# Quantum computing: possible applications

The term **quantum computing** refers to a new way of handling and processing information, by substituting the traditional *bits* currently used by computers with *qubits*. The latter are units of information based on *quanti*, subatomic particles which have extraordinary properties.

The development of **computers based on the principles of quantum physics** began a few years ago and the first prototypes are already available, but there are still several obstacles to be overcome. The possible applications are, nevertheless, very interesting and could open the door to a new concept of computerised calculation.

**How quantum computing works**

Quantum physics is a relatively new science, involving the study of subatomic particles (**quanti**). These are very distinctive, because they do not follow the laws of traditional physics and possess unique properties, such as the overlapping of states, entanglements and quantum interference.

That is where the concept of quantum computing (or quantum computational calculation) comes from, which, instead of the two states of ‘0’ and ‘1’ as in the traditional bit, uses the **complexities of the quantum states **in order to speed up the resolution of algorithms.

Some researchers have theorised that by exploiting these characteristics, it would be possible to achieve** faster calculations**, allowing data to be processed at previously unimaginable speeds.

With such a **calculation capacity** it would also be possible to solve certain problems, which in technical parlance are defined as ‘*complexity classes*’: that is the type of problems which cannot be solved using current systems, due to the enormous amount of resources required (both in terms of time and money and engineering capabilities).

**The evolution of quantum computing so far**

The first to theorise about the possible applications of quanti in computational calculation were Yuri Manin, Richard Feynman and David Deutsch in the seventies and eighties. The next stage was the drafting of certain **algorithms** relating to quantum computing, developed in the nineties by Peter Shor (with his algorithm for cryptoanalysis) and Lov Grover for applications on databases.

The early 2000’s was an interesting time for quantum computational calculation, thanks to the creation of the first **functioning prototypes**. Nowadays, many IT companies are investing considerable resources in the development and improvement of quantum systems which, however, still face several conundrums which need to be resolved.

At the current time, qubits do not have a real superiority over traditional bits in terms of efficiency, given the unpredictable nature of subatomic particles. If you add to this the **constructional** **complexity** of quantum computers and the need to make them work exclusively at **near-zero** **temperatures**, it is clear that there is a still a long way to go.

**Applications of quantum computational calculation**

When all the problems that affect quantum computing have finally been resolved, it will be possible to carry out increasingly complex calculations and manage information in a revolutionary new way. Its **potential applications** will be countless and will involve almost every sector.

In the **short term**, researches will focus on the resolution of problems that require the use of just a few qubits, such as for example simulations in scientific environments. Applications in the chemical-biological and pharmaceutical fields are being discussed, including the analysis of fertilisers and the development of systems for storing energy.

With an increasingly complex technological evolution, in the **medium term**, financial applications and applications for big data analysis will be developed for companies. New systems of cryptography may be created, guaranteeing total security in financial transactions and in communications (especially in the military).

The most forward-thinking experts envisage that, in the **long term**, quantum computing based forms of AI will be created, capable of enhancing the learning speeds of neural networks. This could lead to a new generation of AI with practically infinite potential.

Translated by Joanne Beckwith