Table 2 Comparison of performance of QGAN and SQGEN for 20 epochs of learning with BFGS optimizer for varied size of generated n-qubit GHZ state.

From: Synergic quantum generative machine learning

n

Feature

SQGEN

QGAN

Discriminator

Generator

1

Experiments per epoch

32.68

19.3

130.73

Circuit depth

27

19

5

Average time per epoch

0.93

1.52

Range of time per epoch

0.88–1.06

1.33–1.77

2

Experiments per epoch

61.01

46.8

124.86

Circuit depth

92

71

18

Average time per epoch

10.87

9.97

Range of time per epoch

10.08–11.87

8.65–11.92

3

Experiments per epoch

92

70.72

147.25

Circuit depth

317

239

55

Average time per epoch

52.87

39.80

Range of time per epoch

51.14–55.34

34.73–44.90

4

Experiments per epoch

136.42

102

229.19

Circuit depth

976

747

174

Average time per epoch

229.44

174.34

Range of time per epoch

62.03–470.08

135.01–288.03

5

Experiments per epoch

127.69

156.66

172.83

Circuit depth

2404

1851

434

Average time per epoch

516.45

515.85

Range of time per epoch

172.25–628.61

300.55–925.95

6

Experiments per epoch

176.79

137.5

158.44

Circuit depth

5385

4159

979

Average time per epoch

1558.58

999.93

Range of time per epoch

443.82–2621.82

718.19–1148.19

  1. The total run time is given in seconds, and it may vary depending both on software and hardware. The run times here were obtained as averages over 5 runs (for various initial configurations) on a workstation equipped with Intel(R) Xeon(R) CPU X5690 @ 3.47GHz, using Python-based programs utilizing, e.g., qiskit, numpy, and scipy modules. The tabulated data corresponds to Fig. 6.