Qiskit implementation of a Portfolio Optimization.
- Python 3.8+
- Qiskit
- CVXPY
- MOSEK
- tqdm
pip install -r requirements.txtThe VQE Solver takes a Covariance matrix and changes it to an Ising problem. The Ising model's hamiltonian is then minimized with the VQE.
from src.solver.vqe.vqe_solver import VQESolver from qiskit import BasicAer from qiskit.utils import QuantumInstance from qiskit.circuit.library import TwoLocal from qiskit.algorithms.optimizers import SLSQP vqe = VQESolver() vqe.qp(Cov = Cov) vqe.to_ising(Nq = Nq) # Prepare QuantumInstance qi = QuantumInstance(BasicAer.get_backend('statevector_simulator'), seed_transpiler=seed, seed_simulator=seed) # Select the VQE parameters N = Cov.shape[0] ansatz = TwoLocal(num_qubits=N*Nq, rotation_blocks=['ry','rz'], entanglement_blocks='cz', reps=args.reps, entanglement='full') slsqp = SLSQP(maxiter=args.maxiter) vqe.vqe_instance(ansatz=ansatz, optimizer=slsqp, quantum_instance=qi) res_vqe = vqe.solve() print(res_vqe)