Martin J. A. Schuetz

Amazon (United States) — United States

Computer Science · Physical Sciences

21h-index1.8kcitations59works5.72yr avg
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Research Topics

Quantum Computing Algorithms and Architecture(19), Quantum Information and Cryptography(17), Quantum and electron transport phenomena(11), Cold Atom Physics and Bose-Einstein Condensates(8), Mechanical and Optical Resonators(7)

Publications59 total

A Random-Key optimizer for combinatorial optimization
Journal of Heuristics·2025· 1 cited
Quantum-Guided Cluster Algorithms for Combinatorial Optimization
2025
Decomposition pipeline for large-scale portfolio optimization with applications to near-term quantum computing
Physical Review Research·2025· 3 citedOpen Access
Optimization of Next-Day Delivery Coverage using Constraint Programming and Random Key Optimizers
arXiv (Cornell University)·2025Open Access
Modern Computational Methods in Reinsurance Optimization: From Simulated Annealing to Quantum Branch & Bound
arXiv (Cornell University)·2025Open Access
BoolXAI: Explainable AI Using Expressive Boolean Formulas
Proceedings of the AAAI Conference on Artificial Intelligence·2025· 2 citedOpen Access
Quantum Compilation Toolkit for Rydberg Atom Arrays with Implications for Problem Hardness and Quantum Speedups
arXiv (Cornell University)·2024Open Access
Scalable iterative pruning of large language and vision models using block coordinate descent
arXiv (Cornell University)·2024Open Access
Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing
arXiv (Cornell University)·2024Open Access
Quantum-Informed Recursive Optimization Algorithms
PRX Quantum·2024· 33 citedOpen Access
Designing quantum annealing schedules using Bayesian optimization
Physical Review Research·2024· 24 citedOpen Access
Hardness of the maximum-independent-set problem on unit-disk graphs and prospects for quantum speedups
Physical Review Research·2023· 25 citedOpen Access
Explainable Artificial Intelligence Using Expressive Boolean Formulas
Machine Learning and Knowledge Extraction·2023· 15 citedOpen Access
End-To-End Resource Analysis for Quantum Interior-Point Methods and Portfolio Optimization
PRX Quantum·2023· 14 citedOpen Access
Quantum-Informed Recursive Optimization Algorithms
arXiv (Cornell University)·2023· 1 citedOpen Access
Hardness of the Maximum Independent Set Problem on Unit-Disk Graphs and Prospects for Quantum Speedups
arXiv (Cornell University)·2023· 2 citedOpen Access
Explainable AI using expressive Boolean formulas
arXiv (Cornell University)·2023Open Access
Designing Quantum Annealing Schedules using Bayesian Optimization
arXiv (Cornell University)·2023· 2 citedOpen Access
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems
Nature Machine Intelligence·2022Open Access
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set
Nature Machine Intelligence·2022· 2 citedOpen Access
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Frequent Co-authors

Helmut G. Katzgraber(20), G. Giedke(13), J. I. Cirac(13), J. Kyle Brubaker(13), Mikhail D. Lukin(9), Lieven M. K. Vandersypen(8), Grant Salton(7), Johannes Knörzer(4), E. M. Kessler(4), Jernej Rudi Finžgar(4), Zhihuai Zhu(4), M. D. Lukin(3), Jan Gieseler(3), Maurício G. C. Resende(3), Ruben S. Andrist(3), Pierre Minssen(3), Romina Yalovetzky(3), Shouvanik Chakrabarti(3), Dylan Herman(3), Ruslan Shaydulin(3)