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 refereed to as noisy intermediate-scale quantum (NISQ) computers and they require
calibration routines in order to obtain consistent results.
In this context, we present the latest developments of 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 large number of qubits (greater than 35).
The latest additions have been Qibolab and Qibocal. The first one is a module that makes 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. The advantage of these tools is that we are able to use different setups while accessing them through the same language.
We illustrate two applications of Quantum Machine Learning aimed at HEP and implemented thanks to our framework: a generative model (quantum GAN) used in the context of Monte Carlo event generation and a variational quantum circuit used to determine the content of the proton.
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