Srivardhini-S

Srivardhini-S / quantum-noise-signal-classifier

Public

Quantum Machine Learning project with noise filtering and signal classification

30
0
100% credibility
Found Mar 31, 2026 at 30 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project generates synthetic noisy signals, filters them, and compares the classification accuracy of a classical machine learning model against a basic quantum circuit model, displaying results in a bar graph.

How It Works

1
🔍 Discover the Project

You come across a fun experiment comparing everyday computer smarts with quantum tricks for sorting out signals hidden in noise.

2
💻 Get It Ready

Download the simple files to your computer and set up the basic tools it needs with easy steps.

3
📡 Make Noisy Signals

The project creates pretend signals mixed with fuzz, just like messy real-world data you might encounter.

4
🧹 Smooth Out the Noise

It gently cleans up the signals by averaging out the roughness, making patterns easier to spot.

5
🤖 Try Both Ways

Test the traditional guessing method and the quantum approach to classify the cleaned signals.

6
📊 Watch the Comparison

A clear bar chart appears, showing side-by-side how well each method performs on your noisy data.

🎉 Learn the Winner

You see the accuracies and discover insights into when quantum magic might outperform regular methods.

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Star Growth

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AI-Generated Review

What is quantum-noise-signal-classifier?

This Python project demos a hybrid classical-quantum setup for classifying noisy signals, generating synthetic data with noise, applying basic filtering, and pitting logistic regression against a Qiskit-based quantum circuit. You get terminal output with accuracy scores plus a bar graph comparing both models—perfect for quick experiments in quantum machine learning and classification tasks amid github quantum algorithms or quantum mechanics noise. Run it via pip install -r requirements.txt then python main.py.

Why is it gaining traction?

It stands out by offering an instant side-by-side benchmark of classical vs quantum performance on real-world-like noisy data, without needing a quantum computer—just a simulator. Developers dig the simplicity for prototyping quantum machine intelligence, like filtering signals in quantum optics or github quantum leaps, beating verbose alternatives that demand custom circuits from scratch. The visualization hook makes quantum fusion tangible fast.

Who should use this?

Quantum machine learning PhD students or hobbyists testing quantum machine learning in feature Hilbert spaces basics. ML engineers exploring quantum safe classification for noisy sensors in quantum physics apps. Early-career quantum machines devs at places like quantum machines stuttgart, needing a runnable baseline before diving into quantum machines documentation.

Verdict

Skip for production—1.0% credibility score, 30 stars, thin docs, and no tests signal early prototype status—but grab it as a free, constructive starter for quantum github qbittorrent-style tinkering or quantum machine learning book companions. Worth a 15-minute spin if you're curious about quantum machines jobs in signal classification.

(178 words)

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