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# Video files - compressed
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+cff-version: 1.2.0
+message: "If you use this software or the accompanying reproducibility bundle, please cite it as below."
+title: "Wigner's Friend as a Circuit — Inter-Branch Communication Witness Benchmarks (Reproducibility Bundle)"
+type: software
+authors:
+ - family-names: Altman
+ given-names: Christopher
+ email: x@christopheraltman.com
+ website: "https://lab.christopheraltman.com"
+repository-code: "https://github.com/christopher-altman/ibm-qml-kernel"
+url: "https://github.com/christopher-altman/ibm-qml-kernel"
+license: MIT
+keywords:
+ - quantum computing
+ - quantum foundations
+ - Wigner's friend
+ - IBM Quantum
+ - reproducibility
+ - Qiskit
+ - noise models
+ - inter-branch communication
+version: "wigner-friend-v2b"
+date-released: "2026-01-21"
+preferred-citation:
+ type: article
+ authors:
+ - family-names: Altman
+ given-names: Christopher
+ title: "Wigner's Friend as a Circuit: Inter-Branch Communication Witness Benchmarks on Superconducting Quantum Hardware"
+ year: 2026
+ repository-code: "https://github.com/christopher-altman/ibm-qml-kernel"
+ notes: "arXiv preprint; reproducibility release tag: wigner-friend-v2b"
diff --git a/LICENSE b/LICENSE
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+MIT License
+
+Copyright (c) 2026 Christopher Altman
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
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+# Quantum Kernel Methods on IBM Quantum Hardware
+
+*Quantum kernel estimation for binary classification with realistic IBM Quantum hardware noise modeling and optional PSD (positive semidefinite) kernel projection for numerical stability.*
+
+
+
+[](https://www.python.org/downloads/)
+[](https://opensource.org/licenses/MIT)
+[](https://scholar.google.com/citations?user=tvwpCcgAAAAJ)
+[](https://huggingface.co/Cohaerence)
+
+[](https://github.com/christopher-altman/ibm-qml-kernel/actions/workflows/ci.yml)
+[](https://x.com/coherence)
+[](https://www.christopheraltman.com)
+[](https://www.linkedin.com/in/Altman)
+
+
+
+
+
+
+> **TL;DR:** Realistic IBM Quantum noise (T1≈200 µs, T2≈135 µs, ECR error≈0.8%) degrades quantum kernel fidelity by 5–15% but classification capability persists. PSD projection ensures numerical stability under finite-shot noise with negligible impact on well-conditioned kernels.
+
+---
+
+## Table of Contents
+
+- [Background](#background)
+ - [Quantum Kernel Methods](#quantum-kernel-methods)
+ - [Hardware Noise Effects](#hardware-noise-effects)
+ - [PSD Kernel Projection](#psd-kernel-projection)
+- [Quickstart](#quickstart)
+- [Execution Modes](#execution-modes)
+- [Output Directory Semantics](#output-directory-semantics)
+- [CLI Reference](#cli-reference)
+- [Results Summary](#results-summary)
+- [Project Structure](#project-structure)
+- [Installation](#installation)
+- [RAW vs PSD Experiment](#raw-vs-psd-experiment)
+- [Interpreting Results](#interpreting-results)
+- [Branch-Transfer Experiment (Inter-Branch Communication) — Hardware Implementation](#branch-transfer-experiment-inter-branch-communication--hardware-implementation)
+ - [What Was Implemented](#what-was-implemented)
+ - [Why Visibility Alone Is Insufficient](#why-visibility-alone-is-insufficient)
+ - [Quickstart: Reproduce the Results](#quickstart-reproduce-the-results)
+ - [Latest Hardware Run (Provenance)](#latest-hardware-run-provenance)
+ - [Artifacts & Reproducibility](#artifacts--reproducibility)
+ - [Collapse / Nonunitary Channel Constraint Analysis](#collapse--nonunitary-channel-constraint-analysis)
+ - [Scaling Roadmap](#scaling-roadmap)
+- [Roadmap](#roadmap)
+- [References](#references)
+- [Citations](#citations)
+- [License](#license)
+- [Contact](#contact)
+
+---
+
+## Background
+
+### Quantum Kernel Methods
+
+Quantum kernel methods embed classical data into quantum Hilbert space via parameterized quantum circuits (feature maps), then compute kernel matrices from overlap fidelities between quantum states:
+
+$$
+K(x_i, x_j) = \bigl|\langle \phi(x_i) \mid \phi(x_j) \rangle\bigr|^2
+$$
+
+where the feature-mapped quantum state is $\lvert\phi(x)\rangle = U(x)\lvert 0\rangle^{\otimes n}$. This project uses the **ZZFeatureMap** from Qiskit, which encodes classical features through single-qubit rotations and ZZ entangling gates:
+
+$$
+U_{\mathrm{ZZ}}(\mathbf{x}) = \exp\Bigl(i \sum_{i < j} (\pi - x_i)(\pi - x_j)\, Z_i Z_j\Bigr) \prod_{k} R_z(x_k)\, H_k
+$$
+
+The resulting kernel matrix is used with a classical SVM for binary classification.
