QAOA Fire Opal

QAOA_FO is Q-CTRL's implementation of the Quantum Approximate Optimization Algorithm (QAOA) through their Fire Opal framework. It is a hybrid quantum-classical algorithm for solving combinatorial optimization problems with enhanced performance through Q-CTRL's error mitigation and control techniques.

The algorithm works by preparing a quantum state through alternating applications of problem-specific (cost) and mixing Hamiltonians, controlled by variational parameters that are optimized classically to maximize the probability of measuring the optimal solution.

QAOA_FO leverages Q-CTRL's expertise in quantum control to improve circuit fidelity and optimization performance. It is particularly suited for problems that can be encoded as quadratic unconstrained binary optimization (QUBO) or Ising models, such as MaxCut, TSP, and portfolio optimization.

Compatible Backends

Backend	Default
Qctrl

Initialization

Python

from luna_quantum.solve.parameters.algorithms.quantum_gate.qaoa_fo import QAOA_FO

algorithm = QAOA_FO(
    backend=None
)

Usage

from luna_quantum.algorithms import QAOA_FO

algorithm = QAOA_FO()
solve_job = algorithm.run(model, name="my-solve-job")