47 citations · 96 across the 11 of their papers we have counts for
5 papers · 1 filter
Parameter Setting Heuristics Make the Quantum Approximate Optimization Algorithm Suitable for the Early Fault-Tolerant Era
Zichang He, Ruslan Shaydulin, Dylan Herman +4
Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising quantum heuristics for combinatorial optimization. While QAOA has been shown to perform well on small…
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest
Romina Yalovetzky, Niraj Kumar, Changhao Li +1
Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data…
Prospects of Privacy Advantage in Quantum Machine Learning
Jamie Heredge, Niraj Kumar, Dylan Herman +5
Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collab…
Quantum counterdiabatic driving enhanced by two-stage local control
Changhao Li, Jiayu Shen, Ruslan Shaydulin +1
Counterdiabatic driving can suppress diabatic losses during adiabatic ground state preparation. As its implementation necessitates the generation of an adiabatic gauge potential, w…
Q-CHOP: Quantum constrained Hamiltonian optimization
Michael A. Perlin, Ruslan Shaydulin, Benjamin P. Hall +7
Combinatorial optimization problems that arise in science and industry typically have constraints. Yet the presence of constraints makes them challenging to tackle using both class…