Grado en Ingeniería en Sistemas de Telecomunicación - TFG - Cursos 2020/2021 - 2029/2030
Permanent URI for this communityhttps://hdl.handle.net/10171/60931
See
13 results
Results
- Design of a control and readout system for quantum computing based on superconducting qubits.(Servicio de Publicaciones. Universidad de Navarra, 2023-09-07) Nerushenko Savitskaia, A. (Aleksei); Solar Ruiz, H. (Hector)This thesis will focus on the development of testbenches in Cadence Virtuoso for the verification of the readout and control of analog electronics involving transmon qubits. Taking the parameters of an existing qubit upon which the simulations will be compared to, the system will provide the resulting signals that are involved in the processes of control and readout. Custom analog models will be programmed in order to implement all the arithmetic calculations needed to run the simulations. These programs can be separated into control and readout and they will be written using Verilog-A as the programming language. For the control of the qubit two different codes will be implemented, one of them will generate Gaussian pulses and the other one will perform the relevant operations. For the readout a distinction will be made between transient and steady-state readout. The steady-state readout will consist of a single code that will output relevant values and signals based on the qubit parameters that the user inputs. The transient readout will study how the system operates in the first microseconds of the readout process and simulate how the resonator charges up before the system stabilizes. Both readout systems will simulate noisy and noiseless outputs. After validating the proper functionality of the systems the testbenches will be available for other users to validate the analog electronics designed for the readout and control of a transmon-type qubit.
- Quantum Error Correction for Realistic Decoherence Models.(Servicio de Publicaciones. Universidad de Navarra., 2022-10-07) Nolasco Ferencikova, C. (Carolina)Quantum Computing has garnered the attention of the scientific community, due to its revolutionary potential to tackle problems that are infeasible with classical computation. Quantum Error Correction is the field of study in quantum communication that focuses on detecting and correcting errors, caused by decoherence, that corrupt quantum information, in order to make fault-tolerant Quantum Computing feasible. Quantum channels model the effects of decoherence, which, in recent studies, have been proven to be time-variant. Current correction schemes, which were designed considering static decoherence models, under-perform under time-varying noise scenarios. In order to adapt these schemes, it is necessary to correctly estimate the channel error and detect its time variations. With this framework, the goal of this project is to study how to estimate quantum channel error probabilities with its time fluctuations effectively and with minimum delay.
- Machine learning methods for drug repurposing(2022-07) Ochoa-Álvarez, I. (Idoia); Casals-Baena, M. (Mikel)Drug Repurposing consists on using already approved drugs to treat other diseases. This is done by identifying new targets that the drug may have. To accelerate this long and costly process, computational methods have been developed to predict drug-target interactions (DTIs). Recently, Machine Learning has had a tremendous impact on many scientific fields including DTI prediction. In this project, two state-of-the-art methods named MolTrans and Hyper- AttentionDTI are described and compared on 8 different datasets. Moreover, each dataset is divided according to 3 different splits, so that the generalization of the methods with respect to drugs and proteins is tested. Graphs are a type of data structures that have nodes and edges connecting them. They can model complex systems accurately such as DTI networks. In recent years, Graph Machine Learning methods have been developed to improve on conventional models. Node embedding techniques and Graph Neural Networks are introduced as ways to transform nodes into vector embeddings to be able to make predictions on them. While the two analyzed methods offer competitive results, Graph Machine Learning can take advantage of the expressiveness that graphs have in order to make more accurate predictions.
- Modified belief propagation decoders applied to non-CSS QLDGM codes.(Servicio de Publicaciones. Universidad de Navarra, 2021-06) Aizpurua Altuna, B. (Borja); Crespo-Bofil, P. (Pedro)Quantum technology is becoming increasingly popular, and big companies are starting to invest huge amounts of money to ensure they do not get left behind in this technological race. Presently, qubits and operational quantum channels may be thought of as far-fetched ideas, but in the future, quantum computing will be of critical importance. In this project, it is provided a concise overview of the basics of coding theory and how they can be used in the design of quantum computers. Specifically, Low Density Parity Check (LDPC) codes are focused, as they can be integrated within the stabilizer construction to build effective quantum codes. Following this, it is introduced the specifics of the quantum paradigm and present the most common family of quantum codes: stabilizer codes. Finally, it is explained the codes that have been used in this project, discussing what type of code they are and how they are designed. In this last section, it is also presented the ultimate goal of the project: using modified belief propagation decoders that had previously been tested for QLDPCs, for the proposed non-CSS QLDGM codes of this project.