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Found 5 papers, 1 papers with code
Quantum Multiple Kernel Learning
Our method leverages the power of deterministic quantum computing with one qubit (DQC1) to estimate the combined kernel for a set of classically intractable individual quantum kernels.
A Quantum Annealing-Based Approach to Extreme Clustering
Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements.
Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines
The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
Free energy-based reinforcement learning using a quantum processor
The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
Reinforcement Learning Using Quantum Boltzmann Machines
We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks.
