# Accelerating variational quantum algorithms with multiple quantum processors

@article{Du2021AcceleratingVQ, title={Accelerating variational quantum algorithms with multiple quantum processors}, author={Yuxuan Du and Yan Qian and Dacheng Tao}, journal={ArXiv}, year={2021}, volume={abs/2106.12819} }

Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages over classical methods. Nevertheless, modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large-volume data. As such, to better exert the superiority of VQAs, it is of great significance to improve their runtime efficiency. Here we devise an efficient distributed optimization…

## 3 Citations

### CAFQA: A classical simulation bootstrap for variational quantum algorithms

- Computer Science
- 2022

This work proposes CAFQA, a Clifford Ansatz For Quantum Accuracy, a hardware-efficient circuit built with only Clifford gates that is well-suited to classical computation and allows for preliminary ground state energy estimation of the challenging chromium dimer (Cr 2 ) molecule.

### QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines

- Computer Science
- 2022

It is proved that the merging process in MaxCut can be further cast into a new MaxCut problem and thus be addressed by QAOAs or other MaxCut solvers, and it is proven that the approximation ratio of QAOA 2 is lower bounded by 1 / 2.

### Toward Trainability of Deep Quantum Neural Networks

- Computer Science
- 2021

This work provides the first viable solution to the vanishing gradient problem for deep QNNs with theoretical guarantees and proves that for circuits with controlled-layer architectures, the expectation of the gradient norm can be lower bounded by a value that is independent of the qubit number and the circuit depth.

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