Y
Yuan Su
Researcher at University of Maryland, College Park
Publications - 48
Citations - 2570
Yuan Su is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Quantum & Quantum computer. The author has an hindex of 18, co-authored 43 publications receiving 1498 citations. Previous affiliations of Yuan Su include Chinese Academy of Sciences & Google.
Papers
More filters
Journal ArticleDOI
Toward the first quantum simulation with quantum speedup.
TL;DR: It is argued that simulating the time evolution of spin systems is a classically hard problem of practical interest that is among the easiest to address with early quantum devices, and develops optimized implementations and performs detailed resource analyses for several leading quantum algorithms for this problem.
Journal ArticleDOI
Theory of Trotter Error with Commutator Scaling
TL;DR: A new theory quantifying product formulas' errors puts these algorithms on a rigorous foundation, showcasing their superiority over other methods.
Proceedings ArticleDOI
Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics
TL;DR: In this article, a quantum singular value transformation (SVTT) algorithm is proposed to transform the singular values of a unitary operator into polynomial transformations, leading to optimal algorithms with appealing constant factors.
Proceedings ArticleDOI
Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics
TL;DR: In this paper, the singular value transformation (SVT) algorithm was proposed for computing the singular values of a block of a unitary, which can apply polynomial transformations to the value of the unitary.
Journal ArticleDOI
Automated optimization of large quantum circuits with continuous parameters
TL;DR: An automated methods for optimizing quantum circuits of the size and type expected in quantum computations that outperform classical computers are developed and implemented and a collection of fast algorithms capable of optimizing large-scale quantum circuits are reported.