181 citations · 237 across the 18 of their papers we have counts for
7 papers · 1 filter
Quantum Simulation on Riemannian Manifolds
Dylan Herman, Jacob Watkins, Guneykan Ozgul +4
We investigate algorithms for the quantum simulation of the Schrödinger equation on a Riemannian manifold, where the kinetic operator is defined by the Laplace--Beltrami operator c…
An analysis of iterative refinement for quantum linear system solvers
Adrian Harkness, Mohammadhossein Mohammadisiahroudi, Brandon Augustino +2
We present and analyze an iterative refinement (IR) framework for improving the precision dependence of algorithms that combine a quantum linear system algorithm (QLSA) with quantu…
Near-Optimal Quantum Lower Bounds for Convex Optimization via Fourier Rank
Brandon Augustino, Shouvanik Chakrabarti, Enrico Fontana +4
We establish a linear quantum query lower bound for high-accuracy convex optimization over an explicit family of -dimensional ellipsoids. We focus on linear optimization with an…
Global Multi-Maturity SPX-VIX Calibration Beyond Markovian Stitching
Atithi Acharya, Yue Sun, Brandon Augustino +3
We develop a global martingale optimal transport framework for joint S&P 500 (SPX)-VIX smile calibration across multiple maturities, without the conditional independence that Marko…
Faster Algorithms for Multimarginal Optimal Transport
Brandon Augustino, Yue Sun, Atithi Acharya +4
We study constructive discrete multimarginal optimal transport (MOT) among distributions on points, where the cost tensor has entries. For additive accuracy $\v…
Regularized Warm-Started Quantum Approximate Optimization and Conditions for Surpassing Classical Solvers on the Max-Cut Problem
Zichang He, Anuj Apte, Brandon Augustino +4
Demonstrating quantum heuristics that outperform strong classical solvers on large-scale optimization remains an open challenge. Here we introduce Regularized Warm-Started QAOA (RW…
Quantum Speedups for Group Relaxations of Integer Linear Programs
Brandon Augustino, Dylan Herman, Guneykan Ozgul +5
Integer Linear Programs (ILPs) are a flexible and ubiquitous model for discrete optimization problems. Solving ILPs is \textsf{NP-Hard} yet of great practical importance. Super-qua…