Eventos

Title: Quantum Convolutional Neural Networks are (Effectively) Classically Simulable Abstract: Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts. First, that when randomly initialized, they can only operate on the information encoded in low-bodyness measurements of […]

Title: Classical shadows in theory, numerics and experiment Abstract: Classical shadows are a scalable way to extract meaningful information from a n-qubit system in a scalable and online fashion. Crucially, this method has the potential to overcome bottlenecks that plague more traditional general-purpose readout protocols. We will review the overall idea and then present numerical […]

Title: Reservoir computing with complex quantum systems Abstract: Non-conventional computing inspired by the brain, or neuromorphic computing, is a successful approach in a broad spectrum of applications, and in the last few years proposals of Quantum Reservoir Computing have been explored. Quantum physical reservoirs have the potential to boost the processing performance in temporal tasks […]

Title: But why would we use quantum computers after all? Approaching Quantum Machine Learning a little differently Abstract: The last years of research in quantum machine learning have taught us a lot. There are problems where quantum computers have a provable advantage for learning (just apply Shor somewhere!). Training variational "quantum neural networks" is a […]

he 9th edition of the Quantum Information in Spain (ICE) conference will be held from the 11th to the 15th of November 2024 at Puerto de la Cruz (Tenerife). ICE is the annual meeting of the Spanish Network on Quantum Information (RITCE) which brings together national and international researchers working on quantum computing, quantum communications, quantum metrology, quantum […]

Speaker: Marco Cerezo Title: Is Quantum Machine Learning an ill-defined framework? Abstract: In this talk we will discuss some our recent results regarding the trainability and classical simulability of Quantum Machine Learning (QML). Despite the initial hype, it has been shown that many QML models can exhibit critical issues such as barren plateaus, and exceeding […]

Speaker: Diego Andrade Canosa Abstract: Benchmarking Quantum Computers is required to objectively evaluate these platforms' performance, stressing their capabilities and leading the evolution of the hardware platforms. Some approaches work at the hardware level and are not necessarily linked to any practical application, others are based on representative real-world use cases of this technology. This seminar will […]

Speaker: Vedran Dunjko Title: Topics in quantum topological data analysis Abstract: Although still arguably an emerging field in classical machine learning, topological data analysis has already raised substantial interest from the perspective of quantum algorithms in the last few years, and for good reasons. In this talk we will explain why this is the case. We will introduce the topic of topological data […]

Speaker: Andrew Lucas Title: Low-density parity-check codes as stable phases of quantum matter Abstract: Phases of matter with robust ground-state degeneracy, such as the quantum toric code, are known to be capable of robust quantum information storage. Here, we address the converse question: given a quantum error correcting code, when does it define a stable […]

Speaker: Almudena Carrera, Research Scientist at IBM Title: Combining quantum processors with real-time classical communication Abstract: Many applications of quantum computing require more connectivity than the planar lattice offered by the hardware on more qubits than is available on a single quantum processing unit (QPU). In this work, we address both challenges by linking two QPUs […]