2 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting
Filip B. Maciejewski, Stuart Hadfield, Oscar Wallis +5
Progress towards a quantum advantage using known heuristic methods for combinatorial optimization is impeded by hardware noise and limited qubit count. Here, we propose a noise-awa…
Setting angles in quantum approximate optimization at utility-scale
Maosheng Guo, Joel Jurado Diaz, Anurag Ramesh +16
The quantum approximate optimization algorithm (QAOA) is a powerful heuristic that seeks to solve combinatorial optimization problems using quantum hardware and classical optimizat…
Boosting Sparsity in Graph Decompositions with QAOA Sampling
George Pennington, Naeimeh Mohseni, Oscar Wallis +5
We study the problem of decomposing a graph into a weighted sum of a small number of matchings, a task that arises in network resource allocation problems such as peer-to-peer ener…
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
M. Emre Sahin, Edoardo Altamura, Oscar Wallis +6
We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning. The API abstracts Qiskit's primi…