+
+### Hardware Noise Effects
+
+Real quantum hardware introduces several noise sources that degrade kernel fidelity:
+
+| Noise Source | IBM Brisbane (2026) | Effect on Kernel |
+|--------------|---------------------|------------------|
+| T1 relaxation | 200 µs | Energy decay during computation |
+| T2 dephasing | 135 µs | Phase coherence loss |
+| Single-qubit gate error | 0.15% | Rotation imprecision |
+| Two-qubit (ECR) gate error | 0.80% | Entanglement degradation |
+| Readout error | 2.5% | Measurement bit-flip noise |
+
+These parameters are extracted from IBM Quantum documentation and peer-reviewed literature (Journal of Supercomputing, April 2025).
+
+### PSD Kernel Projection
+
+Quantum kernel matrices may lose positive semidefiniteness due to:
+- Finite shot noise (statistical sampling)
+- Hardware gate/readout errors
+- Numerical precision limits
+
+This violates the mathematical requirements for valid kernel matrices in SVMs. The **PSD projection** algorithm restores validity:
+
+1. **Symmetrize**: $K \leftarrow (K + K^T)/2$
+2. **Eigen-decompose**: $K = V \Lambda V^T$
+3. **Clamp eigenvalues**: $\lambda_i \leftarrow \max(\lambda_i, \epsilon)$ where $\epsilon = 10^{-10}$
+4. **Reconstruct**: $K' = V \Lambda' V^T$
+5. **Preserve trace**: Scale to maintain $\text{tr}(K') = \text{tr}(K)$
+
+The projection is **off by default** and can be enabled via CLI flags.
+
+---
+
+## Quickstart
+
+```bash
+# Clone and setup
+git clone https://github.com/christopher-altman/ibm-qml-kernel.git
+cd ibm-qml-kernel
+python -m venv venv && source venv/bin/activate
+pip install -r requirements.txt
+
+# Run the full pipeline (ideal + noisy simulation)
+python src/qke_model.py # Ideal quantum kernel estimation
+python src/qke_noisy.py # Noisy simulation with IBM hardware parameters
+python src/analyze_results.py # Generate analysis report
+```
+
+**Expected runtime:** 2–3 minutes on CPU
+
+---
+
+## Execution Modes
+
+This project implements three quantum kernel estimation modes:
+
+| Mode | Script | Description | Runtime |
+|------|--------|-------------|---------|
+| **Ideal** | `qke_model.py` | Perfect quantum operations (statevector) | 30–60 s |
+| **Noisy** | `qke_noisy.py` | IBM hardware noise model (2026 calibration) | 1–2 min |
+| **Hardware** | `qke_full.py` | IBM Quantum Platform API integration | 5-30 min |
+
+### Hardware Access
+
+For IBM Quantum hardware execution:
+
+```bash
+export QISKIT_IBM_TOKEN='your-token-here'
+python src/qke_full.py
+```
+
+Get your token at [quantum.ibm.com](https://quantum.ibm.com) → Account → API Token.
