Conveners
Session 3: Quantum Computing
- Andrea Bulgarelli (Istituto Nazionale di Astrofisica (INAF))
Description
Quantum computing
I will summarise the INAF participation to the Spoke 10 of the ICSC (National Research Centre for High Performance Computing, Big Data and Quantum Computing
The intersection of Quantum Computing and Machine Learning, known as Quantum Machine Learning (QML), presents significant potential for advancing data-intensive fields like astrophysics. Astrophysics increasingly relies on Deep Learning (DL) for handling vast datasets generated by ground-based and satellite experiments, though the potential of Quantum DL remains underexplored. This study...
Quantum Machine Learning (QML) is an emerging field that integrates Machine Learning techniques with quantum computing to take advantage of the computational power of qubits. By leveraging quantum phenomena such as superposition and entanglement, QML has the potential to solve complex problems significantly faster than traditional methods, enhancing efficiency in data analysis, optimization...
A hybrid quantum genetic algorithm has been developed to minimize $\chi^2$ functions of different cosmological probes, to find the best-fit value for two cosmological parameters. The algorithm computes the merit function classically, and then uses a quantum circuit to entangle the population and perform crossover and mutation operations. The results show consistency with the drawn plots of the...
