Over the last 20 years, thanks to the development of quantum technologies, it has been possible to deploy quantum algorithms and applications that before were only accessible through simulation on real quantum hardware. The current devices available are often referred to as noisy intermediate-scale quantum (NISQ) computers, and they require calibration routines in order to obtain consistent results.
In this context, we present Qibo, an open-source framework for quantum computing.
Qibo was initially born as a tool for simulating quantum circuits.
Through its modular layout for backend abstraction, it is possible to change effortlessly between different backends, including a high-performance simulator based on just-in-time compilation, Qibojit, which is able to simulate circuits with a large number of qubits (greater than 35).
In the first part of this talk, we show some Quantum Machine Learning algorithms implemented in Qibo, including a generative model (quantum GAN) used in the context of Monte Carlo event generation, a variational quantum circuit used to determine the content of the proton, and a novel way of performing density estimation.
In the second part, we show the latest addition to Qibo, which enables the execution and calibration of self-hosted quantum hardware: Qibolab and Qibocal. The first one is a module that makes it possible to employ the language developed by Qibo to execute quantum circuits on real quantum hardware, also based on different electronics. The second one is a general framework for performing calibration, characterization, and randomized benchmarking protocols on all the platforms compatible with Qibolab.
