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Learning To Optimize Quantum Neural Network Without Gradients
This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer.
Towards Practical Explainability with Cluster Descriptors
The goal is to find a representative set of tags for each cluster, referred to as the cluster descriptors, with the constraint that these descriptors we find are pairwise disjoint, and the total size of all the descriptors is minimized.
Ising-Based Louvain Method: Clustering Large Graphs with Specialized Hardware
Recent advances in specialized hardware for solving optimization problems such quantum computers, quantum annealers, and CMOS annealers give rise to new ways for solving real-word complex problems.
Binary matrix factorization on special purpose hardware
In this work, we focus on the important binary matrix factorization (BMF) problem which has many applications in data mining.
Ising-based Consensus Clustering on Specialized Hardware
The emergence of specialized optimization hardware such as CMOS annealers and adiabatic quantum computers carries the promise of solving hard combinatorial optimization problems more efficiently in hardware.
