462 citations · 4.5k across the 108 of their papers we have counts for
6 papers · 2 filters
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…
Quantum-accessible reinforcement learning beyond strictly epochal environments
A. Hamann, V. Dunjko, S. Wölk
In recent years, quantum-enhanced machine learning has emerged as a particularly fruitful application of quantum algorithms, covering aspects of supervised, unsupervised and reinfo…
Hybrid divide-and-conquer approach for tree search algorithms
Mathys Rennela, Sebastiaan Brand, Alfons Laarman +1
One of the challenges of quantum computers in the near- and mid- term is the limited number of qubits we can use for computations. Finding methods that achieve useful quantum impro…
Towards quantum advantage via topological data analysis
Casper Gyurik, Chris Cade, Vedran Dunjko
Even after decades of quantum computing development, examples of generally useful quantum algorithms with exponential speedups over classical counterparts are scarce. Recent progre…
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Michael Broughton, Guillaume Verdon, Trevor McCourt +26
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high…
To quantum or not to quantum: towards algorithm selection in near-term quantum optimization
Charles Moussa, Henri Calandra, Vedran Dunjko
The Quantum Approximate Optimization Algorithm (QAOA) constitutes one of the often mentioned candidates expected to yield a quantum boost in the era of near-term quantum computing.…