Quantum speedups for stochastic optimization
We consider the problem of minimizing a continuous function given given access to a
natural quantum generalization of a stochastic gradient oracle. We provide two new …
natural quantum generalization of a stochastic gradient oracle. We provide two new …
Quantum lower bounds for finding stationary points of nonconvex functions
Quantum computing is an emerging technology that has been rapidly advancing in the past
decades. In this paper, we conduct a systematic study of quantum lower bounds on finding …
decades. In this paper, we conduct a systematic study of quantum lower bounds on finding …
Quantum Transfer Learning with Adversarial Robustness for Classification of High‐Resolution Image Datasets
The application of quantum machine learning to large‐scale high‐resolution image datasets
is not yet possible due to the limited number of qubits and relatively high level of noise in the …
is not yet possible due to the limited number of qubits and relatively high level of noise in the …
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization
Finite-sum optimization has wide applications in machine learning, covering important
problems such as support vector machines, regression, etc. In this paper, we initiate the …
problems such as support vector machines, regression, etc. In this paper, we initiate the …
Quantum Algorithms for Non-smooth Non-convex Optimization
C Liu, C Guan, J He, J Lui - arXiv preprint arXiv:2410.16189, 2024 - arxiv.org
This paper considers the problem for finding the $(\delta,\epsilon) $-Goldstein stationary
point of Lipschitz continuous objective, which is a rich function class to cover a great number …
point of Lipschitz continuous objective, which is a rich function class to cover a great number …