A Survey on Quantum Reinforcement Learning

Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning (our interpretation of this term will be clarified below), we put particular emphasis on recent developments. With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators in an otherwise classical reinforcement learning setting. In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage. We provide both a birds-eye-view of the field, as well as summaries and reviews for selected parts of the literature.Comment: 62 pages, 16 figure

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Towards Scalable Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning

La computación cuántica tiene un inmenso potencial para resolver problemas clásicamente intratables aprovechando las propiedades únicas de los cúbits. Sin embargo, la escalabilidad de las arquitecturas cuánticas sigue siendo un desafío significativo. Para abordar este problema, se proponen arquitecturas cuánticas de múltiples núcleos. No obstante, la realización de dichas arquitecturas plantea múltiples desafíos en hardware, algoritmos y la interfaz entre ellos. En particular, uno de estos desafíos es cómo particionar de manera óptima los algoritmos para que se ajusten dentro de los múltiples núcleos. Esta tesis presenta un enfoque novedoso para la partición escalable de circuitos en arquitecturas cuánticas de múltiples núcleos utilizando Aprendizaje Profundo Reforzado. El objetivo es superar a los algoritmos metaheurísticos existentes, como el algoritmo de particionamiento de FGP-rOEE, en términos de precisión y escalabilidad. Esta investigación contribuye al avance tanto de la computación cuántica como de las técnicas de particionamiento de gráficos, ofreciendo nuevos conocimientos sobre la optimización de los sistemas cuánticos. Al abordar los desafíos asociados con la escalabilidad de las computadoras cuánticas, abrimos el camino para su implementación práctica en la resolución de problemas computacionalmente desafiantes.Quantum computing holds immense potential for solving classically intractable problems by leveraging the unique properties of qubits. However, the scalability of quantum architectures remains a significant challenge. To address this issue, multi-core quantum architectures are proposed. Yet, the realization of such multi-core architectures poses multiple challenges in hardware, algorithms, and the interface between them. In particular, one of these challenges is how to optimally partition the algorithms to fit within the cores of a multi-core quantum computer. This thesis presents a novel approach for scalable circuit partitioning on multi-core quantum architectures using Deep Reinforcement Learning. The objective is to surpass existing meta-heuristic algorithms, such as FGP-rOEE's partitioning algorithm, in terms of accuracy and scalability. This research contributes to the advancement of both quantum computing and graph partitioning techniques, offering new insights into the optimization of quantum systems. By addressing the challenges associated with scaling quantum computers, we pave the way for their practical implementation in solving computationally challenging problems