# saranyanalluri/comparative-cml-qml-benchmarking architecture

> Architecture diagram of the GitHub repository saranyanalluri/comparative-cml-qml-benchmarking, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-26). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/saranyanalluri/comparative-cml-qml-benchmarking
- Repository: https://github.com/saranyanalluri/comparative-cml-qml-benchmarking

## Overview

This repository is a set of runnable experiments comparing classical and quantum or quantum-enhanced machine-learning models across synthetic and real datasets. The principal workflow is to construct or load a dataset, optionally preprocess it, evaluate models using cross-validation, calculate accuracy and timing metrics, and save or summarize results. Experiments cover Iris, crop recommendation, RadioML, clustering/moons, Ising, ad hoc, bit parity, and bars-and-stripes; `visual.py` separately plots Iris and ad hoc data. Source excerpts are partial, so links from individual scripts to the collected result files are not asserted where output wiring is uncertain.

Main entry points: [`Quantum-Vs-Classical-ML-Analysis-main/results`](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/tree/main/Quantum-Vs-Classical-ML-Analysis-main/results) (Benchmark result files), [`Quantum-Vs-Classical-ML-Analysis-main/experiments/adhoc.py`](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/adhoc.py) (Ad hoc benchmark), [`Quantum-Vs-Classical-ML-Analysis-main/experiments/barsAndStripes.py`](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/barsAndStripes.py) (Bars and stripes), [`Quantum-Vs-Classical-ML-Analysis-main/experiments/bitparity.py`](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/bitparity.py) (Bit parity)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_benchmarks["Benchmark experiments"]
  node_adhoc["Ad hoc benchmark<br/>[adhoc.py]"]
  node_bars["Bars and stripes<br/>[barsAndStripes.py]"]
  node_bitparity["Bit parity<br/>[bitparity.py]"]
  node_clustering["Moons clustering<br/>[clustering.py]"]
  node_crop["Crop benchmark<br/>[crop.py]"]
  node_ising["Ising benchmark<br/>[Ising.py]"]
  node_iris["Iris benchmark<br/>[iris.py]"]
  node_irisupdate["Iris model variants<br/>[irisupdate.py]"]
  node_radioml["RadioML benchmark<br/>[radioml10a.py]"]
end

subgraph group_data["Datasets and inputs"]
  node_cropdata["Crop data"]
end

subgraph group_outputs["Results and visualization"]
  node_results["Benchmark result files"]
  node_visual["Dataset plots<br/>[visual.py]"]
end

node_researcher(("Experiment runner"))

node_researcher -->|"runs"| node_adhoc
node_researcher -->|"runs"| node_bars
node_researcher -->|"runs"| node_bitparity
node_researcher -->|"runs"| node_clustering
node_researcher -->|"runs"| node_crop
node_researcher -->|"runs"| node_ising
node_researcher -->|"runs"| node_iris
node_researcher -->|"runs"| node_irisupdate
node_researcher -->|"runs"| node_radioml
node_researcher -->|"runs"| node_visual
node_crop -->|"reads"| node_cropdata
node_adhoc -->|"writes results"| node_results
node_bars -->|"writes results"| node_results
node_bitparity -->|"writes results"| node_results
node_clustering -->|"writes results"| node_results
node_ising -->|"writes results"| node_results
node_iris -->|"writes results"| node_results
node_radioml -->|"writes results"| node_results

click node_adhoc "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/adhoc.py"
click node_bars "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/barsAndStripes.py"
click node_bitparity "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/bitparity.py"
click node_clustering "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/clustering.py"
click node_crop "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/crop.py"
click node_ising "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/Ising.py"
click node_iris "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/iris.py"
click node_irisupdate "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/irisupdate.py"
click node_radioml "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/radioml10a.py"
click node_cropdata "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/Crop_recommendation.csv"
click node_results "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/tree/main/Quantum-Vs-Classical-ML-Analysis-main/results"
click node_visual "https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/visual.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_adhoc,node_bars,node_bitparity,node_clustering,node_crop,node_ising,node_iris,node_irisupdate,node_radioml toneBlue
class node_cropdata toneAmber
class node_results,node_visual toneMint
class node_researcher toneIndigo
```

## Components

### Benchmark experiments

- [Ad hoc benchmark](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/adhoc.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/adhoc.py`
- [Bars and stripes](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/barsAndStripes.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/barsAndStripes.py`
- [Bit parity](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/bitparity.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/bitparity.py`
- [Moons clustering](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/clustering.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/clustering.py`
- [Crop benchmark](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/crop.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/crop.py`
- [Ising benchmark](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/Ising.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/Ising.py`
- [Iris benchmark](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/iris.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/iris.py`
- [Iris model variants](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/irisupdate.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/irisupdate.py`
- [RadioML benchmark](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/experiments/radioml10a.py): `Quantum-Vs-Classical-ML-Analysis-main/experiments/radioml10a.py`

### Datasets and inputs

- [Crop data](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/Crop_recommendation.csv): `Quantum-Vs-Classical-ML-Analysis-main/Crop_recommendation.csv`

### Results and visualization

- [Benchmark result files](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/tree/main/Quantum-Vs-Classical-ML-Analysis-main/results): `Quantum-Vs-Classical-ML-Analysis-main/results`
- [Dataset plots](https://github.com/saranyanalluri/comparative-cml-qml-benchmarking/blob/main/Quantum-Vs-Classical-ML-Analysis-main/visual.py): `Quantum-Vs-Classical-ML-Analysis-main/visual.py`

### Other components

- **Experiment runner**

## Connections

- Experiment runner → Ad hoc benchmark: runs
- Experiment runner → Bars and stripes: runs
- Experiment runner → Bit parity: runs
- Experiment runner → Moons clustering: runs
- Experiment runner → Crop benchmark: runs
- Experiment runner → Ising benchmark: runs
- Experiment runner → Iris benchmark: runs
- Experiment runner → Iris model variants: runs
- Experiment runner → RadioML benchmark: runs
- Experiment runner → Dataset plots: runs
- Crop benchmark → Crop data: reads
- Ad hoc benchmark → Benchmark result files: writes results
- Bars and stripes → Benchmark result files: writes results
- Bit parity → Benchmark result files: writes results
- Moons clustering → Benchmark result files: writes results
- Ising benchmark → Benchmark result files: writes results
- Iris benchmark → Benchmark result files: writes results
- RadioML benchmark → Benchmark result files: writes results