Search Results for author:
Found 31 papers, 8 papers with code
Quantum machine learning beyond kernel methods
In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models.
Quantum enhancements for deep reinforcement learning in large spaces
In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods.
Operationally meaningful representations of physical systems in neural networks
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems.
Optimal foraging strategies can be learned
Recognizing the interconnected nature of these challenges, this work addresses them simultaneously by exploring optimal foraging strategies through a reinforcement learning framework.
Quantum circuit synthesis with diffusion models
The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device.
Machine learning for long-distance quantum communication
But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication?
Automated Gadget Discovery in Science
This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm.
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias
To overcome this limitation, we introduce Multi-Excitation Projective Simulation (mePS), a generalization that considers a chain-of-thought to be a random walk of several particles on a hypergraph.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Faster quantum mixing for slowly evolving sequences of Markov chains
Markov chain methods are remarkably successful in computational physics, machine learning, and combinatorial optimization.
Benchmarking projective simulation in navigation problems
Projective simulation (PS) is a model for intelligent agents with a deliberation capacity that is based on episodic memory.
Active learning machine learns to create new quantum experiments
We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.
Modelling collective motion based on the principle of agency
Collective motion is an intriguing phenomenon, especially considering that it arises from a set of simple rules governing local interactions between individuals.
Projective simulation with generalization
Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.
Speeding-up the decision making of a learning agent using an ion trap quantum processor
We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions.
Machine learning \& artificial intelligence in the quantum domain
For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world.
Quantum-enhanced machine learning
Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements.
Meta-learning within Projective Simulation
The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.
Quantum machine learning with glow for episodic tasks and decision games
We consider a general class of models, where a reinforcement learning (RL) agent learns from cyclic interactions with an external environment via classical signals.
Framework for learning agents in quantum environments
In this paper we provide a broad framework for describing learning agents in general quantum environments.
Projective simulation for classical learning agents: a comprehensive investigation
We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced [H. J.
Projective simulation applied to the grid-world and the mountain-car problem
We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios.
Optimizing Quantum Error Correction Codes with Reinforcement Learning
Using efficient simulations with about 70 data qubits with arbitrary connectivity, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of data qubits, for various error models of interest.
Photonic architecture for reinforcement learning
The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices.
How a minimal learning agent can infer the existence of unobserved variables in a complex environment
According to a mainstream position in contemporary cognitive science and philosophy, the use of abstract compositional concepts is both a necessary and a sufficient condition for the presence of genuine thought.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes
Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes.
Development of swarm behavior in artificial learning agents that adapt to different foraging environments
Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics.
Collective defense of honeybee colonies: experimental results and theoretical modeling
Social insect colonies routinely face large vertebrate predators, against which they need to mount a collective defense.
Parametrized quantum policies for reinforcement learning
With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction.
Towards interpretable quantum machine learning via single-photon quantum walks
Variational quantum algorithms represent a promising approach to quantum machine learning where classical neural networks are replaced by parametrized quantum circuits.
Variational measurement-based quantum computation for generative modeling
Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms.
Measurement-based quantum computation from Clifford quantum cellular automata
Measurement-based quantum computation (MBQC) is a paradigm for quantum computation where computation is driven by local measurements on a suitably entangled resource state.
