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Found 29 papers, 9 papers with code
Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution.
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT).
Challenges for Reinforcement Learning in Quantum Circuit Design
Quantum computing (QC) in the current NISQ era is still limited in size and precision.
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).
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms.
Constructing Organism Networks from Collaborative Self-Replicators
We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights.
Empirical Analysis of Limits for Memory Distance in Recurrent Neural Networks
Common to all different kinds of recurrent neural networks (RNNs) is the intention to model relations between data points through time.
Capturing Dependencies within Machine Learning via a Formal Process Model
We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way.
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.
Case-Based Inverse Reinforcement Learning Using Temporal Coherence
As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning.
VAST: Value Function Factorization with Variable Agent Sub-Teams
We evaluate VAST in three multi-agent domains and show that VAST can significantly outperform state-of-the-art VFF, when the number of agents is sufficiently large.
Solving Large Steiner Tree Problems in Graphs for Cost-Efficient Fiber-To-The-Home Network Expansion
The expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures.
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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).
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
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
We further present a first viable solution for calculating a dynamic classification threshold, based on the uncertainty distribution of the training data.
Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions
In this work we propose a system architecture for the integration of quantum accelerators.
Benchmarking Surrogate-Assisted Genetic Recommender Systems
The surrogate is used to recommend new items to the user, which are then evaluated according to the user's liking and subsequently removed from the search 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.
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning
In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
Although prior work has shown that dropout-based variational inference techniques and bootstrap-based approaches can be used to model epistemic uncertainty, the suitability for detecting OOD samples in deep reinforcement learning remains an open question.
Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms
As automatic optimization techniques find their way into industrial applications, the behavior of many complex systems is determined by some form of planner picking the right actions to optimize a given objective function.
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum.
Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms
The evolutionary edit distance between two individuals in a population, i. e., the amount of applications of any genetic operator it would take the evolutionary process to generate one individual starting from the other, seems like a promising estimate for the diversity between said individuals.
Bayesian Verification under Model Uncertainty
We propose such a definition of subjective satisfaction based on recently introduced satisfaction functions.
Stacked Thompson Bandits
We introduce Stacked Thompson Bandits (STB) for efficiently generating plans that are likely to satisfy a given bounded temporal logic requirement.
QoS-Aware Multi-Armed Bandits
Motivated by runtime verification of QoS requirements in self-adaptive and self-organizing systems that are able to reconfigure their structure and behavior in response to runtime data, we propose a QoS-aware variant of Thompson sampling for multi-armed bandits.
