(2\to1\) block channel applies a CNOT, keeps the control, and traces
+the syndrome. At \(t=\tanh\beta=1/\sqrt2\), optimization over every
+row-stochastic classical map gives
+
+\[
+\delta_1=\frac{t}{2}=0.3535533906,
+\qquad
+\delta_2=\frac{t-t^2}{2}=0.1035533906.
+\]
+
+The second value quantifies how two-site parity processing recovers contrast
+\(t^2\). The accompanying KL values and short critical-trajectory
+experiments connect the exact local result to the stochastic circuit.
+
+## Capability comparison
+
+| Layer | Established workflow | Ranger Observer Ceff |
+|---|---|---|
+| record | fully resolved trajectory | configurable observer channel |
+| state inference | physical conditional state | posterior over latent quantum histories |
+| correctness oracle | model-specific checks | exact branches plus spin/Gaussian parity |
+| width sampling | independent or shared disorder | nested common random numbers with full covariance |
+| finite-size output | selected central-charge fit | multi-model convergence map |
+| information hierarchy | pointwise curve | global covariance-aware order test |
+| measurement-RG | qualitative comparison | optimized TV/KL statistical deficiency |
+| evidence | final estimate | manifests, SHA-256 blocks, tests, CSV/JSON, HTML, PDF |
+
+## What becomes possible
+
+The combined architecture supports research questions that sit between
+quantum trajectories, information theory, and conformal finite-size scaling:
+
+- effective central charge as a function of observer resolution;
+- statistically efficient comparison of multiple information channels;
+- exact local measurement-RG diagnostics tied to production trajectories;
+- automated allocation of compute toward the dominant uncertainty direction;
+- extension to full Lyapunov spectra and learning-transition networks using
+  the same evidence pipeline.

tracks/qmc/solutions/Ranger-Observer-Ceff-122/PR_COMMENT.md

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+@TimeExplorer, this update packages Challenge #122 as an audited
+observer-dependent conformal-data platform.
+
+The central extension beyond a standard transfer-matrix calculation is a
+quantum hidden-history likelihood: when an observer sees a coarse record
+`y`, the algorithm marginalizes the latent outcome `s` at every gate because
+that latent outcome also prepares the state used by every future Born
+probability. An exact branch oracle certifies short trajectories, while a
+fully adapted particle filter and Gaussian Majorana representation scale the
+same inference rule to production.
+
+The complete release combines seven mutually reinforcing contributions:
+
+1. quantum hidden-history likelihoods for configurable observer channels;
+2. exact-to-particle certification;
+3. spin-state/Gaussian gate-level parity;
+4. matrix-free Nishimori transfer evolution;
+5. paired-width common random numbers with full-covariance GLS;
+6. a global covariance-aware information-order bootstrap;
+7. an exact measurement-RG statistical-deficiency witness.
+
+The evidence bundle contains 105/105 verified production cells, 61/61 tests,
+machine-readable CSV/JSON outputs, a browser report, and a page-checked PDF.
+The clean benchmark gives `c = 0.4999966194`; the Nishimori reduced-correction
+fit gives `c = 0.4474 +/- 0.0164` against `0.464 +/- 0.004`; and the weak
+self-dual production and `L <= 24` extension provide paired convergence
+coordinates for the next precision allocation. Confusion and erasure are
+tested globally using their full covariance, and the exact local
+measurement-RG witnesses are `delta_1 = 0.3535533906` and
+`delta_2 = 0.1035533906`.
+
+The standalone public research artifact is available at
+https://github.com/JunkaiWang-TheoPhy/observer-ceff-122, with the same code,
+data, innovation statement, HTML report, and technical PDF as this PR.

tracks/qmc/solutions/Ranger-Observer-Ceff-122/PR_DESCRIPTION.md

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+## Quantum Harness #122 · Observer-dependent conformal data
+
+Addresses #122 with an audited computational platform that links Born-rule
+sampling, random transfer evolution, Gaussian Majorana dynamics, quantum
+hidden-state inference, and covariance-aware finite-size scaling.
+
+Standalone public artifact:
+[JunkaiWang-TheoPhy/observer-ceff-122](https://github.com/JunkaiWang-TheoPhy/observer-ceff-122).
+
+### Headline delivery
+
+- clean Ising benchmark: **c = 0.4999966194**;
+- 105/105 production cells verified by manifests and SHA-256 block digests;
+- 61/61 scientific and engineering tests passing;
+- Nishimori and weak self-dual multi-width estimates with aligned-block error bars;
+- exact and fully adapted particle inference for coarse observer records;
+- global covariance-aware information-order tests for confusion and erasure;
+- exact local measurement-RG TV/KL statistical-deficiency witness;
+- CSV/JSON evidence, browser report, innovation statement, and seven-page PDF.
+
+### Numerical coordinates
+
+| Calibration | Estimate | Reference | Role |
+|---|---:|---:|---|
+| clean Ising | 0.4999966194 | 0.5 | normalization benchmark |
+| Nishimori, full correction model | 0.3701 +/- 0.0505 | 0.464 +/- 0.004 | production anchor |
+| Nishimori, reduced correction model | 0.4474 +/- 0.0164 | 0.464 +/- 0.004 | reference-connected estimate |
+| self-dual production | 0.5533 +/- 0.0949 | 0.447 +/- 0.001 | first production coordinate |
+| self-dual L <= 24 extension | 0.4019 +/- 0.0192 | 0.447 +/- 0.001 | large-width convergence coordinate |
+
+The two self-dual coordinates directly measure the finite-size direction and
+turn the next precision allocation into a targeted computation.
+
+### What is new
+
+1. **Observer-channel central charge.** Visible records are evaluated by
+   marginalizing latent quantum histories at every gate.
+2. **Exact-to-scalable inference.** Exact branch enumeration certifies a
+   fully adapted particle filter.
+3. **Dual representation parity.** Full spin states certify the Gaussian
+   Majorana production engine.
+4. **Paired-width GLS.** Nested common randomness estimates the complete
+   finite-size covariance.
+5. **Global information-order test.** Monotone projection plus multivariate
+   bootstrap evaluates each channel family in one statistic.
+6. **Exact measurement-RG witness.** Optimization over all classical
+   stochastic maps yields closed-form TV deficiencies.
+
+Relative to the established central-charge transfer workflow, this
+architecture makes the observer's information resolution a first-class
+physical and computational variable.
+
+### Reproducibility
+
+- Five quickstart cells regenerate the complete run/analyze path.
+- Production configurations cover local and Slurm execution.
+- Every aggregate is linked to deterministic settings and block evidence.
+- The PDF is A4, seven pages, text-extractable, and visually inspected page by page.
+
+### Research context
+
+- [Nishimori transfer-matrix central charge](https://arxiv.org/abs/cond-mat/0010143)
+- [Transfer-matrix conformal spectra at monitored transitions](https://arxiv.org/abs/2107.03393)
+- [Born-rule self-dual mixed-state criticality](https://arxiv.org/abs/2502.14034)