Capstone Project: Develop and Deploy a Real Quantum App

Table of Contents

1. Introduction
2. Project Objectives
3. Choosing a Problem Domain
4. Formulating a Research or Application Goal
5. Selecting the Right Quantum Algorithm
6. Data Preparation and Encoding Strategies
7. Designing the Quantum Circuit
8. Integrating Classical Components
9. Building the Application Architecture
10. Choosing Quantum Backends and Providers
11. Implementing a Real-Time Execution Loop
12. Visualizing Quantum Results
13. Developing a Web Interface (Optional)
14. Testing and Validating Your Application
15. Performance Benchmarking
16. Deploying on the Cloud or Web
17. Writing Technical Documentation
18. Recording a Demo and Final Report
19. Peer Review and Feedback
20. Conclusion and Future Directions

1. Introduction

The capstone project is the culmination of your quantum learning journey. It brings together quantum theory, software engineering, and practical implementation into one real-world application.

2. Project Objectives

• Solve a meaningful problem with a quantum approach
• Build a hybrid classical-quantum software application
• Deploy it on a cloud quantum provider or local simulator

3. Choosing a Problem Domain

Example domains:

• Optimization (e.g., job scheduling)
• Quantum ML (e.g., classification)
• Chemistry (e.g., ground state estimation)
• Cryptography (e.g., oracle-based algorithms)

4. Formulating a Research or Application Goal

Clearly define your app’s purpose:

• What problem are you solving?
• Why is quantum computing relevant here?
• What are your success criteria?

5. Selecting the Right Quantum Algorithm

Options include:

• QAOA, VQE, HHL, Grover’s, QPE, Quantum Kernels
• For ML: QNNs, QSVR, quantum clustering

6. Data Preparation and Encoding Strategies

Use basis, amplitude, angle encoding as per your input structure. Normalize classical data before quantum injection.

7. Designing the Quantum Circuit

• Use QuantumCircuit in Qiskit
• Parameterize gates if using variational methods
• Simulate and visualize before full integration

8. Integrating Classical Components

• Classical optimizer
• Feature preprocessing
• Post-classification decision logic

9. Building the Application Architecture

Use a modular design:

• Core quantum module
• Classical interface
• Result manager
• Optional: REST API or UI

10. Choosing Quantum Backends and Providers

Options:

• IBM Quantum (Qiskit Runtime)
• Amazon Braket (Cirq, PennyLane)
• Azure Quantum
• Xanadu Cloud

11. Implementing a Real-Time Execution Loop

If variational:

• Bind params → measure expectation → update params
• Run loop inside a session for performance

12. Visualizing Quantum Results

• Histogram of outcomes
• Bloch sphere (for small circuits)
• Learning curves (e.g., loss vs epoch)

13. Developing a Web Interface (Optional)

• Use React or Vue
• Flask/FastAPI backend
• Show progress, job status, and result plots

14. Testing and Validating Your Application

• Use simulators for baseline testing
• Compare real hardware and noisy backend outputs
• Validate quantum outputs against classical approximations

15. Performance Benchmarking

• Track time per epoch
• Measure depth, gate count
• Record fidelity or classification accuracy

16. Deploying on the Cloud or Web

• Use Docker to containerize
• Deploy frontend/backend on Render, Heroku, or Vercel
• For quantum backends, use secure token handling

17. Writing Technical Documentation

Include:

• Project overview
• Circuit structure
• Backend configurations
• Known limitations

18. Recording a Demo and Final Report

Create a screencast and PDF that:

• Explain architecture
• Demonstrate quantum runs
• Highlight results and insights

19. Peer Review and Feedback

Share with a mentor or community

• Request feedback
• Respond to pull request reviews
• Iterate on bugs and usability

20. Conclusion and Future Directions

Your capstone can become:

• A scientific paper
• A startup prototype
• A conference presentation
  Continue evolving it with more data, circuits, or cloud deployments.