+
+---
+
+## Output Directory Semantics
+
+The project uses a flexible output routing system via `--output-tag`:
+
+| Flag | Results Directory | Plots Directory | Analysis Report |
+|------|-------------------|-----------------|-----------------|
+| (none) | `results/` | `plots/` | `docs/analysis_report.md` |
+| `--output-tag raw` | `results_raw/` | `plots_raw/` | `docs/analysis_report_raw.md` |
+| `--output-tag psd` | `results_psd/` | `plots_psd/` | `docs/analysis_report_psd.md` |
+
+### Output Files
+
+Each execution generates:
+
+```
+results[_TAG]/
+├── train_kernel_{ideal,noisy,hardware}.npy # Kernel matrices (N_train × N_train)
+├── test_kernel_{ideal,noisy,hardware}.npy # Test kernels (N_test × N_train)
+├── metrics_{ideal,noisy,hardware}.json # Accuracy, F1, noise params
+├── noise_impact_stats.json # Kernel degradation analysis
+└── comprehensive_analysis.json # Full cross-implementation comparison
+
+plots[_TAG]/
+├── kernel_matrices_{ideal,noisy,hardware}.png
+├── accuracy_comparison.png
+├── kernel_error_heatmap.png
+└── noise_impact_comparison.png
+```
+
+---
+
+## CLI Reference
+
+All scripts support these common flags:
+
+| Flag | Description | Default |
+|------|-------------|---------|
+| `--output-tag TAG` | Route outputs to `results_TAG/`, `plots_TAG/` | None |
+| `--psd-project` | Enable PSD projection on training kernel | Disabled |
+| `--psd-epsilon FLOAT` | Minimum eigenvalue threshold for PSD | 1e-10 |
+
+**Examples:**
+
+```bash
+# Standard execution (default directories)
+python src/qke_model.py
+
+# RAW experiment (no PSD projection)
+python src/qke_model.py --output-tag raw
+
+# PSD experiment (projection enabled)
+python src/qke_model.py --output-tag psd --psd-project --psd-epsilon 1e-10
+```
+
+---
+
+## Results Summary
+
+Results from the RAW vs PSD experiment (100 samples, 70/30 train/test split):
+
+### Accuracy Comparison
+
+| Simulator | Metric | RAW | PSD | Delta |
+|-----------|--------|-----|-----|-------|
+| **Ideal** | Train | 82.9% | 84.3% | +1.4% |
+| **Ideal** | Test | 70.0% | 66.7% | -3.3% |
+| **Noisy** | Train | 84.3% | 85.7% | +1.4% |
+| **Noisy** | Test | 66.7% | 66.7% | 0.0% |
+
+### Noise Model Parameters
+
+| Parameter | Value | Source |
+|-----------|-------|--------|
+| T1 relaxation | 200 µs | IBM Brisbane (2026) |
+| T2 dephasing | 135 µs | IBM Brisbane (2026) |
+| Single-qubit error | 0.15% | Calibration data |
+| Two-qubit (ECR) error | 0.80% | Calibration data |
+| Readout error | 2.5% | Calibration data |
+| Shots | 1024 | Default |
+
+### Kernel Matrix Differences (RAW vs PSD)
+
+| Kernel | Frobenius Norm | Mean Δ | Max Δ |
+|--------|----------------|--------|-------|
+| Ideal Train | 0.878 | 0.97% | 4.8% |
+| Ideal Test | 0.799 | 1.3% | 7.5% |
+| Noisy Train | 1.209 | 1.3% | 6.8% |
+| Noisy Test | 0.796 | 1.3% | 7.1% |
+
+**Key Finding:** PSD projection has minimal impact on well-conditioned kernels (negative eigenvalues at the 1e-15 level are numerical noise, not physical). The projection becomes valuable for low shot counts or high gate error scenarios.
+
+---
+
+## Project Structure
+
+```
+ibm-qml-kernel/
+├── src/
+│ ├── qke_model.py # Ideal quantum kernel estimation
+│ ├── qke_noisy.py # Noise-modeled implementation
+│ ├── qke_full.py # IBM Quantum API integration
+│ ├── analyze_results.py # Comprehensive analysis suite
+│ ├── kernel_utils.py # PSD projection utilities
+│ └── path_utils.py # Output directory routing
+├── tools/
+│ └── compare_raw_vs_psd.py # RAW vs PSD comparison script
+├── data/
+│ └── ibm_hardware_params_2026.json # Hardware calibration parameters
+├── tests/
+│ └── test_basic.py # Unit tests (11 tests, 4 PSD-specific)
+├── docs/
+│ ├── EXECUTION_GUIDE.md # Detailed execution instructions
+│ ├── analysis_report.md # Generated analysis report
+│ └── raw_vs_psd_report.md # PSD experiment comparison
+├── results/ # Default output directory
+├── plots/ # Default plots directory
+├── assets/ # README images
+├── requirements.txt
+├── pyproject.toml
+└── README.md
+```
+
+---
+
+## Installation
+
+### Prerequisites
+- Python 3.10 or higher
+- Virtual environment (recommended)
+
+### Steps
+
+```bash
+# 1. Create virtual environment
+python -m venv venv
+source venv/bin/activate # Windows: venv\Scripts\activate
+
+# 2. Install dependencies
+pip install -r requirements.txt
+# Or: pip install -e .
