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quant-ph2026

Operational criteria for quantum advantage in latency-constrained nonlocal games

Changhao Li, Seigo Kikura, Akihisa Goban +2

Remote entanglement enables coordinated decision making without communication and produces correlations beyond those achievable by any classical strategy, representing a practical…

quant-ph2026

Metriq: A Collaborative Platform for Benchmarking Quantum Computers

Alessandro Cosentino, Changhao Li, Vincent Russo +6

The fragmented landscape of quantum computer benchmarks, characterized by system-specific tools and inconsistent evaluation methodologies, hinders reliable cross-platform performan…

quant-ph2026

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…

quant-ph2024

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…

quant-ph2024

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…

quant-ph2024

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…