Join us for the operational launch event of the Open Quantum Institute (OQI) – a new, three-year CERN-based programme that will help to unleash the full power of quantum computing for the benefit of all.

Hosted at CERN, born at GESDA, supported by UBS, the OQI is a collaborative initiative that

On 17 November, join us for the special edition of the CERN QTI lecture series featuring Dr Nicole Yunger Halpern, theoretical physicist at the National Institute of Standards and Technology (NIST), fellow of the Joint Center for Quantum Information and Computer Science, and adjunct assistant

This lecture is organised as part of the CERN QTI online lecture series.

In the past decades, numerous experiments have emerged to unveil the nature of dark matter (DM), one of the most discussed open questions in modern particle physics. Direct detection experiments aim to measure the scattering

This lecture is organised as part of the CERN QTI online lecture series.

In the last few years, the progressive miniaturisation of devices has approached the size of atoms and molecules. The study of these quantum systems out of equilibrium contributed to the advancement of quantum thermodynamics

This lecture is organised as part of the CERN QTI online lecture series.

In this talk, the latest developments of Qibo, a full-stack and open-source framework for quantum computing, will be presented.

Qibo was initially born as a quantum circuit simulation tool, but over time new packages were

This lecture is organised as part of the CERN QTI online lecture series.

Large-scale numerical simulations of the Hamiltonian dynamics of a Noisy Intermediate Scale Quantum (NISQ) computer - a digital twin - could play a major role in developing efficient and scalable strategies for tuning quantum

This lecture is organised as part of the CERN QTI online lecture series.

Simulating many-body quantum systems is a promising task for quantum computers. However, early fault-tolerant devices are not expected to correct arbitrary amounts of error, making the quest for better algorithms an important

This lecture is organised as part of the CERN QTI online lecture series.

The representation of data is of paramount importance for machine learning methods. Kernel methods are used to enrich the feature representation, allowing better generalisation. Quantum kernels implement efficiently complex