Research

Our group strives to understand the nonclassical phenomena featured in Nature, and how to harness their power to enable new forms of information processing.


Quantum computers carry the promise of unparalleled computational power, offering dramatic speed-up relative to their classical counterparts. However, a precise delineation of the power and limitations of quantum computation is still lacking. Our goal is to research the source, the structure, and the extent of advantage offered by quantum resources (such as contextuality) in quantum computation by exploring how to quantify the cost of resources and then optimize their use. We aim to leverage resource-theoretic approaches to quantum resources to explore the range of applicability of current benchmarks of quantum advantage. We are particularly interested in photonic implementations of quantum computation. 

Does smoking cause cancer? How to answer such a question has been in the minds of people for centuries. Formally, this is explored in the field of causal inference, whose main goal is to understand how to identify the cause-effect relations among a set of variables/systems given their statistical data (called the “causal discovery” problem). This plays a crucial role in a variety of fields, such as medicine, epidemiology, finance, and climate change, to name a few. The relation between the quantum information and causality communities goes both ways: by exporting techniques and ideas from quantum information, the field of classical causal inference has gained new tools for classical discovery. Conversely, the perspective from causal inference has provided insight on the causal mechanisms that could enable Bell inequality violations. In addition, substantial progress has been made on the understanding of “what is happening” (causality) vs. “what an agent believes that is happening” (inference). This has led to the development of a deeper understanding of non-classical aspects of nature, and was enabled by the development of the so-called causal-inferential theories formalism. We aim to combine newly-developed tools from the fields of causation and inference, and apply them to the causal discovery problem.  We're particularly interested in the question of how to propose intrinsically quantum notions of causation and inference. We will further explore how to design algorithms that can leverage these quantum notions to identify the possibly cause-effect relations underpinning physical systems or variables.

To date, it still remains unclear whether Quantum theory is the ultimate theory of Nature, one main reason for this being the tension it displays with the theory of General Relativity. If quantum theory is replaced in the future, what can we expect from the new physical theory that will take its place? When quantum theory emerged, we were taken by surprise: can we prepare ourselves this time? Here we explore the possibilities of a physical theory beyond quantum theory; that is, we search for physical theories that may supersede quantum. We study whether such a theory can be conceived, and, if so, which apparently fundamental quantum features must be abandoned.

It is well known that quantum systems can be correlated in a way that is stronger than classically allowed. But how strong can those correlations be? Can we quantify this in a way that tells us how useful the correlations are to perform secure cryptography? This is just an example of a broad list of questions, all which aim at the following open questions that we tackle here:

- how to generate an intuitive understanding of the statistical predictions of quantum theory, which would help us devise technological applications,

- and how to quantify such non-classicality in a way that measures the quantum advantage. 

Nonclassical features of quantum theory enable information processing, such as cryptography and quantum computing, beyond our classical capabilities. New discoveries in the field of quantum foundations bring constantly new insight on new properties of Nature that we could exploit. Examples of these are Indefinite causal order, and generalised Contextuality. Here we explore how these new phenomena can be used to power technological developments.