Speakers

Entangled Structures in Classical and Quantum Optics

Leader of the Quantum Optics group at the Fluminense Federal University, with research in quantum noise in optical systems, parametric down conversion and twin photon generation, pattern formation, and optical vortices.

Lecture I

Lecture I: In this lecture we introduce the paraxial wave equation that describes the propagation of collimated optical beams and the corresponding solutions in terms of orthonormal mode functions of the beam transverse coordinates. When combined with polarization, they give rise to a tensor product vector space of spin-orbit modes where non-separable (entangled) structures can be recognized as the well-known vector beams.

Lecture II

Quite surprisingly, these entangled structures can be used to simulate some quantum information protocols. Moreover, the spin-orbit mode entanglement can be evidenced by quantum-like inequalities. We will present experimental investigations on both aspects of this classical-quantum connection in optics.

Lecture III

In this last lecture we present the quantized vector beams and discuss the interplay between quantum and classical entanglement in the quantized field framework. Coherent and Fock states provide elementary examples illustrating the subtle connections between mode separability and quantum entanglement.

• Lecture I slides.
• Lecture II slides.

Characterising quantum correlation via semi-definite programming

Researcher of the Institute of Photonic Sciences with a prestigious project and an expert in foundations of quantum physics, specially quantum nonlocality, quantum steering, and its applications to information processing.

Lecture Program

Semi-definite programming (SDP) is a class of optimization problems that appears in several situations in quantum information. In this series of lectures, I will discuss the problem of characterising quantum correlations with SDPs. I will discuss several scenarios, namely the entanglement, quantum nonlocality and Einstein-Podolski-Rosen steering. By the end of the course I expect the students to understand the different notions of quantum correlations, to identify when a problem can be cast as an SDP and to know the basics of how to use available numerical tools to solve them.

• Lecture notes.

Quantum-assisted machine learning in near-term quantum devices

Before joining Zapata Computing as a Senior Quantum Scientist, Alejandro was the lead scientist of the quantum machine learning efforts at NASA’s Quantum Artificial Intelligence Laboratory (NASA QuAIL). He was also the Co-Founder of Qubitera LLC, a consulting company acquired by Rigetti Computing where he worked after NASA and before his current appointment with Zapata Computing. He also holds an Honorary Senior Research Associate position at University College London. His research focuses in exploring the computational limits and opportunities of quantum computers for problems in artificial intelligence

Lecture Program

With quantum computing technologies nearing the era of commercialization and quantum advantage, machine learning (ML) has been proposed as one of the promising killer applications. Despite significant effort, there has been a disconnect between most quantum ML proposals, the needs of ML practitioners, and the capabilities of near-term quantum devices towards a conclusive demonstration of a meaningful quantum advantage in the near future. In this course, we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning tasks. We focus on hybrid quantum-classical approaches and illustrate some of the key challenges we foresee for near-term implementations. We will present as well recent experimental implementations of these quantum ML models in both gate-based (superconducting-qubit and ion-trap) quantum computers and in quantum annealers.