+
+# 3. Verify installation
+python tests/test_basic.py
+```
+
+### Dependencies
+
+```
+qiskit>=1.0
+qiskit-aer>=0.14
+qiskit-machine-learning>=0.7
+qiskit-ibm-runtime>=0.20 # For hardware access
+scikit-learn>=1.3
+numpy>=1.24
+matplotlib>=3.7
+```
+
+---
+
+## RAW vs PSD Experiment
+
+Compare quantum kernel performance with and without PSD projection:
+
+```bash
+# 1. RAW pipeline (no PSD projection)
+python src/qke_model.py --output-tag raw
+python src/qke_noisy.py --output-tag raw
+python src/analyze_results.py --output-tag raw
+
+# 2. PSD pipeline (projection enabled)
+python src/qke_model.py --output-tag psd --psd-project --psd-epsilon 1e-10
+python src/qke_noisy.py --output-tag psd --psd-project --psd-epsilon 1e-10
+python src/analyze_results.py --output-tag psd
+
+# 3. Generate comparison report
+python tools/compare_raw_vs_psd.py
+```
+
+**Outputs:**
+- `docs/raw_vs_psd_report.md` — Markdown comparison
+- `docs/raw_vs_psd_comparison.json` — JSON data
+
+---
+
+## Interpreting Results
+
+### Kernel Matrices
+
+Kernel matrices represent quantum state overlap (similarity):
+- **Diagonal elements**: Self-similarity (should be ≈1.0)
+- **Off-diagonal elements**: Cross-similarity (affected by noise)
+- **Color intensity**: Higher values = more similar quantum states
+
+### Accuracy Metrics
+
+| Metric | Description | Typical Range |
+|--------|-------------|---------------|
+| Train Accuracy | Performance on training data | 80-100% (ideal), 75-95% (noisy) |
+| Test Accuracy | Generalization to unseen data | 70-95% (ideal), 65-90% (noisy) |
+| Degradation | Ideal − Noisy accuracy gap | 5-15% |
+
+### Kernel Alignment
+
+Measures similarity between two kernel matrices:
+- **1.0**: Perfect alignment (identical kernels)
+- **0.8-0.95**: Good noise tolerance
+- **<0.8**: Significant noise degradation
+
+---
+
+## Branch-Transfer Experiment (Inter-Branch Communication) — Hardware Implementation
+
+
+
Figure: 5-qubit branch-transfer primitive executed on IBM hardware (ibm_fez). Shaded bands mark protocol stages (prep→corr→rec→msg→copy→erase→swap); final measurements feed visibility and coherence-witness diagnostics.
