Learn Quantum Computing by Running Real Experiments - No PhD Required
Built with Perplexity Pro in under 20 minutes. Every experiment runs locally on your computer using Origin Quantum's QPanda3 simulator.
This is a beginner-friendly, hands-on quantum computing lab that you can run on your own computer. Instead of reading theory for months, you'll run 20 real quantum experiments and see quantum mechanics in action through charts and results.
No cloud account needed. No quantum hardware required. Just Python and curiosity.
| # | Experiment | What It Teaches You | Real-World Connection |
|---|---|---|---|
| 1 | Quantum Coin Flip | Superposition - a qubit can be 0 AND 1 | Random number generation for security |
| 2 | Quantum Entanglement | Two qubits linked across any distance | Foundation of quantum internet |
| 3 | Quantum Random Number Generator | True randomness (not pseudo-random) | Cryptography, lottery systems |
| 4 | Grover's Search | Searching a database quadratically faster | Database search, optimization |
| 5 | Quantum Teleportation | Transfer quantum states instantly | Quantum networking |
| 6 | Deutsch-Jozsa Algorithm | Determine function type in 1 shot | Algorithm speedup proof |
| 7 | Quantum Fourier Transform | Frequency analysis on qubits | Shor's algorithm (breaks RSA) |
| 8 | VQE (Variational Quantum Eigensolver) | Find minimum energy of molecules | Drug discovery, materials science |
| 9 | Noise Simulation | How real quantum computers make errors | Understanding hardware limitations |
| 10 | Quantum Coin Game | Quantum strategy always beats classical | Game theory, decision making |
| 11 | Quantum Password Cracker | Grover's algorithm finds passwords faster | Cybersecurity implications |
| 12 | Quantum Error Correction | Protecting fragile qubits from errors | Building reliable quantum computers |
| 13 | Quantum Random Walk | Quantum particles spread faster than classical | Why quantum search is faster |
| 14 | BB84 Key Distribution | Create unbreakable encryption keys | Bank security, government comms |
| 15 | Superdense Coding | Send 2 bits using 1 qubit | Doubling communication capacity |
NEW! The next frontier: combining Artificial Intelligence with Quantum Computing.
| # | Experiment | What It Teaches You | Real-World Connection |
|---|---|---|---|
| 13 | Quantum Fruit Classifier | Quantum feature maps + classical SVM | Quantum-enhanced pattern recognition |
| 14 | Hybrid Quantum Neural Network | PyTorch + QPanda3 quantum hidden layer | Google/IBM quantum ML architecture |
| 15 | Quantum Random Forest | True quantum randomness powers AI decisions | Unhackable AI for banking/security |
| 16 | Quantum vs Classical Showdown | 5 algorithms compete on hard dataset | Understanding where quantum helps |
| 17 | Quantum Stock Predictor | Quantum+Classical hybrid for finance | JPMorgan/Goldman Sachs quantum research |
Most tutorials teach quantum OR AI separately. This repo runs hybrid quantum-classical code where PyTorch neural networks have quantum circuit layers. Not theory — working code.
We show experiments where quantum wins AND where classical wins. Experiment #16 (The Showdown) is a head-to-head battle with honest accuracy numbers. This teaches you when quantum helps and when it doesn't — something most tutorials hide.
Everything runs on your laptop using QPanda3's local simulator. No IBM Quantum account, no AWS Bracket, no Google Cloud credits needed. Zero cost, zero barriers.
Most quantum ML tutorials use Qiskit + PennyLane. We demonstrate hybrid neural networks using PyTorch + QPanda3 — a combination almost no one else has documented publicly. This is original work.
Every experiment includes comments explaining the quantum physics in simple language with real-world analogies (mangoes, cricket, shopping malls) — not math symbols.
