Improve QAOA reproducibility and docs navigation

Author: bernalde

README.md

@@ -5,8 +5,6 @@
 [![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/release/python-3100/)
 [![License](https://img.shields.io/github/license/bernalde/stochastic-benchmark)](LICENSE)
 
-Repository for Stochastic Optimization Solvers Benchmark code
-=======
 Repository for Stochastic Optimization Solvers Benchmark implementation of the Window Sticker framework.
 
 The benchmarking approach is described in this [preprint](https://arxiv.org/abs/2402.10255) titled: *Benchmarking the Operation of Quantum Heuristics and Ising Machines: Scoring Parameter Setting Strategies on Optimization Applications*.
@@ -18,6 +16,7 @@ Details of the implementation and an illustrative example for Wishart instances
 - [Background](#background)
 - [Installation](#installation)
 - [Examples](#examples)
+- [Documentation](#documentation)
 - [Testing](#testing)
 - [Contributors](#contributors)
 - [Acknowledgements](#acknowledgements)
@@ -99,6 +98,15 @@ The current package implements the following functionality:
 
 For a full demonstration of the stochastic-benchmark analysis in action, refer to the example notebooks located in the `examples` folder of this repository.
 
+## Documentation
+
+Use the root README as the entry point, then follow the focused documents for the part of the workflow you need:
+
+- [examples/general_workflow.md](examples/general_workflow.md) for the end-to-end benchmark flow
+- [CI-TESTING.md](CI-TESTING.md) for local CI reproduction and environment setup
+- [TESTING.md](TESTING.md) for the test suite overview
+- [examples/wishart_n_50_alpha_0.5/README.md](examples/wishart_n_50_alpha_0.5/README.md) for the Wishart example details
+
 ## Testing
 
 Tests can be executed using the helper script `run_tests.py`. Specify the type of

examples/QAOA_iterative/qaoa_demo.ipynb

@@ -352,6 +352,9 @@
     "# ============================================================\n",
     "# Execute Statistics Computation\n",
     "# ============================================================\n",
+    "# Fix the seed so the train/test split is reproducible across runs\n",
+    "np.random.seed(42)\n",
+    "\n",
     "# This will:\n",
     "# 1. Add 'train' column to sb.interp_results (True/False for each instance)\n",
     "# 2. Compute aggregate statistics for training and testing sets separately\n",