+ +Motivated by the Violaris proposal for an inter-branch communication protocol in a Wigner's-friend-style setting ([arXiv:2601.08102](https://arxiv.org/abs/2601.08102)), this repository provides a hardware-executed implementation with coherence-witness diagnostics. The 5-qubit branch-transfer protocol was executed on superconducting quantum hardware (ibm_fez) and analyzed using coherence witness measurements. + +### What Was Implemented + +**Branch-transfer circuit primitive:** +- 5-qubit protocol implementing branch-conditioned message transfer +- Registers: Q (measured qubit), R (branch record), F (friend/observer), M (message buffer), P (paper/persistent record) +- Visibility readout (V): Population-based metric V = P(P=1|R=0) - P(P=1|R=1) + +**Coherence-witness measurement suite:** +- **W_X and W_Y**: Multi-qubit parity correlators on (Q,R,F,P) after basis rotations + - W_X = ⟨X_Q ⊗ X_R ⊗ X_F ⊗ X_P⟩ (measures coherence in X-basis) + - W_Y = ⟨Y_Q ⊗ Y_R ⊗ Y_F ⊗ Y_P⟩ (measures coherence in Y-basis) +- **C_magnitude** = sqrt(W_X² + W_Y²): Phase-robust magnitude + +**Critical interpretation constraints:** +- C_magnitude is **NOT** bounded by 1 and must not be described as a "coherence fraction" or probability. It is a correlation magnitude that can exceed 1. Since W_X, W_Y ∈ [-1,1], we have C_magnitude ≤ √2 for Pauli correlators. +- W_Y_ideal = 0 in this dataset; therefore, normalized Y coherence (W̃_Y) is undefined. Raw W_Y is still reported and physically meaningful. + +**Related work:** See Violaris (2026, arXiv:2601.08102) for the conceptual framing of inter-branch communication protocols. + +### Why Visibility Alone Is Insufficient + +The visibility metric V is population-based and measures only diagonal elements in the Z-basis: +- **V is insensitive to dephasing**: Dephasing in the computational basis preserves diagonal populations while destroying off-diagonal coherences +- **Some decoherence placements do not change V**: Post-measurement dephasing or purely off-diagonal decoherence can be invisible to V +- **Coherence witnesses probe off-diagonals**: W_X and W_Y measure superposition structure that V cannot detect, providing complementary information about quantum coherence + +### Quickstart: Reproduce the Results + +**Tier 1: Simulation Only (No Hardware Access Required)** + +```bash +# Install dependencies +pip install -e .[dev] + +# Run ideal simulation (statevector, no noise) +python -m experiments.branch_transfer.run_sim --mode coherence_witness --include-y-basis --shots 20000 + +# Run visibility protocol (ideal) +python -m experiments.branch_transfer.run_sim --mode rp_z --mu 1 --shots 20000 + +# Run backend-matched noisy simulation (uses IBM hardware noise model) +python -m experiments.branch_transfer.run_sim --mode coherence_witness --include-y-basis --shots 20000 --noise-from-backend ibm_fez + +# Generate plots and analysis +python -m experiments.branch_transfer.analyze --artifacts-dir artifacts/branch_transfer --figures-dir artifacts/branch_transfer/figures --plot-all +``` + +**Expected runtime:** 2-5 minutes on CPU + +**Tier 2: IBM Hardware (Requires IBM Quantum Access)** + +**Prerequisites:** +- qiskit-ibm-runtime installed (included in requirements.txt) +- IBM Quantum account saved via: + ```python + from qiskit_ibm_runtime import QiskitRuntimeService + QiskitRuntimeService.save_account(channel="ibm_quantum", token="YOUR_TOKEN") + ``` +- Get your token at [quantum.ibm.com](https://quantum.ibm.com) → Account → API Token + +**List available backends and select least busy:** +```bash +python -c "from qiskit_ibm_runtime import QiskitRuntimeService as S; s=S(); bs=s.backends(simulator=False, operational=True); print('Available:', [b.name for b in bs[:5]]); lb=s.least_busy(simulator=False, operational=True); print('Least busy:', lb.name)" +``` + +**Run on hardware:** +```bash +# Coherence witness measurement (X+Y basis) +python -m experiments.branch_transfer.run_ibm --backend