Chenfeng Cao

TL;DR: In this article, a noise-assisted quantum-autoencoder algorithm was proposed to achieve high recovering fidelity for general input states, where appropriate noise channels were used to make the input mixedness and output mixedness consistent.

TL;DR: This work designs a (noise-assisted) adiabatic model of quantum autoencoder that can be implemented on quantum annealers, and provides an information-theoretic solution to its recovering fidelity.

TL;DR: This work builds a noise model to capture the noise in a real quantum computer, and shows that the ground state energy will deviate from the exact value as the noise probability increase and normally noise will accumulate as the circuit depth increase.

TL;DR: In this article, a modified method based on transfer learning was proposed to solve the inverse problem for low-lying eigenstates, where neural networks turn out to be efficient and scalable even with a shallow network and a small data set.

TL;DR: This work simulates random quantum circuits in 1D with Matrix Product Density Operators (MPDO), for different noise models such as dephasing, depolarizing, and amplitude damping, and proposes a more effective tensor updates scheme with optimal truncations for both the inner and the bond dimensions, performed after each layer of the circuit.