Search Results for author:
Found 9 papers, 4 papers with code
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML).
SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed.
Black Box Optimization Using QUBO and the Cross Entropy Method
A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box optimization methods.
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines
Reinforcement learning has driven impressive advances in machine learning.
BIG-bench Machine Learning
Multi-agent Reinforcement Learning
+2
A Flexible Pipeline for the Optimization of CSG Trees
CSG trees are an intuitive, yet powerful technique for the representation of geometry using a combination of Boolean set-operations and geometric primitives.
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
We discuss the synergetic connection between quantum computing and artificial intelligence.
Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
Quantum Physics
Soccer Team Vectors
In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space.
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning
We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning.
