TITLE: Quantum Error Correction and Scaling with Trapped-Ions ABSTRACT: Quantinuum's quantum processors use the quantum charge-coupled device (QCCD) architecture with trapped-ion qubits to deliver leading performance with high-fidelity gates and all-to-all connectivity. As we look towards performing more complex algorithms that require vast numbers of operations, it will be necessary to develop quantum error correction […]

QTYR24, the Quantum Technologies for Young Researchers workshop, is a conference designed specifically for scientists in the early stages of their careers working in various branches of Quantum Science. From July 9th to 12th, 2024, in Madrid, this workshop offers a unique opportunity for postdocs, PhD, and Master's students to share their research in a […]

Title: Programming Heterogenous Quantum-Classical Supercomputing Architectures Abstract: Valuable quantum computing will integrate tightly with and depend on classical high-performance computing and AI. Such a hybrid system needs a programming model that enables easy and performant co-programming across quantum and classical resources. NVIDIA CUDA-Q is an open-source platform for integrating and programming QPUs, GPUs, and CPUs […]

Title: Towards quantum advantage on the cloud: benchmarking a 20 qubit quantum computer Abstract: Quantum computing is a field with incredible potential to solve fundamental limitations of classical computing, as well as provide a way for scientists to simulate complex quantum systems. Current technological implementations require further improvements in quality and scalability in order for […]

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 […]