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 the advantages and limitations of different profiles for classical drives used to charge these miniaturized batteries,...
Poster title: "Sideband Thermometry on Ion Crystals"
QCD jets provide one of the best avenues to extract information about the quark-gluon plasma produced in the aftermath of ultrarelativistic heavy-ion collisions. The structure of jets is determined by multiparticle quantum interference hard to tackle using perturbative methods. When jets evolve in a QCD medium, this interference pattern is modified, adding another layer of complexity. By...
In a digital gate-based quantum computer, the effect of noise is usually described as a quantum channel appearing either before or after each gate. However, realistic noises are more complicated because every gate is experimentally realized via the time evolution of quantum controls, thus noises act along with (and affect) the evolution of the system. To handle noise effects in a more...
We introduce the Piquasso quantum programming framework, a full-stack open source platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends Piquasso provides...
Given the recent developments and successes, Quantum Computing (QC) has seen a tremendous increase in relevance and interest. Particularly in the last years, QC algorithms have been developed to study their use in solving typical problems in High Energy Physics.
In this poster, we present the recent QC applications to jet tagging and track reconstruction currently pursued by the “Data...
We study the case where quantum computing could improve jet clustering by considering two new quantum algorithms that might speed up classical jet clustering algorithms. The first one is a quantum subroutine to compute a Minkowski-based distance between two data points, while the second one consists of a quantum circuit to track the rough maximum into a list of unsorted data. When one or both...
Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g. phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing extracting insights about new physics. In this work, using quantum convolutional neural networks we overcome this limit with the determination of the phase diagram of a model...
We summarize our recent work on quantum chaotic sensors, machine
learning in quantum metrology, and Bayesian experimental design and
data analysis.
Measurement devices have traditionally always used integrable systems
as sensors, such as precessing spins or harmonic oscillators prepared
in non-classical states. But large benefits can be drawn from
rendering integrable quantum...
In the Standard Model framework, the Higgs-boson gives rise to the mass of elementary particles. Measuring the coupling of a Higgs-boson to charged leptons is an important process to test the Standard Model predictions. In this work, we only focus on the Higgs' decay into two tau leptons as the masses of electron and muon leptons are too small for the detection of their coupling to Higgs by...
Quantum computing has demonstrated the potential to revolutionize our understanding of nuclear, atomic, and molecular structure by obtaining forefront solutions in non-relativistic quantum many-body theory. In this work, we show that quantum computing can be used to solve for the structure of hadrons, governed by strongly-interacting relativistic quantum field theory. Following our previous...
