From: A case study of variational quantum algorithms for a job shop scheduling problem
 | N | VQE | QAOA | VarQITE | F-VQE |
---|---|---|---|---|---|
Ansatz | 5 >5 | Fig. 2 (p = 2) – | Eq. (7) (p = 2) – | Fig. 2 (p = 2) – | Fig. 2 (p = 2) Fig. 2 (p = 1) |
Initial param. | Â | \({|+\rangle}^{\otimes N}\) | uniform in [0,Ď€] | \({|+\rangle}^{\otimes N}\) | \({|+\rangle}^{\otimes N}\) |
Objective |  | CVaR Eq. (6) (α = 0.5) | CVaR Eq. (8) (α = 0.5) | Mean energy Eq. (9) | Custom Eq. (13) |
Optimizer |  | COBYLA | COBYLA | Eq. (12) | Eq. (14) |
No. shots | 5 10 12 16 23 | 1000 – – – – | 1000 – – – – | 1000 – – – – | 1000 500 550 650 450 |
Quantum chip | 5 10 12 16 23 | multiple – – – – | multiple – – – – | multiple – – – – | multiple ibmq_toronto ibmq_guadalupe ibmq_manhattan ibmq_manhattan |
Key findings | Â | Flexible ansatz; converges slower than F-VQE | Ansatz fixed by problem topology; poor convergence likely due to noise | Flexible ansatz; strongly varying performance across runs; converges slower than F-VQE | Flexible ansatz; fastest, most consistent convergence |