70 citations · 271 across the 35 of their papers we have counts for
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Training Variational Quantum Algorithms Is NP-Hard, Even Locally
Dax Enshan Koh, Triscia Mundo, Iosif Sakos +1
Variational quantum algorithms (VQAs) generally rely on classical optimization to train parameterized quantum circuits. This training seeks to minimize an objective function, and i…
Global Optimization for Parametrized Quantum Circuits
Iosif Sakos, Antonios Varvitsiotis, Georgios Korpas +1
In the absence of error correction, noisy intermediate-scale quantum devices are operated by training parametrized quantum circuits (PQCs) so as to minimize a suitable loss functio…
Convex Markov Games and Beyond: New Proof of Existence, Characterization and Learning Algorithms for Nash Equilibria
Anas Barakat, Ioannis Panageas, Antonios Varvitsiotis
Convex Markov Games (cMGs) were recently introduced as a broad class of multi-agent learning problems that generalize Markov games to settings where strategic agents optimize gener…