We characterize for the first time the performances of IBM quantum chips as quantum batteries, specifically addressing the single-qubit Armonk processor. By exploiting the Pulse access enabled to some of the IBM Quantum processors via the Qiskit package, we investigate advantages and limitations of different profiles for classical drives used to charge these miniaturized batteries,...
Activities on Quantum computing are increasing thanks to the push of large investments promoted by Governments, Industries, and international actors of research. This environment stimulates the creation and integration of tools and components to design and simulate quantum circuits.
At the current state of the art, there are several different languages and frameworks for programming quantum...
Starting from the idea of Quantum Computing, conceptualized back to 80s, we come to the present day being able to perform calculations on real prototype quantum computers. Recent technology improvements open new scenarios that quickly lead to the real possibility to integrate this technology into current software architectures. Designing a software on distributed systems is based on essential...
As is well-known, eigenvalue problems have to be solved in many areas of physics and applied sciences. While several algorithms exist for classical computing, the possibilities of quantum computing in this field are not entirely explored. In this talk, we will present a quantum annealer algorithm based on the D-wave system that has the aim of solving the Generalized Eigenvalue Problem for the...
Optimal control techniques provide a means to tailor the control pulse sequence necessary for the generation of customized quantum gates, which help enhancing the resilience of quantum simulations to gate errors and device noise. However, the substantial amount of (classical) computing required for the generation of customized gates can quickly spoil the effectiveness of such an approach,...
We report the experimental realization of the prime number quantum potential $V_N(x)$, defined as the potential entering the single-particle Schroedinger Hamiltonian with eigenvalues given by the first $N$ prime numbers. Using computer-generated holography, we create light intensity profiles suitable to optically trap ultracold atoms in these potentials for different $N$ values. As a further...
Le teorie di gauge rivestono un ruolo fondamentale nella nostra comprensione dei costituenti fondamentali della materia e delle loro interazioni, dalla fisica delle alte-energie alla fisica quantistica a molti corpi a bassa temperatura. Tuttavia, la caratterizzazione completa dei loro diagrammi di fase e la piena comprensione degli effetti non-perturbativi sono ancora dibattuti, specialmente...
Nell'era dei 'Noisy Intermediate Scale Quantum' (NISQ) computer, simulare la dinamica di circuiti quantistici con un elevato numero di qubits a livello dell'Hamiltoniana può essere rilevante per lo sviluppo di strategie efficienti e scalabili per implementare algoritmi in specifici hardware quantistici. Tramite l'impiego di sofisticati metodi di tensor networks, abbiamo sviluppato un...
Quantum computing is becoming a new paradigm in computational physics, with two complementary emerging approaches, the circuit-based technology and the quantum annealers such as D-Wave. To better understand the software and the hardware performances of these new technologies, we have started to investigate how to solve a general optimization problem with the Quantum Annealer D-Wave thanks to a...
Studies of neutrinos from astrophysical environments such as core-collapse supernovae, neutron star mergers and the early universe provide a large amount of information about various phenomena occurring in them. The description of the flavor oscillation is a crucial aspect for such studies, since the physics of matter under extreme conditions is strongly flavor-dependents (nucleosynthesis,...
We present a study on the possibility to apply quantum machine learning techniques for the detection of anomalous patterns in a typical high energy physics detector. To approach this task we propose an anomaly detection algorithm based on a parameterized quantum circuit. The algorithm has been trained on a classical computer and tested with simulations as well as on real quantum hardware....
Le Reti Neurali Quantitiche (RNQ) sono candidate al raggiungimento del vantaggio quantistico nell'era dei Noisy Intermediate Scale Quantum (NISQ)computers. Diverse architetture di RNQ sono state proposte e testate con successo su dataset di prova utilizzati nel machine learning. Tuttavia, l'entanglement generato dalle RNQ non è stato studiato in maniera quantitativa per RNQ con più di pochi...
Machine Learning algorithms are playing a fundamental role in solving High Energy Physics tasks. In particular, the classification of hadronic jets at the Large Hadron Collider is suited for such types of algorithms, and despite the great effort that has been put in place to tackle such a classification task, there is room for improvement. In this context, Quantum Machine Learning is a new...
I will discuss different theoretical results on using quantum computers for learning classical data, and on the use of classical and quantum computers to learn quantum states, quantum processes and quantum channels. In particular, I'll focus on foundational aspects, by introducing entropic measures that allow us to identify what kind of problems can be easily learnt directly from the data.
