Conveners
Session 4.3
- Chair: Geza Giedke (Donostia International Physics Center (DIPC))
Session 4.3
- Chair: Enrique Rico Ortega (Ikerbasque & UPV/EHU)
Session 4.3
- Chair: Adán Cabello
Session 4.3
- Chair: Diego Andrade
In a Mach-Zehnder-type light pulse atom interferometer, matter waves are split, mirrored, and recombined using coherent atom optics. With the leading order phase shift scaling with the enclosed space-time area, the momentum transfer induced by the atom optics light pulses as well as the free evolution time are key to significantly enhanced sensitivity to inertial forces and motivate...
Continuous variable quantum key distribution with discrete modulation has the potential to provide unconditional security using widely available optical elements and existing telecom infrastructure, while allowing for the use of well studied error correction protocols. However, proving finite-size security against coherent attacks poses a challenge. In this work we apply the entropy...
In quantum metrology, the usefulness of a quantum state is determined by how much it outperforms separable states. For the maximal metrological usefulness genuine multipartite entanglement (GME) is required. In order to improve the usefulness of a quantum state we consider a scheme of having several of its copies. With this scheme, it is possible to find a large class of practically important...
Quantum machine learning (QML) is often put forward as one of the most likely quantum applications to bring about useful advantages, perhaps even in the near term.
Large-scale quantum computers, once available, will give definite answers to whether this is true, but to make the most out of the significant investments in experimental quantum computing, it is important to try to learn as much...
Quantum Key Distribution (QKD) has the potential to play a significant role in improving security in communication networks in the near future. Since the first experimental demonstration [1], multiple QKD experiments have been carried out, the majority of which were proof-of-concept demonstrations that continually broke new records in terms of transmission distance, in both fibre [2,3] and...
The biggest challenge that quantum computing and quantum machine learning are currently facing is the presence of noise in quantum devices. As a result, big efforts have been put into correcting or mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, we demonstrate that under some circumstances, quantum noise can be used to improve the performance of...
Theoretical and algorithmic advances, availability of data, and computing power have opened the door to exceptional perspectives for application of classical Machine Learning in the most diverse fields of science, business and society at large, and notably in High Energy Physics (HEP). In particular, Machine Learning is among the most promising approaches to analyse and understand the data...
We present our first steps towards the coherent coupling between inhomogeneous magnon excitations and resonant photons living in a superconducting cavity. Using a coplanar superconducting transmission line, we perform broad-band ferromagnetic resonance of thin-film mesoscopic magnets. This allows identifying the low-energy Kittel spin-wave excitation (with infinite wavelength). By patterning...
Quantum machine learning (QML) is recently gaining interest in both theory and experiment thanks to variational circuits implemented in the noisy intermediate-scale quantum computers (NISQs) [1]. Since we are in such an era, algorithms capable of being implemented in small circuits are of great interest. In pursuit of this objective, we explore QML algorithms that are implementable in circuits...
In this talk I will provide a tutorial introduction to quantum simulation with quantum computers. I will review the failure of conventional computing to address many-body problems and how this prevents progress in many scientific areas. I will discuss whether and how quantum computers, either fault tolerant in the future or noisy intermediate scale state of the art, can help to solve...
Quantum reservoir computing (QRC) is a machine learning technique where complex quantum systems are exploited to solve temporal tasks, such as predicting chaotic time series and complex spatiotemporal dynamics. Most existing results in the analysis of QRC systems with classical inputs have been obtained using the density matrix formalism. This paper shows that alternative representations can...
Pattern matching of quantum circuits, the task of finding sub-circuits of a quantum circuit that match a given pattern, is an essential tool of quantum circuit compilation. It can be used for instance to find redundant gate sequences that can be rewritten as more efficient computations. We propose an algorithm that performs this task for many patterns simultaneously, independently of the...
We present a general strategy for mapping fermionic systems to quantum hardware with square qubit connectivity which yields low-depth quantum circuits, counted in the number of native two-qubit fSIM gates. We achieve this by leveraging novel operator decomposition and circuit compression techniques paired with specifically chosen fermion-to-qubit mappings that allow for a high degree of gate...
