YQuantum

A quantum computing research and development project focused on implementing and analyzing quantum algorithms using Qiskit.

Project Overview

YQuantum is a comprehensive quantum computing project that includes:

• Classical implementations of quantum algorithms
• Quantum algorithm implementations using Qiskit
• Experimental notebooks and documentation
• Research tools and utilities
• Team collaboration resources

Project Structure

• algorithms/: Contains implementations of quantum algorithms
• Classical Implementation/: Classical computing approaches to quantum problems
• docs/: Project documentation and research materials
• experiments/: Experimental code and results
  • rigetti.py: Portfolio optimization implementation using Rigetti's PyQuil framework
  • testing_vqe.py: Portfolio optimization using Qiskit with VQE and QAOA implementations
  • testing_vqe.ipynb: Interactive Jupyter notebooks for VQE testing and visualization
  • final.ipynb: Comprehensive experimental notebook with analysis and results
  • experiment_log.md: Documentation of experimental procedures and findings
• notebooks/: Jupyter notebooks for analysis and visualization
• team/: Team-related resources and documentation
• tools/: Utility scripts and helper functions

Experiments Overview

The experiments directory contains implementations and analysis of quantum portfolio optimization algorithms using different quantum computing frameworks:

1. Rigetti Implementation (rigetti.py):

   • Implements portfolio optimization with 4 assets
   • Uses VQE with an efficient SU2 ansatz
   • Features adaptive penalty method for constraint handling
   • Demonstrates quantum circuit construction and optimization

2. Qiskit Implementation (testing_vqe.py):

   • Implements portfolio optimization using multiple quantum algorithms
   • Compares VQE, QAOA, and classical NumPy solutions
   • Uses Qiskit Finance for problem formulation
   • Includes comprehensive result analysis and visualization

3. Interactive Analysis:

   • Jupyter notebooks for interactive experimentation
   • Visualization of quantum circuit performance
   • Step-by-step analysis of optimization results
   • Documentation of experimental procedures and findings

Using the Experiments

To explore the portfolio optimization experiments:

1. Interactive Analysis:

   • Start with final.ipynb for an interactive walkthrough
   • Use final.ipynb for comprehensive analysis and results
   • The notebooks include visualizations and step-by-step explanations

2. Running the Implementations:

   • For Qiskit implementation: python experiments/final.py
   • For Rigetti implementation: python experiments/rigetti.py

VQE Portfolio Optimization Explained

The experiments implement portfolio optimization using the Variational Quantum Eigensolver (VQE) algorithm. Here's how it works:

1. Problem Formulation:

   • The portfolio optimization problem is converted into a quadratic unconstrained binary optimization (QUBO) problem
   • The objective is to maximize returns while minimizing risk, subject to budget constraints
   • The problem is mapped to a quantum Hamiltonian using Pauli operators

2. VQE Implementation:

   • Uses a parameterized quantum circuit (ansatz) to prepare quantum states
   • The circuit consists of rotation gates (RY, RZ) and entangling gates (CNOT)
   • Parameters are optimized classically to minimize the expectation value of the Hamiltonian

3. Key Components:

   • Cost Hamiltonian: Combines return maximization and risk minimization terms
   • Ansatz Circuit: Efficient SU2 ansatz with alternating rotation and entanglement layers
   • Constraint Handling: Uses penalty terms to enforce budget constraints
   • Classical Optimizer: COBYLA optimizer to find optimal circuit parameters

4. Results Analysis:

   • Compares quantum solutions with classical approaches
   • Analyzes convergence behavior and solution quality
   • Evaluates the impact of different ansatz structures and optimization parameters

For detailed implementation and analysis, refer to the Jupyter notebooks in the experiments directory.

Dependencies

The project uses the following main dependencies:

• Qiskit (0.44.1)
• Qiskit Aer (0.12.2)
• Qiskit Finance (0.3.4)
• Qiskit Algorithms (0.2.1)
• Qiskit Optimization (0.5.0)
• Matplotlib (≥3.7.0)
• NumPy (≥1.22.0)
• Qiskit IBM Runtime
• SciencePlots

Setup

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

Contributing

Please refer to the documentation in the docs/ directory for contribution guidelines and project standards.

License

This project is licensed under the terms specified in the LICENSE file.