Physics

White Rabbit (WR) is an open-source synchronisation technology born at CERN and developed with other institutes and companies. It is commercially available from several vendors, and its adoption in industry and academia increased significantly after it was standardised as part of the Precise Time Protocol (IEEE 1588). As a result, the number of requests for support grew, and the core WR team found it difficult to justify the time needed to help new users and developers. This triggered the creation of the WR Collaboration (WRC). Members of the WRC pay a yearly fee which is mainly used to staff the so-called WRC Bureau, whose main tasks include maintaining the fundamental building blocks of WR (the WR Switch and the WR PTP Core) and providing guaranteed support to members. The WRC is also an experiment in public-private partnerships and knowledge transfer, which can become a template for other projects and technologies. After a quick historical and technological introduction, the talk will cover the rationale for past strategic decisions in WR and for the main driving ideas in the WRC. The evolution of the WRC during its first year of existence will be analysed, and the presentation will finish with plans for the short and medium-term future.

Abstract:

Accurate estimation of quantum observables is a fundamental challenge in quantum computing. In this talk, we explore a novel approach to providing unbiased estimators of multiple observables with low statistical error by leveraging informationally (over)complete measurements and tensor networks. The technique involves an observable-specific classical optimization of measurement data using tensor networks, leading to low-variance estimations. Compared to other observable estimation protocols based on classical shadows and measurement frames, our approach offers several advantages: (i) it can be optimized to achieve lower statistical error, reducing the measurement budget required for a given estimation precision; (ii) it scales efficiently to large numbers of qubits due to the tensor network structure; (iii) it applies to any measurement protocol with operators that admit an efficient tensor network representation. We benchmark the method through various numerical examples, including spin and chemical systems in both infinite- and finite-statistics scenarios, and demonstrate how optimal estimation can be achieved even with tensor networks of low bond dimension.

The young Universe - that is, the epoch when first stars, galaxies and black holes formed in less than 0.5 Gyr after the Big Bang - is one of the most actively studied sectors of astrophysics, with deep connections to fundamental physics. In my talk (targeted to non-specialists)  I will present how recent observations - mostly obtained with the space telescope JWST and the ground-based sub-mm array ALMA -  are revolutionising our understanding of the formation of the first galaxies and their embedded Black Holes.

The physical nature of these  galaxies is challenging our models of early galaxy formation, showing that the formation of the first stars has been more efficient than previously expected.

These galaxies also appear to host Black Holes that are more numerous and massive than expected, opening the possibility that they do not descend from stellar seeds but rather from primordial black holes of different nature.

I will finally discuss how these discoveries offer a new way to provide independent constraints on some classes of non-standard cosmologies that are being proposed to address the tensions in the current Λ-CDM model.