Quantum computers have always been considered a kind of “holy grail” of technological innovation, capable of revolutionising the IT world and all of society thanks to their applications. These are computers (still being developed) whose operation exploits the laws of quantum mechanics, overcoming the limits of conventional computers. The latter adopt, as a minimum unit of information, the bit, which can only assume two values (conventionally called 0 and 1).
Quantum computers too have a two-level information unit, the qubit, but with an essential difference. While the bit can only take on one of the two permitted values in a given moment, the qubit may be a combination of both, due to the superposition principle (a peculiar effect of quantum mechanics). And not just this. Thanks to the entanglement phenomenon, a kind of correlation at a distance that can be generated between quantum systems, each qubit can be correlated to another, thus multiplying the computational power. These conditions, if multiplied by a large number of qubits, make the computing power of a quantum computer incomparable to that of any classic supercomputer.
However, although there has been talk of quantum computers for more than forty years (the first to intuit their potential was the American Nobel Prize winner Richard Feynman in 1982), the technological obstacles to overcome to obtain computers for practical applications are still enormous.
In recent years, however, a strong acceleration in high-level investments and projects in the area of quantum computing has been recorded throughout the world, with the development of several promising technologies.
The first result already obtained was the creation of some examples of “quantum advantage”, i.e. the demonstration that some prototypes of quantum computers are already able to do operations in a way that is extremely more efficient than the best conventional computers (although these are, for the most part, symbolic demonstrations not linked to useful applications). INFN is strongly committed to important projects in the field of quantum computing. In particular, it is the sole non-American partner of the Superconducting Quantum Materials and Systems Center – a centre of excellence based at Fermilab in Chicago, founded in 2020, which aims to create a cutting-edge quantum computer based on superconducting technologies in the next few years. For example, the Gran Sasso laboratories are the ideal environment for measuring the effect of cosmic rays and natural radioactivity on decoherence time of quantum computers.
Today, computers and supercomputers are essential tools for physics research, with the purpose of analysing experimental data and implementing theoretical models useful for studying often very complex phenomena, impossible to investigate with conventional analytical tools.
The increase in the sensitivity and efficiency of physics experiments, combined with the use of more and more technologically advanced electronics, has led to an explosion in the quantity of data collected during experiments in recent decades.
For many years, INFN has developed its own infrastructure dedicated to scientific computing. Both analysing data produced by the big experiments and theoretical simulations actually need computing power, large storage quantities, and ultra-fast networks.
The integration of automatic learning or machine learning – i.e. the capacity of a machineto learn and improve its performance with experience – in modelling physical systems and analysing experimental data is offering very interesting opportunities (in some cases, revolutionary ones) for the scientific community.
Since the dawn of the scientific investigation of natural phenomena, the construction ofsimplified models of physical reality has been an essential tool for studying more or less complex systems.