In the last few years, quantum computing (in English Quantum Computing) has attracted the attention of fields ranging from physics to engineering, computer science, mathematics and statistics. Data science combines statistical methods and algorithms to extract information from large amounts of data to solve complex real-world problems.
Quantum computing is a branch of study aimed at developing computer technology based on the principles of quantum theory. According to the laws of quantum physics, the processing power of quantum computers is due to their ability to be in multiple states and to perform tasks using all possible permutations simultaneously.
Classical computing is based on Boolean algebra, i.e. bits- 0 or 1-. Although the time each transistor or capacitor needs to be at 0 or 1 before changing state can be measured in billionths of a second, the speed with which these devices can be made to change state is still limited. As progress is made in smaller and faster chips, physical limits of chip materials emerge. This is where the quantum world comes in. In a quantum computer, the charge or polarity of elementary particles - such as electrons or photons - can be used to represent zeros and/or ones. Each of these particles is known as a quantum bit, or qubit. The nature and behaviour of these particles form the basis of quantum computing.
The term "Quantum Data Science" refers to the use of quantum computers for the processing of quantum algorithms. Machine Learning. The goal is to harness the processing superiority of quantum computing to obtain results that are unachievable with classical computing technologies. Quantum computers are designed to handle large volumes of data (Big Data), as well as discover patterns and detect anomalies extremely quickly.
Quantum computers are capable of solving calculations in a matter of seconds that would take a 'normal' computer years. With quantum computing, developers can simultaneously perform multiple calculations from multiple inputs. Quantum computers are essential for processing the vast amounts of data that businesses generate on a daily basis. These computers are capable of quickly solving extremely complex problems in a short time. For example, a quantum computer can carry out calculations in 200 seconds that would take a traditional computer 10,000 years. This is done by translating the real-world problems faced by companies into quantum language.
Sectors for applying Quantum Data Science
Therefore, the main applications of Quantum Data Science are:
- FinanceThe data would be very large and have to be processed in a limited amount of time. Firms would further optimise their investment portfolios and improve systems for fraud detection and simulation.
- HealthDNA research, as well as the development of new genetically personalised medicines and treatments.
- CybersecurityThe new Quantum Key Distribution (QKD) system could lead to advances in data encryption, such as the new Quantum Key Distribution (QKD) system. This new technique for sending sensitive information uses light signals to detect any intrusion into the system.
- Mobility and transportThe project will be used in both car/aircraft design as well as traffic planning and route optimisation systems.
Quantum hardware has been developed by IonQ, Honeywell and DWave. It can be accessed from cloud providers such as Azure, AWS or Google Cloud. Notably IBM, which is developing its own quantum computers and, at the same time, its own cloud infrastructure called IBM Cloud.
The market value of quantum computing is estimated to be more than $2 billion by 2026. By then, there will be an estimated 180 quantum computers.
At Bosonit, we have started to offer our customers the possibility to solve problems with supercomputers that cannot be dealt with in any other way.
