Fig. 3: Results of a binary classification case study on IBM quantum processor of “ibmq_essex” backend.
From: A co-design framework of neural networks and quantum circuits towards quantum advantage
a Binary classification with two inputs “x” and “y”; b QF-Nets with trained parameters; c QF-Circ derived from the trained QF-Nets where input is encoded by f(x, y); d the virtual-to-physic mapping obtained by QF-Map upon “ibmq_essex” quantum processor; e QF-FB(C) achieves 100% accuracy; f QF-F(Q) achieves 98% accuracy where 2 marked error cases having probability deviation within 0.6%; g results on “ibmq_essex” using the default mapping, achieving 68% accuracy; h results obtained by “ibmq_essex” with the mapping in (d), achieving 82% accuracy; shots number in all tests is set as 8192.
