Participating in the event is simple! All you need to do is sign up to secure your spot. Once registered, you’ll have two options to follow the lectures:

1. Live on YouTube: Tune in to the exciting talks from wherever you are, through live broadcasts on our official YouTube channel. You’ll have the opportunity to interact through comments and directly absorb knowledge from experts in the field of Quantum Computing.

2. In Person: If you prefer a more immersive experience, we invite you to join us in person at the event venue. Check the schedule for details on the timings and locations of the lectures. We look forward to welcoming you to an inspiring environment, where you can directly connect with fellow enthusiasts and delve into revolutionary discussions.

Remember to check the complete schedule for information about timings, locations, and speakers. This is your opportunity to engage with the forefront of Quantum Computing and expand your horizons in this ever-evolving field. We can’t wait to share this journey with you!

Stay tuned to our Instagram and Youtube and don’t miss the opportunity to sign up and participate in this transformative event. Get ready to join us on this journey of discoveries and advancements in Quantum Computing. Your presence is crucial to further enrich this unique experience. We eagerly await your registration!

As we prepare for the exciting seventh edition of the Quantum Computing Workshop, it’s worth reflecting on the previous editions that have brought us to this point:

• VI Quantum Computing Workshop (2023)
• V Quantum Computing Workshop (2022)
• IV Quantum Computing Workshop (2021)
• III Quantum Computing Workshop (2020)
• II Quantum Computing Workshop (2019)
• I Quantum Computing Workshop (2018)

The activities will take place at the following locations:

• João Ernesto Escosteguy Castro Auditorium (EPS Auditorium)
• LIICT6 Laboratory non the 3rd floor of CTC Block B
• INE106 Laboratory, located on the ground floor of the INE Building
• Auditorium of the Department of Architecture and Urbanism

Exploring Physics Problems with Machine Learning and Quantum Variational Algorithms

Felipe Fanchini

Machine learning algorithms and their quantum variational counterparts have proven to be powerful tools for studying complex physical systems. In this talk, we will discuss how these approaches can be applied to fundamental problems in physics. We will present a series of studies we have developed, including the analysis of magnetic systems, non-Markovian processes, quantum synchronization, particle physics, quantum thermometry, and Hamiltonian tomography. These studies demonstrate the potential of these algorithms for exploring properties and behaviors in physical systems.

See recording

Quantum Core Technology Based on Neutral Atoms

Manuel A. Lefrán Torres

In this seminar, we will explore the fundamental components of a quantum processor that utilizes neutral atoms. We begin by analyzing systems that enable the cooling and trapping of alkali neutral atom samples. Next, we will discuss Rydberg atomic states, which are crucial for creating a controlled environment, allowing both quantum simulation and computation. Finally, we will present an example that demonstrates how to develop a controlled quantum gate using precise sequences of light pulses (state rotation). Understanding these elements is vital for advancing quantum technologies based on neutral atoms, promising exciting innovations in quantum computing and simulation.

See recording

Assessing Quantum and Classical Approaches to Combinatorial Optimization: Testing Quadratic Speed-ups for Heuristic Algorithms

Pedro C. da Silva Costa

We propose a benchmarking strategy to compare quantum and classical algorithms for combinatorial optimization, using the Sherrington-Kirkpatrick Hamiltonian as the benchmark problem. Classical and quantum structure search algorithms demonstrate more than quadratic speedup over brute-force methods. However, while there is hope that the quantum heuristic algorithm tested could provide a quadratic speedup over its classical counterpart, it exhibits worse asymptotic scaling in practice. These results highlight the current limitations of quantum heuristics and the strengths of structured search approaches. Our framework offers key insights for future research on benchmarking quantum heuristics, particularly in contexts where access to fault-tolerant quantum computers is unavailable.

See recording

Learning in the Quantum Universe

Robert Huang

I will present recent progress in building a rigorous theory to understand how scientists, machines, and future quantum computers could learn models of our quantum universe. The talk will begin with an experimentally feasible procedure for converting a quantum many-body system into a succinct classical description of the system, its classical shadow. Classical shadows can be applied to efficiently predict many properties of interest, including expectation values of local observables and few-body correlation functions. I will then build on the classical shadow formalism to answer two fundamental questions at the intersection of machine learning and quantum physics: Can classical machines learn to solve challenging problems in quantum physics? And can quantum machines learn exponentially faster and predict more accurately than classical machines? I will answer both questions positively through mathematical analysis and experimental demonstrations.

See recording

Introduction to quantum programming with Ket

Evandro C. R. da Rosa

We can use superposition and entanglement to develop applications accelerated by quantum computers to solve some problems faster than any supercomputer ever could. Although quantum computers capable of outperforming classical computers in solving real-world problems are not yet a reality, we hope they will be ready soon. Until then, we can prepare for that future by developing and testing quantum computing-accelerated solutions today. In this mini-course we will present the main concepts of quantum computing applied in the quantum programming language Ket. We expect all participants to interact during the course, expressing their doubts and testing what they have learned.

