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20242026
most citedExponentially Reduced Circuit Depths Using Trotter Error Mitigation

2 citations · 2 across the 5 of their papers we have counts for

collaborators

8 papers

quant-ph2026

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…

quant-ph2026

Cycle Codes and Decoded Quantum Interferometry

Anuj Apte, Shouvanik Chakrabarti, Andi Gu +6

Decoded Quantum Interferometry (DQI) reduces optimization problems with two-variable constraints to decoding cycle codes. For one such problem, namely MaxCut, prior work showed tha…

quant-ph2026

Proper Learning of Shallow All-to-All Quantum Circuits

Steven Kordonowy, Jacob Watkins

This work considers a variation on the problem of learning shallow quantum circuits. Given query access to the circuit, as well as knowledge of its gate layout, we consider the tas…

quant-ph2026

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…

quant-ph2026

Digital signatures with classical shadows on near-term quantum computers

Pradeep Niroula, Minzhao Liu, Sivaprasad Omanakuttan +15

Quantum mechanics provides cryptographic primitives whose security is grounded in hardness assumptions independent of those underlying classical cryptography. However, existing pro…

quant-ph2025

On Speedups for Convex Optimization via Quantum Dynamics

Shouvanik Chakrabarti, Dylan Herman, Jacob Watkins +4

We explore the potential for quantum speedups in convex optimization using discrete simulations of the Quantum Hamiltonian Descent (QHD) framework, as proposed by Leng et al., and…

quant-ph2025

Fast Convex Optimization with Quantum Gradient Methods

Brandon Augustino, Dylan Herman, Enrico Fontana +4

We study quantum algorithms based on quantum (sub)gradient estimation using noisy function evaluation oracles, and demonstrate the first dimension-independent query complexities (u…

quant-ph2024★ 2 cited

Exponentially Reduced Circuit Depths Using Trotter Error Mitigation

James D. Watson, Jacob Watkins

Product formulae are a popular class of digital quantum simulation algorithms due to their conceptual simplicity, low overhead, and performance which often exceeds theoretical expe…