Data integrity and privacy are critical concerns in the financial sector. Traditional methods of data collection face challenges due to privacy regulations and time-consuming anonymization processes. In collaboration with Banco BV, we trained a hybrid quantum-classical generative adversarial network (HQGAN), where a quantum circuit serves as the generator and a classical neural network acts as the discriminator, to generate synthetic financial data efficiently and securely. We compared our proposed HQGAN model with a fully classical GAN by evaluating loss convergence and the MSE distance between the synthetic and real data. Although initially promising, our evaluation revealed that HQGAN failed to achieve the necessary accuracy to understand the intricate patterns in financial data. This outcome underscores the current limitations of quantum-inspired methods in handling the complexities of financial datasets. Graphical

The Variational Quantum Eigensolver (VQE) is a hybrid algorithm that combines quantum and classical computing to determine the ground-state energy of molecular systems. In this context, this study applies VQE to investigate the ground state of protocatechuic acid, analyzing its performance with various Ansatzes and active spaces. Subsequently, all VQE results were compared to those obtained with the CISD and FCI methods. The results demonstrate that Ansatzes, like Unitary Coupled Cluster Singles and Doubles (UCCSD) and variations of Hardware-Efficient Ansatzes, generally achieve accuracy close to that of FCI. In conclusion, this study highlights the effectiveness of VQE as a robust method for investigating molecular ground-state energies. Additionally, the findings emphasize the pivotal role of Ansatz design and active space selection in optimizing VQE performance, offering meaningful insights into its capabilities and constraints.

A realidade atual exige conhecimentos científicos modernos para interpretar e participar das decisões sobre Ciência e Tecnologia. No ensino de Física, a Física Moderna e Contemporânea é pouco abordada na educação básica devido à desatualização curricular e falta de materiais, afetando a formação crítica dos estudantes. Mesmo quando abordada, a ênfase se limita aos primórdios da Mecânica Quântica, insuficiente para a compreensão dos problemas atuais. Este trabalho propõe explorar práticas de ensino de Computação Quântica no Ensino Médio, destacando sua relevância científica e industrial. Essas práticas se baseiam em ações realizadas no âmbito da presente pesquisa, assim como ações identificadas em um levantamento bibliográfico. Identifica-se três enfoques: Conceitual, Instrumental e Sociocientífico. O Enfoque Conceitual articula os conceitos da Computação Quântica; o Enfoque Instrumental aborda habilidades procedimentais e uso de tecnologias digitais e algoritmos quânticos; o Enfoque Sociocientífico foca na formação de atitudes e valores. A partir desses enfoques, elaborou-se uma matriz de orientação com três objetivos de aprendizagem: Informação, Questionamento e Tomada de Decisões. Essa matriz visa não apenas classificar, mas proporcionar uma compreensão abrangente das abordagens, possibilitando orientar práticas de ensino de Computação Quântica no ensino médio e como base para futuras investigações sobre a interseção entre práticas educacionais e discursos científicos.

Understanding the thermodynamic properties of quantum systems is essential for developing energy-efficient quantum technologies. In this regard, this work explores the application of quantum computational methods to study the quantum properties and work extraction processes in a dinuclear quantum battery model. Our results demonstrate that variational quantum algorithms can reproduce key trends in experimental data, making it possible to analyze the effectiveness of the presented protocol in noisy environments and providing insights into the feasibility of quantum batteries in near-term devices. We have shown that the presence of a noisy environment hinders the accuracy of the evaluation of the amount of energy stored in the system. Additionally, we analyze the work extraction precision, revealing that although the system can store energy at room temperature, the protocol is highly precise only at low temperatures, and its accuracy at ambient conditions remains limited, compromising its usability.