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20242026
most citedParameter Setting Heuristics Make the Quantum Approximate Optimization Algorithm Suitable for the Early Fault-Tolerant Era

7 citations · 13 across the 5 of their papers we have counts for

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5 papers

quant-ph2026

Quantum-Informed Portfolio Selection: An End-to-End Pipeline Validated on Trapped-Ion Hardware with Real Market Data

Romina Yalovetzky, Martin J. A. Schuetz, Zichang He +11

Portfolio diversification - a cornerstone of modern investment management - can be formulated as a Maximum Independent Set (MIS) problem on asset correlation graphs. Solving this p…

quant-ph2025

qReduMIS: A Quantum-Informed Reduction Algorithm for the Maximum Independent Set Problem

Martin J. A. Schuetz, Romina Yalovetzky, Ruben S. Andrist +8

We propose and implement a quantum-informed reduction algorithm for the maximum independent set problem that integrates classical kernelization techniques with information extracte…

quant-ph2025

Quantum Compilation Toolkit for Rydberg Atom Arrays with Implications for Problem Hardness and Quantum Speedups

Martin J. A. Schuetz, Ruben S. Andrist, Grant Salton +7

We propose and implement a comprehensive quantum compilation toolkit for solving the maximum independent set (MIS) problem on quantum hardware based on Rydberg atom arrays. Our end…

math.OC2024★ 6 cited

Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing

Atithi Acharya, Romina Yalovetzky, Pierre Minssen +10

Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work…

quant-ph2024★ 7 cited

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…