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Found 16 papers, 4 papers with code
KetGPT -- Dataset Augmentation of Quantum Circuits using Transformers
Quantum algorithms, represented as quantum circuits, can be used as benchmarks for assessing the performance of quantum systems.
Challenges for Reinforcement Learning in Quantum Circuit Design
Quantum computing (QC) in the current NISQ era is still limited in size and precision.
qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation
The goal of qgym is to connect the research fields of Artificial Intelligence (AI) with quantum compilation by abstracting parts of the process that are irrelevant to either domain.
NISQ-ready community detection based on separation-node identification
The analysis of network structure is essential to many scientific areas, ranging from biology to sociology.
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.
Predictive Collision Management for Time and Risk Dependent Path Planning
The task of collision avoidance can be divided into a global and a local level.
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning
Extracting a Construction Tree from potentially noisy point clouds is an important aspect of Reverse Engineering tasks in Computer Aided Design.
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.
Insights on Training Neural Networks for QUBO Tasks
Current hardware limitations restrict the potential when solving quadratic unconstrained binary optimization (QUBO) problems via the quantum approximate optimization algorithm (QAOA) or quantum annealing (QA).
Trajectory annotation using sequences of spatial perception
In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities.
Bayesian Surprise in Indoor Environments
Agents of LBS, such as mobile robots or non-player characters in computer games, may use the context surprise to focus more on important regions of a map for a better use or understanding of the floor plan.
Optimizing Geometry Compression using Quantum Annealing
The compression of geometry data is an important aspect of bandwidth-efficient data transfer for distributed 3d computer vision applications.
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
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies
We experimentally evaluate STEP in two challenging and stochastic domains with large state and joint action spaces and show that STEP is able to learn stronger policies than standard multi-agent reinforcement learning algorithms, when combining multi-agent open-loop planning with centralized function approximation.
Segmented and Directional Impact Detection for Parked Vehicles using Mobile Devices
The system is capable of detecting the impact segment and the point of time of an impact event on a vehicle's surface, as well as its direction of origin.
