Inspiration
What it does
Exploring Quantum Computing with Qiskit We built three simulator-based notebooks to explore how quantum algorithms work and how noise affects their results. The project covers E91 quantum key distribution, Grover’s search under depolarizing and bit-flip noise, and a variational quantum eigensolver (VQE) for estimating the energy of a LiH molecule.
We were inspired by how the same quantum principles—superposition, entanglement, interference, and measurement—can be applied to very different problems. Working through the challenges helped us connect the mathematics to Qiskit circuits, measurement statistics, and molecular Hamiltonians.
Each notebook includes an implementation, experiments, and plots. We compared ideal and noisy Grover search, studied correlations and eavesdropping in E91, and explored how ansatz design, optimization, finite shots, and gate noise affect VQE estimates.
One challenge was balancing accuracy with limited shots and circuit resources. The simulations also made clear that results depend on noise and statistical uncertainty, and that a simulator study is not the same as running on quantum hardware. The project is a practical introduction to quantum algorithms and their limitations.
How we built it
We built three Jupyter notebooks using Python, Qiskit, and Qiskit Aer. Each notebook implements and simulates a different quantum computing challenge: E91 quantum key distribution, Grover search under noise, and VQE for estimating the energy of a LiH molecule. We used NumPy and SciPy for calculations, and Matplotlib to visualize experimental results.
Challenges we ran into
Balancing accuracy with limited shots and circuit resources was challenging, especially for VQE. We also had to account for noise and statistical uncertainty, which can make measured results differ from ideal predictions.
Accomplishments that we're proud of
We completed three end-to-end quantum simulation notebooks, including circuit implementations, noise experiments, analysis, and visualizations. We also compared Grover’s performance under different noise models and explored how ansatz choices affect molecular energy estimates.
What we learned
We learned how quantum circuits implement algorithms, how entanglement supports quantum key distribution, and how Grover’s iterations amplify a search result. We also saw how measurement noise, circuit depth, and shot budgets affect practical VQE results.
What's next for Qiskit Quantum Simulating Search, Security & Molecules
Next, we’d like to improve the VQE ansatz and optimization, explore error mitigation, and test the algorithms on quantum hardware to compare real device results with our simulations.
