EPJ Quantum Technology

Table 5 Performance results based on the logarithm of the Holevo variance \(\ln (V_{H}(\alpha ,\beta ))\) for the PSO algorithm. Values in bold represent the configurations which have achieved convergence in Table 4. Lower variance values are obtained by solutions that lead to more precise estimations of the unknown parameter that is being measured

From: Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors

α	β
α	0	0.2	0.4	0.6	0.8	1
0	1.3082	2.3717	0.7076	1.5727	1.7540	6.0563
0.2	−1.5475	−1.3651	−1.6623	−1.4530	−0.8383	−1.0281
0.4	−1.4016	−1.5598	−1.5295	−1.6831	−1.6005	1.2126
0.6	−1.6229	−1.5110	−1.9324	−1.3650	−1.6605	−1.5408
0.8	−1.7227	−1.4539	−1.5615	−1.5828	−1.8361	−1.2132
1	−1.6869	−1.3290	−1.3956	−1.6726	−1.5909	−1.4826

Back to article page