Material

Recordings: Part 1, Part 2, Part 3.

Quantum Machine Learning

Lucas Friedrich & Tiago Farias

In this mini-course, we will explore a variety of quantum machine learning algorithms, from their principles to practical applications. We will begin with a discussion on parameterized quantum gates, cost functions, measurements, gradient estimations, and optimization processes, which are essential foundations for understanding the functioning of these algorithms. We will then proceed with an analysis of the family of variational quantum algorithms, such as the Variational Quantum Algorithm (VQA) and quantum neural networks. We will discuss how these algorithms can be applied to classification and regression problems. Through these implementations, participants will gain a better understanding of how quantum machine learning can be used to solve practical problems of scientific relevance.

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Recordings: Part 1, Part 2, Part 3.

Dive into the Fast-Paced Lightning Talks!

The Lightning Talks are a series of quick presentations focused on current topics in quantum computing. Each speaker has 15 minutes to share their ideas and findings, followed by 5 minutes for questions and discussions. This format aims to facilitate a rapid and direct exchange of knowledge.

See recording

Schedule of Lightning Talks

1. Packing Problem - A Quantum and Classical Approach
   César Freitas (UFSC)

2. Benchmarking Complexity in Quantum Processors with a Majorization-Based Indicator
   Nina M. O’Neill (CBPF)

3. Global Entanglement Estimation via Classical Shadows
   João Pedro Engster (UFSC)

4. Shadow Measurements for Feedback-Based Quantum Optimizations
   Leticia Bertuzzi (UFSC)

5. Optimizing Time in Quantum Circuit Execution
   Eduardo Willwock Lussi (UFSC)

The Quantum Computing Hackathon is a unique opportunity to tackle practical challenges and apply your knowledge in the field. The challenges will be announced during the event’s minicourses, and participants will have until 11/04, to submit their solutions.

Although the hackathon does not offer prizes, it provides a collaborative and learning-focused environment, encouraging the exchange of ideas and the development of skills in quantum computing.

Stay tuned! More information will be available soon.

• Desafio: Classical Shadows e Ket [pdf]
• Desafio: Aprendizado de Máquina Quântico [pdf]

The Poster Exhibition provides a valuable opportunity for participants to share their research and discoveries with the academic and professional community. The aim is to create an interactive environment where participants can discuss their projects, findings, and ideas related to quantum computing. It’s an excellent networking opportunity!

To present your poster, simply complete the registration form and bring your poster on the day of the event:

In our Quantum Podcast, experts will share their experiences and perspectives in the field of quantum computing. Join us for authentic conversations as we delve into professional journeys, challenges faced, and the defining moments that shape the path to success in the quantum industry. Whether you’re curious about the possibilities of the quantum world or contemplating your own career options, this podcast offers valuable insights directly from those treading this path!

Participants:

• Rômulo Pinho

• Waldemir Cambiucci

See recording

The VR Quantum Experience is an immersive and interactive Virtual Reality experience designed to make the understanding of fundamental quantum mechanics concepts more accessible.

During the demonstration, participants will dive into a representation of Schrödinger’s wave functions applied to the hydrogen atom. In a virtual environment with 6 degrees of freedom, they will be able to explore different configurations of atomic orbitals, move freely around the atom, scale it up or down, and rotate it.

This dynamic approach offers a unique and accessible perspective for visualizing complex quantum phenomena!

See recording

Evandro C. R. da Rosa

Quantuloop (Brazil)

Evandro is the co-founder of the quantum computing startup Quantuloop, and he is a PhD and MSc candidate in Computer Science at the Federal University of Santa Catarina (UFSC). His research focuses on quantum computer programming and simulation. He is the creator of the open-source quantum programming platform Ket, leading its development within the UFSC Quantum Computing Group.

Felipe Fanchini

São Paulo State University (Brazil)

Felipe is a professor and researcher at the São Paulo State University (Unesp) with experience in quantum information and computation, as well as machine learning. He is a member of the coordination team for the FAPESP program in quantum technologies. His research is related to open quantum systems, quantum information protection strategies, and the analysis of quantum systems using machine learning techniques. In recent years, he has focused on quantum machine learning and quantum optimization, highlighting the confluence of his main areas of expertise.

Hsin-Yuan (Robert) Huang

Caltech (USA)

Hsin-Yuan Huang is a visiting scientist at MIT and a research scientist at Google Quantum AI. He earned his Ph.D. in 2023 under the guidance of John Preskill and Thomas Vidick. Also known as Robert, he will join Caltech as an Assistant Professor of Theoretical Physics in 2025. His research focuses on quantum information theory, quantum many-body physics, and learning theory to advance our understanding of quantum phenomena and accelerate scientific discovery. He aims to develop quantum machines capable of exploring new frontiers in quantum mechanics and beyond.