We cover all 5 major patterns that companies like Google, IBM, and JPMorgan are researching:
| Architecture | Our Experiment | Who Uses This |
|---|---|---|
| Quantum Feature Map + Classical ML | Exp 13 (Fruit Classifier) | IBM Quantum, Xanadu |
| Hybrid Quantum Neural Network | Exp 14 (Quantum NN) | Google Quantum AI |
| Quantum Random Number Generation for AI | Exp 15 (Quantum Random Forest) | ID Quantique, Banks |
| Quantum vs Classical Benchmarking | Exp 16 (Showdown) | All quantum ML research |
| Hybrid Quantum-Classical Finance | Exp 17 (Stock Predictor) | JPMorgan, Goldman Sachs |
This entire lab — 20 experiments, charts, explanations, and this README — was built and tested in a single session using Perplexity Pro as an AI coding assistant. What would take a developer 3-5 days of research and coding was done in about 20 minutes.
Explore all 20 experiments through an interactive web dashboard with animated KPIs, filterable experiment cards, AI vs Quantum comparison charts, and a quantum concepts map.
The dashboard is in the dashboard/ folder — just open dashboard/index.html in any browser. No server needed.
Features:
- Animated KPI counters (experiments, qubits, gates, quantum shots)
- Filterable experiment explorer with detail modals
- Classical vs Quantum accuracy bar chart
- 14 quantum concepts with real-world applications
- Dark/light theme toggle
- Fully responsive (mobile to desktop)
- Python 3.10 or higher
- pip (Python package manager)
git clone https://github.com/zkrishna12/Perplexity-Max-Quantum-Computing-Setup.git
cd Perplexity-Max-Quantum-Computing-Setuppython -m venv quantum-env
# Windows:
quantum-env\Scripts\activate
# Mac/Linux:
source quantum-env/bin/activateFor quantum experiments only (Part 1):
pip install pyqpanda3 matplotlib numpyFor AI + Quantum experiments (Part 2) — also install:
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install scikit-learn pandasOr install everything at once:
pip install -r requirements.txt# Quantum fundamentals:
python experiments/01_coin_flip.py
# AI + Quantum:
python experiments/13_quantum_fruit_classifier.pyThis entire lab was built and run using Perplexity Pro as an AI coding assistant. Here's how you can do the same:
- Open Perplexity Pro (perplexity.ai)
- Paste this prompt:
"I have Python installed on my computer. Help me set up Origin Quantum's QPanda3 and run quantum computing experiments. Start with a simple quantum coin flip and explain the results in plain English."
- Perplexity will:
- Create the virtual environment for you
- Install all dependencies
- Write and run the experiment code
- Generate charts showing results
- Explain everything in simple language
- To run more experiments, just say:
"Run experiment #14 (Hybrid Quantum Neural Network) and explain what it means"
Total setup time: ~5 minutes. Perplexity handles all the coding - you just learn.