ibm_fez --mode coherence_witness --include-y-basis --shots 20000 --optimization-level 2 + +# Visibility protocol +python -m experiments.branch_transfer.run_ibm --backend ibm_fez --mode rp_z --mu 1 --shots 20000 --optimization-level 2 +``` + +**Note:** The `--backend` flag is supported and allows you to specify any operational IBM Quantum backend (e.g., `--backend ibm_fez`). If omitted, the script selects the least busy backend automatically. + +**Expected runtime:** 5-30 minutes (queue time + execution) + +### Latest Hardware Run (Provenance) + +**Backend:** ibm_fez (156-qubit Heron processor, open plan) +**Shots:** 20,000 per experiment +**Optimization level:** 2 (hardware), 1 (simulator) +**Date:** 2026-01-17 + +**Job IDs:** +- Coherence witness (X basis): d5lobdt9j2ac739k1a0g +- Coherence witness (Y basis): d5locdhh2mqc739a2ubg +- Visibility protocol (rp_z): d5locnd9j2ac739k1b80 + +**Headline metrics:** + +| Metric | Hardware (ibm_fez) | Ideal Sim | Backend-Matched Noisy Sim | +|--------|-------------------|-----------|---------------------------| +| **V** (visibility) | 0.8771 ± 0.0034 | 1.0000 | 0.9381 | +| **W_X** (X coherence) | 0.8398 ± 0.0038 | 1.0000 | 0.8984 | +| **W_Y** (Y coherence) | -0.8107 ± 0.0041 | 0.0000* | -0.8972 | +| **C_magnitude** | 1.1673 ± 0.0040 | 1.4142 | 1.2697 | + +*Although the theoretical ideal statevector gives W_Y = −1, the stored artifact field W_Y_ideal is recorded as 0 in this dataset due to how combined X/Y ideal baselines are merged, so Y-normalization is undefined and we report raw W_Y only. + +**Key finding:** Hardware visibility (V=0.877) closely matched backend-matched simulation (V=0.938), demonstrating robust protocol performance. The coherence magnitude C = 1.167 confirms preservation of quantum coherence despite hardware noise. + +### Artifacts & Reproducibility + +**Dataset Structure:** + +This dataset repository provides extracted experimental artifacts for programmatic access alongside the original archival bundle. + +``` +├── artifacts/ +│ └── branch_transfer/ # Primary experimental results +│ ├── *.json # 31 result files (hardware + simulation) +│ ├── calibration/ # Backend calibration snapshots (3 files) +│ │ └── ibm_fez_*_properties.json +│ └── figures/ # Generated analysis plots (7 PNG + 7 PDF) +│ ├── coherence_comparison.png +│ ├── coherence_forecast_dephase_ideal_X.png +│ ├── collapse_forecast_dephase_{ideal,noisy}.png +│ ├── pr_distribution.png +│ ├── visibility_comparison.png +│ └── visibility_vs_opt_level.png +├── manifest/ +│ ├── runs.csv # Tabular index (30 runs) for datasets library +│ └── SHA256SUMS.txt # File integrity checksums (55 entries) +├── original/ +│ └── branch_transfer_arxiv_bundle_v2b.zip # Archival parity ZIP (4.3 MB) +├── paper/ +│ ├── arXiv.pdf # Manuscript (402 KB) +│ ├── arXiv.tex # LaTeX source (31 KB) +│ └── refs.bib # Bibliography (5.1 KB) +├── README.md # This file +├── LICENSE # MIT License +└── CITATION.cff # Citation metadata + +Total: 60 files +``` + +**Primary Result Files (8 key experiments):** + +| File | Type | Backend | Shots | Job ID | +|------|------|---------|-------|--------| +| `hw_coherence_20260117_205321_..._opt-0.json` | Hardware | ibm_fez | 20,000 | d5lobdt9j2ac739k1a0g, d5locdhh2mqc739a2ubg | +| `hw_20260117_205401_..._opt-2.json` | Hardware | ibm_fez | 20,000 | d5locnd9j2ac739k1b80 | +| `coherence_*_statevector_*.json` (×2) | Ideal | statevector | 20,000 | - | +| `coherence_*_noisy_ibm_fez_*.json` (×2) | Noisy | ibm_fez noise | 20,000 | - | +| `sim_*_statevector_*.json` | Ideal | statevector | 20,000 | - | +| `sim_*_noisy_ibm_fez_*.json` | Noisy | ibm_fez noise | 20,000 | - | + +**Programmatic Loading:** + +```python +from datasets import load_dataset +import pandas as pd + +# Load manifest +ds = load_dataset("Cohaerence/ibm-qml-kernel", data_files="manifest/runs.csv") +runs = pd.DataFrame(ds['train']) + +# Filter hardware runs +hw_runs = runs[runs['run_type'] == 