Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

Recent advances in quantum computers are demonstrating the ability to solve problems at a
scale beyond brute force classical simulation. As such, a widespread interest in quantum …

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 …

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 …

Quantum cooling has demonstrated its potential in quantum computing, which can reduce
the number of control channels needed for external signals. Recent progress also supports …

Recent results suggest that quantum computers possess the potential to speed up
nonconvex optimization problems. However, a crucial factor for the implementation of …

The Portfolio Optimization task has long been studied in the Financial Services literature as
a procedure to identify the basket of assets that satisfy desired conditions on the expected …

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 …

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 …

Optimization is a collection of principles that are used for problem solving in a vast spectrum
of disciplines. Given the specifics of a problem and a set of constraints, the objective is to …