Fig. 11: The average error ϵ for 10,000 test samples is shown as a function of the number of training points for 100-qubit Hamiltonian \({{\mathcal{H}}}_{{\rm{XY\; Z}}}\). | npj Quantum Information

Fig. 11: The average error ϵ for 10,000 test samples is shown as a function of the number of training points for 100-qubit Hamiltonian \({{\mathcal{H}}}_{{\rm{XY\; Z}}}\).

From: Direct entanglement detection of quantum systems using machine learning

Fig. 11

ϵ is defined as \(\epsilon =\frac{1}{M}\mathop{\sum }\nolimits_{m = 1}^{M}| {S}_{m}^{{\rm{th}}}({\rho }_{A})-{S}_{m}^{{\rm{ML}}}({\rho }_{A})|\). ρA = [n] denote the subsystems of the first n qubits of 100-qubit.

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