Lucas Friedrich

Federal University of Santa Maria (Brazil)

Lucas Friedrich holds a Bachelor’s degree in Physics from the Federal University of Santa Maria, where he is currently pursuing his Ph.D. in Physics. His research is focused on the emerging field of quantum machine learning, exploring how quantum computers can offer significant advantages in creating more robust and efficient models compared to classical methods. Additionally, he is involved in developing new optimization methods and the practical application of variational quantum algorithms.

Manuel A. Lefrán Torres

University of São Paulo (Brazil)

Manuel Alejandro Lefrán Torres holds a bachelor’s degree in Physics from the Central University “Marta Abreu” of Las Villas (2014), a master’s degree in Applied Mathematics from the same institution (2017), and a Ph.D. in Physics from the University of São Paulo (2024). He is currently a postdoctoral researcher at the University of São Paulo, working on the project “Molecular Beam in a Single Quantum State”. He has experience in the field of Physics, with an emphasis on Atomic and Molecular Physics, and is interested in research related to cold atoms and molecules, quantum sensors, and quantum computing.

Pedro C. da Silva Costa

University of Technology of Sydney (Australia)

Pedro C. S. Costa is an Assistant Researcher at the University of Technology Sydney (UTS) and an Honorary Researcher at Macquarie University. Additionally, he consults for two American startups, one of which is BosonQ Psi. He completed his master’s degree in Theoretical Physics at Unesp and his doctorate at the Brazilian Center for Research in Physics under the supervision of Fernando de Melo. During his Ph.D., he began specializing in quantum algorithms, with his first significant result being a quantum algorithm for wave equation simulation, developed with Stephen Jordan during his time as a visiting researcher at the University of Maryland in the United States. He conducted his postdoctoral research at Macquarie University in Sydney, Australia, with Prof. Dominic Berry’s group. Subsequently, he worked for a year and a half for the Australian government, aiming to apply quantum solutions to the transportation network. He has developed the most efficient quantum algorithms for both linear and nonlinear differential equations and the most efficient algorithm possible for solving systems of linear equations.

Rômulo Pinho

Dell (Brazil)

Rômulo Pinho is a researcher and Distinguished Engineer at Dell Technologies, primarily working in the fields of AI and Quantum Computing, helping the company explore and define its role at the intersection of these two areas. He holds a PhD in Physics (medical image processing) from the University of Antwerp, Belgium, and has 25 years of experience in R&D across industry and academia, in sectors such as IT, O&G, healthcare, and television. Rômulo joined Dell Technologies (formerly EMC) in 2014 as an AI researcher. Since then, he has led research in areas such as data compression, scientific workflow and workload optimization, pattern and anomaly detection, computer vision, NLP, and more recently, at the intersections of quantum computing and generative AI. He has published over 20 scientific papers in journals and conferences and has more than 100 patents filed.

Tiago de Souza Farias

Federal University of São Carlos (Brazil)

Tiago Farias holds a Ph.D. in Physics from the Federal University of Santa Maria and is currently pursuing postdoctoral research at the Federal University of São Carlos, focusing on quantum computing and machine learning. With experience in both classical and quantum algorithms, his research interests include quantum computing, statistical physics, and optimization. Tiago is dedicated to exploring the frontiers of quantum technology applied to machine learning, aiming to make significant contributions to the field.

Waldemir Cambiucci

Microsoft (Brazil)

Waldemir is the Director of Innovation and Emerging Technologies at Microsoft Brazil, supporting discussions on digital transformation and innovation with corporate clients. He has been involved in projects and discussions on IoT, Industrial IoT, Artificial Intelligence, Machine Learning, Responsible AI, Cloud Solutions, and Quantum Solutions Architecture. With over 25 years of experience in IT, he has participated in major technology forums in Brazil and abroad. Waldemir holds a degree in Computer Engineering and a Master’s in Electrical Engineering from the Polytechnic School of the University of São Paulo (EPUSP). He is currently a PhD candidate at the University of São Paulo, researching quantum algorithms and distributed quantum computing. He is a member of the IEEE Quantum Community, a Qiskit Developer Associate Certified, and a Microsoft Azure Quantum Ambassador in Brazil.

• Prof. Eduardo Inacio Duzzioni, Dr. (UFSC) - Coordenador
• Profª. Jerusa Marchi, Drª. (UFSC) - Coordenadora
• Prof. Paulo Mafra, Dr. (UFSC)
• Prof. Pedro Castellucci, Dr. (UFSC)
• Evandro Chagas Ribeiro da Rosa, Me. (UFSC/Quantuloop)
• Eduardo Palmeira, Me. (UFSC)
• João Pedro Engster, Me. (UFSC)
• Otto Menegasso Pires, Me. (LAQCC/SENAI CIMATEC)
• Eduardo Willwock Lussi, B.Sc. (UFSC/Quantuloop)
• Gabriel Medeiros Lopes, B.Sc. (UFSC)
• Letícia Bertuzzi, B.Sc. (UFSC)
• César Freitas (UFSC)
• Ruan Luiz Molgero Lopes (UFSC)
• Vinícius Luz Oliveira (UFSC)