Papers QISS1

Kate J Jeffery, Carlo Rovelli CPT Marseille Preprint: –Journal: Trends in Neurosciences, 43, 7 (2020)

Kate Jeffery, Robert Pollack, Carlo Rovelli CPT Marseille Preprint: –Journal: Entropy 21, 12 (2019)

Carlo Rovelli CPT Marseille Preprint: –Journal: Nat. Mater. 20, 2 (2021)

Quantum metrology is one of the most relevant applications of quantum information theory to quantum technologies. Here, quantum probes are exploited to overcome classical bounds in the estimation of unknown parameters. In this context, phase estimation, where the unknown parameter is a phase shift between two modes of a quantum system, is a fundamental problem. In practical and realistic applications, it is necessary to devise methods to optimally estimate an unknown phase shift by using a limited number of probes. Here we introduce and experimentally demonstrate a machine learning-based approach for the adaptive estimation of a phase shift in a Mach-Zehnder interferometer, tailored for optimal performances with limited resources. The employed technique is a genetic algorithm used to devise the optimal feedback phases employed during the estimation in an offline fashion. The results show the capability to retrieve the true value of the phase by using few photons, and to reach the sensitivity bounds in such small probe regime. We finally investigate the robustness of the protocol with respect to common experimental errors, showing that the protocol can be adapted to a noisy scenario. Such approach promises to be a useful tool for more complex and general tasks where optimization of feedback parameters is required.

In physics, the analysis of the space representing states of physical systems often takes the form of a layer-cake of increasingly rich structure. In this paper, we propose an analogous hierarchy in the cognition of spacetime. Firstly, we explore the interplay between the objective physical properties of space-time and the subjective compositional modes of relational representations within the reasoner. Secondly, we discuss the compositional structure within and between layers. The existing evidence in the available literature is reviewed to end with some testable consequences of our proposal at the brain and behavioral level.

Carlo Rovelli CPT Marseille Preprint: –

Carlo Rovelli CPT Marseille Preprint: –

We provide a novel methodological approach to the estimate of the change of the Quantum Vacuum electromagnetic energy density in a High critical Temperature superconducting metal bulk sample, when it undergoes the transition in temperature, from the superconducting to the normal phase. The various contributions to the Casimir energy in the two phases are highlighted and compared. While the TM polarization of the vacuum mode allows for a macroscopic description of the superconducting transition, the changes in the TE vacuum mode induced by the superconductive correlations are introduced within a microscopic model, which does not explicitly take into account the anisotropic structure of the material.

We introduce Compositional Quantum Field Theory (CQFT) as an axiomatic model of Quantum Field Theory, based on the principles of locality and compositionality. Our model is a refinement of the axioms of General Boundary Quantum Field Theory, and is phrased in terms of correspondences between certain commuting diagrams of gluing identifications between manifolds and corresponding commuting diagrams of state-spaces and linear maps, thus making it amenable to formalization in terms of involutive symmetric monoidal functors and operad algebras. The underlying novel framework for gluing leads to equivariance of CQFT. We study CQFTs in dimension 2 and the algebraic structure they define on open and closed strings. We also consider, as a particular case, the compositional structure of 2-dimensional pure quantum Yang-Mills theory.

The existence of incompatible observables is a cornerstone of quantum mechanics and a valuable resource in quantum technologies. Here we introduce a measure of incompatibility, called the mutual eigenspace disturbance (MED), which quantifies the amount of disturbance induced by the measurement of a sharp observable on the eigenspaces of another. The MED provides a metric on the space of von Neumann measurements, and can be efficiently estimated by letting the measurement processes act in an indefinite order, using a setup known as the quantum switch, which also allows one to quantify the noncommutativity of arbitrary quantum processes. Thanks to these features, the MED can be used in quantum machine learning tasks. We demonstrate this application by providing an unsupervised algorithm that clusters unknown von Neumann measurements. Our algorithm is robust to noise can be used to identify groups of observers that share approximately the same measurement context.