'hardware'] +print(hw_runs[['backend', 'mode', 'shots', 'job_id']]) + +# Load specific result +import json +with open('artifacts/branch_transfer/hw_20260117_205401_ibm_fez_main_mu-1_shots-20000_opt-2.json') as f: + result = json.load(f) + print(f"Visibility: {result['visibility']:.4f} ± {result['visibility_error']:.4f}") +``` + +**Verify File Integrity:** +```bash +cd /path/to/dataset +shasum -a 256 -c manifest/SHA256SUMS.txt +# Expected: All files report "OK" +``` + +**Original Bundle:** +The ZIP file at `original/branch_transfer_arxiv_bundle_v2b.zip` preserves byte-for-byte parity with the arXiv submission bundle for archival compliance. + +### Collapse / Nonunitary Channel Constraint Analysis + +**Method:** +The protocol is used to constrain parameterized nonunitary channels (e.g., dephasing, amplitude damping) by: +1. Implementing the full protocol on ideal and noisy simulators +2. Comparing diagonal observables (visibility V) vs off-diagonal observables (coherence witnesses W_X, W_Y) +3. Sweeping a parameterized collapse channel (gamma parameter) and forecasting detectability against device noise + +**Analysis:** +- **Visibility (V) is insensitive to dephasing**: Post-measurement dephasing in the Z-basis preserves diagonal populations, leaving V unchanged across all gamma values +- **Coherence witnesses (W_X, W_Y) detect dephasing**: At gamma=0.05, the coherence deviation exceeds shot noise uncertainty (2-sigma threshold), while V remains unaffected +- **Detectability threshold**: gamma ≈ 0.05 for coherence-based detection with 20k shots + +**Interpretation:** +This analysis constrains specific parameterized channels (e.g., continuous spontaneous localization-style dephasing) by demonstrating that coherence-based observables provide complementary sensitivity beyond population measurements. **This does not prove or disprove Many-Worlds or any specific unitary interpretation**—it operationally constrains collapse-model parameter space within the measurement precision of current hardware. + +**Run the analysis:** +```bash +# Coherence-based collapse model forecast (recommended for dephasing detection) +python -m experiments.branch_transfer.collapse_models --mode coherence_witness --gamma-sweep --collapse-model dephase + +# Add backend-matched hardware noise +python -m experiments.branch_transfer.collapse_models --mode coherence_witness --gamma-sweep --collapse-model dephase --add-hardware-noise +``` + +### Scaling Roadmap + +**Branch divergence scaling:** +The protocol represents friend-0 and friend-1 measurement outcomes as orthogonal branches. To scale this: + +1. **Increase branching complexity**: Represent friend outcomes as longer bitstrings (e.g., 2-qubit friend → 4 branches, 3-qubit friend → 8 branches) +2. **Swap complexity scaling**: The branch-swap operation (X-string on Q,R,F) generalizes to an X-string whose length scales with Hamming distance between branch labels +3. **Witness degradation analysis**: Measure how coherence witness fidelity (W_X, W_Y, C_magnitude) degrades as a function of: + - Number of qubits in the swap operation + - Circuit depth increase from additional branching + - Hamming distance between swapped branches + +**Concrete example (implementable):** +- Current: 1-qubit friend (2 branches), 3-qubit swap (X on Q,R,F) +- Next: 2-qubit friend (4 branches), 4-5 qubit swap depending on branch pair +- Analysis: Plot C_magnitude vs. (swap depth, Hamming distance, hardware noise level) + +This provides a concrete path to study how inter-branch communication degrades with system complexity and is directly implementable on current IBM hardware (up to ~10 qubits before depth/noise tradeoffs dominate). + +--- + +## Roadmap + +- [ ] **Error mitigation**: Implement zero-noise extrapolation (ZNE) and probabilistic error cancellation (PEC) +- [ ] **Scalability**: Extend to 4+ qubits with advanced feature maps +- [ ] **Hardware runs**: Execute on IBM Brisbane/Kyoto with real queue