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# Simple Quantum-Inspired Optimization A.md

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+# Simple Quantum-Inspired Optimization API
+
+This is a simple API that demonstrates quantum-inspired optimization using the Quantum Approximate Optimization Algorithm (QAOA) to solve the MaxCut problem. It uses Qiskit for quantum circuit simulation and FastAPI for creating the API.
+
+## Getting Started
+
+1.  Clone the repository: `git clone https://github.com/kyoo-log/QIOS.git` (replace with your repo URL)
+2.  Create a virtual environment (recommended): `python -m venv .venv`
+3.  Activate the virtual environment:
+    *   Windows: `.venv\Scripts\activate`
+    *   macOS/Linux: `source .venv/bin/activate`
+4.  Install dependencies: `pip install -r requirements.txt`
+5.  Run the API: `uvicorn main:app --reload`
+
+## Usage
+
+Send a POST request to `/max_cut` with a JSON payload containing the adjacency matrix of the graph and the number of QAOA layers (`p`).
+
+Example Request:
+
+```json
+{
+  "graph": [[0, 1, 1], [1, 0, 1], [1, 1, 0]],
+  "p": 2
+}

Example Response.json

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+{
+    "optimal_bitstring": "100",
+    "optimal_value": 2.0
+  }

fastapi.md

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+fastapi
+uvicorn[standard]
+pydantic
+qiskit
+numpy

main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import numpy as np
+from qiskit import Aer
+from qiskit.algorithms import QAOA
+from qiskit.optimization import QuadraticProgram
+from qiskit.optimization.converters import QuadraticProgramToQubo
+
+app = FastAPI(title="Simple Quantum-Inspired Optimization API")
+
+class MaxCutRequest(BaseModel):
+    graph: list[list[int]]
+    p: int = 1
+
+@app.post("/max_cut")
+async def max_cut(request: MaxCutRequest):
+    try:
+        graph = np.array(request.graph)
+        num_nodes = len(graph)
+
+        qp = QuadraticProgram()
+        for i in range(num_nodes):
+            qp.binary_var(f'x_{i}')
+
+        for i in range(num_nodes):
+            for j in range(i + 1, num_nodes):
+                if graph[i, j] == 1:
+                    qp.objective.linear[f'x_{i}'] += 1
+                    qp.objective.linear[f'x_{j}'] += 1
+                    qp.objective.quadratic[f'x_{i}', f'x_{j}'] -= 2
+
+        qubo = QuadraticProgramToQubo().convert(qp)
+
+        backend = Aer.get_backend('qasm_simulator')
+        qaoa = QAOA(quantum_instance=backend, p=request.p)
+        result = qaoa.compute_minimum_eigenvalue(qubo)
+
+        optimal_bitstring = "".join(map(str, map(int, result.optimal_bitstring)))
+        optimal_value = result.optimal_value
+
+        return {"optimal_bitstring": optimal_bitstring, "optimal_value": optimal_value}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))