38 research outputs found
Entanglement from dissipative dynamics into overlapping environments
We consider two ensembles of qubit dissipating into two overlapping
environments, that is with a certain number of qubit in common that dissipate
into both environments. We then study the dynamics of bipartite entanglement
between the two ensembles by excluding the common qubit. To get analytical
solutions for an arbitrary number of qubit we consider initial states with a
single excitation and show that the largest amount of entanglement can be
created when excitations are initially located among side (non common) qubit.
Moreover, the stationary entanglement exhibits a monotonic (resp.
non-monotonic) scaling versus the number of common (resp. side) qubit
Quantum Approaches to Data Science and Data Analytics
In this thesis are explored different research directions related to both the use of classical data analysis techniques for the study of quantum systems and the employment of quantum computing to speed up hard Machine Learning task
Homological analysis of multi-qubit entanglement
We propose the usage of persistent homologies to characterize multipartite
entanglement. On a multi-qubit data set we introduce metric-like measures
defined only in terms of bipartite entanglement and then we derive barcodes. We
show that they are able to provide a good classification of entangled states,
at least for a small number of qubit
Quantum median filter for total variation image denoising
In this new computing paradigm, named quantum computing, researchers from all over
the world are taking their first steps in designing quantum circuits for image process-
ing, through a difficult process of knowledge transfer. This effort is named quantum
image processing, an emerging research field pushed by powerful parallel comput-
ing capabilities of quantum computers. This work goes in this direction and proposes
the challenging development of a powerful method of image denoising, such as the
total variation (TV) model, in a quantum environment. The proposed quantum TV is
described and its sub-components are analysed. Despite the natural limitations of the
current capabilities of quantum devices, the experimental results show a competitive
denoising performance compared to the classical variational TV counterpar
Machine-learning based noise characterization and correction on neutral atoms NISQ devices
Neutral atoms devices represent a promising technology that uses optical
tweezers to geometrically arrange atoms and modulated laser pulses to control
the quantum states. A neutral atoms Noisy Intermediate Scale Quantum (NISQ)
device is developed by Pasqal with rubidium atoms that will allow to work with
up to 100 qubits. All NISQ devices are affected by noise that have an impact on
the computations results. Therefore it is important to better understand and
characterize the noise sources and possibly to correct them. Here, two
approaches are proposed to characterize and correct noise parameters on neutral
atoms NISQ devices. In particular the focus is on Pasqal devices and Machine
Learning (ML) techniques are adopted to pursue those objectives. To
characterize the noise parameters, several ML models are trained, using as
input only the measurements of the final quantum state of the atoms, to predict
laser intensity fluctuation and waist, temperature and false positive and
negative measurement rate. Moreover, an analysis is provided with the scaling
on the number of atoms in the system and on the number of measurements used as
input. Also, we compare on real data the values predicted with ML with the a
priori estimated parameters. Finally, a Reinforcement Learning (RL) framework
is employed to design a pulse in order to correct the effect of the noise in
the measurements. It is expected that the analysis performed in this work will
be useful for a better understanding of the quantum dynamic in neutral atoms
devices and for the widespread adoption of this class of NISQ devices.Comment: 11 pages, 5 figures, 3 table
Exploring Network-Related Optimization Problems Using Quantum Heuristics
Network-related connectivity optimization problems are underlying a wide range of applications and are also of high computational complexity. We consider studying network optimization problems using two types of quantum heuristics.One is quantum annealing, and the other Quantum Alternating Operator Ansatz, an extension of the Quantum Approximate Optimization Algorithms for gate-model quantum computation, in which a cost-function based unitary and a non-commuting mixing unitary are applied alternately. We present problem mappings for problems of finding the spanning-tree or spanning-graph of a graph that optimizes certain costs, and a variant that further requires the spanning-tree be degree-bounded. With quantum annealing, all constraints are cast into penalty terms in the cost Hamiltonian, and the solution is encoded as the ground state of the Hamiltonian. We provide three mappings to the quadratic unconstrained binary optimization (QUBO) form, compare the resource requirements, and analyze the tradeoffs. For QAOA, we give special focus on the design of mixers based on the constraints presented in the problem, such that the system evolution remains in a subspace of the full Hilbert space where all constraints are satisfied. In the spanning-tree problem, one such hard constraint is that a mixer applied to a spanning-tree needs also be a spanning tree. This involves checking the connectivity of a subgraph, which is a global condition common for most network-related problems. We show how this feature can be efficiently represented in the mixer in a quantum coherent way, based on manipulation of a descendant-matrix and an adjacent matrix. We further develop a mixer for the spanning-graphs based on the spanning-tree mixer
Preliminary Validation of a Low-Cost Motion Analysis System Based on RGB Cameras to Support the Evaluation of Postural Risk Assessment
This paper introduces a low-cost and low computational marker-less motion capture system based on the acquisition of frame images through standard RGB cameras. It exploits the open-source deep learning model CMU, from the tf-pose-estimation project. Its numerical accuracy and its usefulness for ergonomic assessment are evaluated by a proper experiment, designed and performed to: (1) compare the data provided by it with those collected from a motion capture golden standard system; (2) compare the RULA scores obtained with data provided by it with those obtained with data provided by the Vicon Nexus system and those estimated through video analysis, by a team of three expert ergonomists. Tests have been conducted in standardized laboratory conditions and involved a total of six subjects. Results suggest that the proposed system can predict angles with good consistency and give evidence about the tool’s usefulness for ergonomist
Entanglement entropy production in Quantum Neural Networks
Quantum Neural Networks (QNN) are considered a candidate for achieving quantum advantage in the Noisy Intermediate Scale Quantum computer (NISQ) era. Several QNN architectures have been proposed and successfully tested on benchmark datasets for machine learning. However, quantitative studies of the QNN-generated entanglement have been investigated only for up to few qubits. Tensor network methods allow to emulate quantum circuits with a large number of qubits in a wide variety of scenarios. Here, we employ matrix product states to characterize recently studied QNN architectures with random parameters up to fifty qubits showing that their entanglement, measured in terms of entanglement entropy between qubits, tends to that of Haar distributed random states as the depth of the QNN is increased. We certify the randomness of the quantum states also by measuring the expressibility of the circuits, as well as using tools from random matrix theory. We show a universal behavior for the rate at which entanglement is created in any given QNN architecture, and consequently introduce a new measure to characterize the entanglement production in QNNs: the entangling speed. Our results characterise the entanglement properties of quantum neural networks, and provides new evidence of the rate at which these approximate random unitaries