Reinforcement learning with a 3D convolutional network designs low-CNOT circuits for variational thermal state preparation of the SYK model up to 14 Majorana fermions, but training uses the exact free energy and fidelity as reward.
Real-time error mitigation for variational optimization on quantum hardware
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abstract
In this work we put forward the inclusion of error mitigation routines in the process of training Variational Quantum Circuit (VQC) models. In detail, we define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. While state-of-the-art QEM methods cannot address the exponential loss concentration induced by noise in current devices, we demonstrate that our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function. We tested the algorithm by simulating and deploying the fit of a monodimensional $\textit{u}$-Quark Parton Distribution Function (PDF) on a superconducting single-qubit device, and we further analyzed the scalability of the proposed technique by simulating a multidimensional fit with up to 8 qubits.
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Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware
Reinforcement learning with a 3D convolutional network designs low-CNOT circuits for variational thermal state preparation of the SYK model up to 14 Majorana fermions, but training uses the exact free energy and fidelity as reward.