quantum-computing-lab/
├── README.md # This file
├── SPECIFICATIONS.md # Technical specs for all experiments
├── requirements.txt # Python dependencies
├── guide/
│ └── Quantum_Computing_Lab_Guide.pdf # Complete PDF guide
├── experiments/
│ ├── 01_coin_flip.py # Quantum coin flip
│ ├── 02_entanglement.py # Quantum entanglement
│ ├── 03_random_numbers.py # True random number generator
│ ├── 04_grovers_search.py # Grover's search algorithm
│ ├── 05_teleportation.py # Quantum teleportation
│ ├── 06_advanced_algorithms.py # DJ, QFT, VQE, noise sim
│ ├── 07_coin_game.py # Quantum coin game
│ ├── 08_password_cracker.py # Quantum password cracker
│ ├── 09_error_correction.py # 3-qubit error correction
│ ├── 10_random_walk.py # Quantum random walk
│ ├── 11_bb84_key_distribution.py # BB84 quantum cryptography
│ ├── 12_superdense_coding.py # Superdense coding
│ ├── 13_quantum_fruit_classifier.py # [AI+Q] Quantum feature map + SVM
│ ├── 14_hybrid_quantum_neural_network.py # [AI+Q] PyTorch + QPanda3 hybrid NN
│ ├── 15_quantum_random_forest.py # [AI+Q] Quantum RNG + Random Forest
│ ├── 16_quantum_vs_classical_showdown.py # [AI+Q] 5-algorithm comparison
│ └── 17_quantum_stock_predictor.py # [AI+Q] Hybrid finance prediction
├── dashboard/ # Interactive web dashboard
│ ├── index.html # Main HTML (open in browser)
│ ├── base.css # CSS reset & foundations
│ ├── style.css # Full dashboard styling
│ ├── app.js # Data, routing, charts & interactions
│ └── README.md # Dashboard documentation
└── charts/ # Generated chart images
└── (auto-generated when you run experiments)
- Hands-on portfolio - show employers you've run quantum AND AI+quantum experiments
- Foundation for quantum computing courses and certifications
- Understanding that goes beyond textbook theory
- Stay ahead - quantum + AI is the next enterprise wave
- Client conversations - explain quantum concepts confidently
- Career positioning - hybrid quantum-AI skills are the rarest in the market
- 5 working AI+Quantum architectures ready to adapt
- Honest benchmarks showing where quantum helps (and where it doesn't yet)
- PyTorch + QPanda3 integration you won't find anywhere else
- Demystify both quantum computing AND AI without needing a physics degree
- See results immediately - every experiment produces visual output
- Plain English explanations - no jargon, no complex math
| Resource | Platform | Level | Link |
|---|---|---|---|
| Quantum Computing for Everyone | edX (UChicago) | Beginner | Link |
| Qiskit Textbook | IBM | Beginner-Advanced | Link |
| Quantum Computing Course | freeCodeCamp (YouTube) | Beginner | Link |
| Quantum 101 | Microsoft Learn | Beginner | Link |
| Linux Foundation QC Fundamentals | Linux Foundation | Beginner | Link |
| Resource | What It Covers |
|---|---|
| PennyLane Demos | Quantum ML tutorials with code |
| TensorFlow Quantum | Google's quantum ML framework |
| Qiskit Machine Learning | IBM's quantum ML module |
| Quantum Machine Learning (Nature) | Research paper on QML foundations |
- Quantum Soar - Simple algorithm explanations
- Qiskit - Official IBM quantum channel
- Quantum Sense - Strong on foundations
- freeCodeCamp - Full beginner course
- Google Quantum AI - Latest hardware breakthroughs
- "Quantum Computing for the Quantum Curious" - Open access (Archive.org)
- "Introduction to Classical and Quantum Computing" by Tom Wong - Only needs trigonometry
- "Learn Quantum Computation using Qiskit" - Free IBM textbook (GitHub)
- Origin Quantum QPanda3 - What this lab uses (originqc.com)
- IBM Quantum Experience - Free access to real quantum computers (quantum.ibm.com)
- Google Cirq - Google's quantum framework (quantumai.google/cirq)
- Microsoft QDK - Q# language and Azure Quantum (learn.microsoft.com)
This lab uses QPanda3 (Quantum Programming Architecture for NISQ Device Application v3), developed by Origin Quantum - China's leading quantum computing company. QPanda3 is:
- Open source and freely available via
pip install pyqpanda3 - 20x faster circuit construction than Qiskit (benchmarked)
- Connected to Origin Wukong, a 72-qubit superconducting quantum processor
- Supports local simulation (what we use) and cloud-based real hardware execution
Found a bug? Want to add an experiment? PRs are welcome!
- Fork this repo
- Create a branch (
git checkout -b new-experiment) - Add your experiment in the
experiments/folder - Submit a Pull Request
MIT License - Use freely for learning, teaching, and sharing.
Built with love using Perplexity Pro — from zero to 20 quantum experiments in record time.