44 citations · 106 across the 15 of their papers we have counts for
6 papers · 1 filter
The curse of random quantum data
Kaining Zhang, Junyu Liu, Liu Liu +3
Quantum machine learning, which involves running machine learning algorithms on quantum devices, may be one of the most significant flagship applications for these devices. Unlike…
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits
Kaining Zhang, Liu Liu, Min-Hsiu Hsieh +1
Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poo…
Toward Trainability of Deep Quantum Neural Networks
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Quantum Neural Networks (QNNs) with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase. This re…
Toward Trainability of Quantum Neural Networks
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform clas…
Quantum Gram-Schmidt Processes and Their Application to Efficient State Read-out for Quantum Algorithms
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Many quantum algorithms that claim speed-up over their classical counterparts only generate quantum states as solutions instead of their final classical description. The additional…
Quantum algorithm for finding the negative curvature direction in non-convex optimization
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
We present an efficient quantum algorithm aiming to find the negative curvature direction for escaping the saddle point, which is the critical subroutine for many second-order non-…