178 citations · 299 across the 7 of their papers we have counts for
20 papers · 1 filter
Limitations of measure-first protocols in quantum machine learning
Casper Gyurik, Riccardo Molteni, Vedran Dunjko
In recent works, much progress has been made with regards to so-called randomized measurement strategies, which include the famous methods of classical shadows and shadow tomograph…
Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm
Charles Moussa, Hao Wang, Thomas Bäck +1
As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of para…
Reinforcement learning for optimization of variational quantum circuit architectures
Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk +2
The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices. However, th…
Experimental quantum speed-up in reinforcement learning agents
Valeria Saggio, Beate E. Asenbeck, Arne Hamann +10
Increasing demand for algorithms that can learn quickly and efficiently has led to a surge of development within the field of artificial intelligence (AI). An important paradigm wi…
On solving classes of positive-definite quantum linear systems with quadratically improved runtime in the condition number
Davide Orsucci, Vedran Dunjko
Quantum algorithms for solving the Quantum Linear System (QLS) problem are among the most investigated quantum algorithms of recent times, with potential applications including the…
Tabu-driven Quantum Neighborhood Samplers
Charles Moussa, Hao Wang, Henri Calandra +2
Combinatorial optimization is an important application targeted by quantum computing. However, near-term hardware constraints make quantum algorithms unlikely to be competitive whe…