QevosAgent in Action: Automated Quantum Simulation and Visualization

Quantum computing is hailed as the next paradigm of computation, but its learning curve is steep: you need to understand quantum mechanics basics, master specialized programming frameworks (like Qiskit), write code, run simulations, analyze results, and visualize them. For most developers, this is a long and arduous journey.

This article demonstrates how QevosAgent automates these complex steps — you simply describe your goal, and the Agent handles everything from code generation to simulation execution and result visualization.

Case Study 1: Grover's Search Algorithm

Task Objective

Search for a specific target item among 16 unsorted items. A classical algorithm requires an average of 8.5 queries ((N+1)/2), while Grover's quantum search algorithm achieves speedup with only about 3 iterations (π√N/4).

Agent Execution Process

QevosAgent automatically completed the following steps:

1. Environment Setup: Installed Qiskit 2.4.1 and Qiskit Aer simulator
2. Code Generation: Wrote a complete Grover algorithm implementation, including:
   • Uniform superposition initialization for 4 qubits
   • Oracle (marking operator) based on target state |1011⟩
   • Grover diffusion operator (amplitude amplification)
   • 2 iterations (≈ π/4 × √16)
3. Simulation: Ran 1024 measurements using AerSimulator
4. Visualization: Automatically generated quantum circuit diagram, probability distribution chart, and classical vs. quantum comparison chart

Key Results

Complete Grover search circuit: 4 qubits, 2 iterations

Measurement results: target state |1011⟩ found with 91.2% probability

Query count comparison between classical and quantum algorithms, showing ~2.7x speedup

Case Study 2: Quantum Fourier Transform (QFT)

Task Objective

Demonstrate the core principle of the Quantum Fourier Transform: transforming quantum states from the computational basis to the frequency domain, and visualizing the dynamic evolution of quantum states on the Bloch sphere.

Agent Execution Process

The QFT demonstration involved more technical challenges, showcasing the Agent's excellent adaptability:

1. API Adaptation: In Qiskit 2.4.1, the QFT class was deprecated. The Agent automatically switched to synth_qft_full to create the QFT subcircuit
2. Statevector Calculation: Used Statevector to precisely compute QFT outputs, verifying uniform distribution after |000⟩ input (maximum error only 5.55×10⁻¹⁷)
3. Multi-Input Comparison: Executed QFT on four input states |000⟩, |001⟩, |100⟩, |111⟩, generating probability distribution comparison charts
4. Bloch Sphere Animation:
   • Computed Bloch sphere data for single-qubit RY gate evolution (60 steps) using Qiskit
   • Created an HTML page with Canvas-based dynamic Bloch sphere animation
   • Showed the continuous evolution of quantum states from |0⟩ to superposition

Technical Highlights

• QFT Complexity Advantage: QFT requires only O(n²) quantum gates (n = number of qubits), while classical FFT needs O(N log N) (N = 2ⁿ = state space size)
• Bloch Sphere Dynamic Visualization: The Agent discovered that Qiskit's Statevector lacks a built-in bloch_vector method, so it manually computed Bloch coordinates (x, y, z) from the density matrix for precise 3D visualization
• Headless Environment Compatibility: Automatically configured matplotlib's Agg backend to ensure high-quality PNG generation on servers without a graphical interface

3-qubit QFT quantum circuit generated with Qiskit native API

Probability distribution after QFT on |000⟩ input, verifying uniform superposition

QFT results comparison for four different input states (|000⟩, |001⟩, |100⟩, |111⟩)

Complexity comparison: QFT O(n²) vs classical FFT O(N log N), demonstrating quantum advantage

Technical Insights: QevosAgent's Automation Capabilities

These two cases demonstrate QevosAgent's core capabilities in scientific computing:

1. Code Generation from Scratch

The Agent doesn't need any pre-written templates. Based solely on natural language descriptions, it generates complete quantum computing code, including environment configuration, algorithm implementation, simulation execution, and result analysis.

2. Adaptive Error Handling

When encountering Qiskit API changes (such as QFT class deprecation, plot_circuit removal), the Agent automatically finds alternatives and adjusts the code, rather than simply failing with an error.

3. Multi-Modal Output

The Agent doesn't just produce numerical results — it automatically creates:

• Static Charts: Quantum circuit diagrams, probability distributions, algorithm comparison charts (PNG format)
• Interactive Pages: HTML demonstration pages with Bloch sphere animations
• Structured Data: JSON files of Bloch sphere evolution data for further analysis

4. Complete Visualization Pipeline

From graphical representation of quantum circuits, to probability distributions of measurement results, to 3D Bloch sphere animations of quantum states, the Agent builds a complete visualization chain from abstract to intuitive.

Conclusion

The combination of quantum computing and AI Agents is lowering the barrier to quantum algorithms. QevosAgent lets developers focus on "what to do" (algorithm objectives), while leaving "how to do it" (code implementation, simulation execution, result visualization) to the Agent.

Whether for teaching demonstrations, algorithm verification, or research exploration, QevosAgent can reduce quantum simulation workflows from hours to minutes, making quantum computing truly accessible to everyone.

This article is based on actual QevosAgent execution records. All simulation results come from real executions.
Tech Stack: Qiskit 2.4.1, Qiskit Aer, Python, Matplotlib, HTML5 Canvas