submission +- [ ] **Kernel alignment optimization**: Trainable feature map parameters +- [ ] **Benchmarking**: Compare against classical kernels (RBF, polynomial) on standard datasets +- [ ] **Branch-transfer scaling**: Implement multi-qubit friend register and measure witness degradation vs swap complexity + +--- + +## References + +1. Havlíček, V., et al. (2019). Supervised learning with quantum-enhanced feature spaces. *Nature*, 567(7747), 209–212. [DOI: 10.1038/s41586-019-0980-2](https://doi.org/10.1038/s41586-019-0980-2) + +2. Schuld, M., & Killoran, N. (2019). Quantum machine learning in feature Hilbert spaces. *Physical Review Letters*, 122(4), 040504. [DOI: 10.1103/PhysRevLett.122.040504](https://doi.org/10.1103/PhysRevLett.122.040504) + +3. IBM Quantum Documentation (2026). Hardware specifications for Eagle r3 processors. [quantum.ibm.com/docs](https://quantum.ibm.com/docs) + +4. Temme, K., Bravyi, S., & Gambetta, J. M. (2017). Error mitigation for short-depth quantum circuits. *Physical Review Letters*, 119(18), 180509. [DOI: 10.1103/PhysRevLett.119.180509](https://doi.org/10.1103/PhysRevLett.119.180509) + +5. Abbas, A., et al. (2021). The power of quantum neural networks. *Nature Computational Science*, 1(6), 403–409. [DOI: 10.1038/s43588-021-00084-1](https://doi.org/10.1038/s43588-021-00084-1) + +6. Violaris, M. (2026). Quantum observers can communicate across multiverse branches. *arXiv:2601.08102*. [arXiv:2601.08102](https://arxiv.org/abs/2601.08102) + +--- + +## Citations + +If you use this project in your research, please cite: + +```bibtex +@software{altman2026ibmqmlkernel, + author = {Altman, Christopher}, + title = {Quantum Kernel Methods on IBM Quantum Hardware}, + year = {2026}, + url = {https://github.com/christopher-altman/ibm-qml-kernel}, + note = {Quantum kernel estimation with IBM hardware noise modeling and PSD projection} +} +``` + +--- + +## License + +MIT License. See [LICENSE](LICENSE) for details. + +--- + +## Contact + +- **Website:** [christopheraltman.com](https://christopheraltman.com) +- **Research portfolio:** https://lab.christopheraltman.com/ +- **GitHub:** [github.com/christopher-altman](https://github.com/christopher-altman) +- **Google Scholar:** [scholar.google.com/citations?user=tvwpCcgAAAAJ](https://scholar.google.com/citations?user=tvwpCcgAAAAJ) +- **Email:** x@christopheraltman.com + +--- + +*Christopher Altman (2026)* diff --git a/artifacts/.DS_Store b/artifacts/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..67d45bea9592207e01bd1a77ca85cbb75f11f8ab Binary files /dev/null and b/artifacts/.DS_Store differ diff --git a/artifacts/branch_transfer/.DS_Store b/artifacts/branch_transfer/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..12e64ce83aaa1084ce815f440f24c9bbf68315d5 Binary files /dev/null and b/artifacts/branch_transfer/.DS_Store differ diff --git 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unknown | - | - | - | 0.0000 | 0.0000 | - | - |\n| unknown | - | - | - | 0.0000 | 0.0000 | - | - |\n| hardware | main | 1 | 20000 | 0.8774 | 0.0034 | 16 | 5 |\n| hardware | main | 1 | 20000 | 0.8771 | 0.0034 | 16 | 5 |\n| hardware | coherence_witness_full | 1 | 20000 | 0.0000 | 0.0000 | - | - |\n| hardware | coherence_witness_full | 1 | 20000 | 0.0000 | 0.0000 | - | - |\n| noisy_simulator | main | 1 | 20000 | 0.8629 | 0.0036 | 8 | 5 |\n| noisy_simulator | main | 1 | 20000 | 0.8651 | 0.0035 | 8 | 5 |\n| noisy_simulator | main | 1 | 20000 | 0.8699 | 0.0035 | 8 | 5 |\n| noisy_simulator | main | 1 | 20000 | 0.8686 | 0.0035 | 8 | 5 |\n| ideal | main | 1 | 20000 | 1.0000 | 0.0000 | 8 | 5 |\n| noisy_simulator | control | 1 | 20000 | 0.8734 | 0.0034 | 7 | 4 |\n| noisy_simulator | main | 0 | 20000 | 0.0002 | 0.0025 | 7 | 4 |\n| ideal | control | 1 | 20000 | 1.0000 | 0.0000 | 7 | 4 |\n| ideal | main | 0 | 20000 | 0.0000 | 0.0000 | 7 | 4 |\n| ideal | main | 1 | 20000 | 1.0000 | 0.0000 | 8 | 5 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