The invention of computer made calculations very fast. It paved the way for a rapid progress in science and technology. The early versions of computer were bulky. However, the invention of integrated chip in 1959 made desktops and laptops possible. For daily life needs, today’s computer technology is good enough. But with the time, data processing and the scientific calculations are becoming very complex – complicated enough for even a super-computer to take months to solve.
That’s where quantum computing comes into picture.
The roots of quantum computing go back to the 1980s. In 1981, Paul Benioff described the first quantum-mechanical model of a computer. Later in 1985, David Deutsch at the University of Oxford described the first universal quantum computer. In 1994, Peter Shor developed a quantum algorithm for factoring integers that had the potential to decrypt all secured communications. Although much research was done in the field and many algorithms were developed based on quantum computing, it was only in 2019 that a quantum computer was finally built. In January 2019, IBM unveiled its first commercial quantum computer: the IBM Q System One.
Quantum computing uses quantum-mechanical phenomena such as superposition and entanglement to perform computation. Superposition is a phenomenon due to which any two (or more) quantum states can be added together and the result will be another valid quantum state; consequently, every quantum state can be represented as a sum of two or more distinct states. This concept in quantum physics is similar to wave superposition in classical physics.
Qubit (quantum bit) is analogous to classical binary bit. Qubits can be in a 1 or 0 quantum state. But they can also be in a superposition of the 1 and 0 states. However, when qubits are measured the result is always either a 0 or a 1; the probabilities of the two outcomes depends on the quantum state they were in at the time.
In a quantum computer, a number of elemental particles such as electrons or photons can be used (in practice, success has also been achieved with ions), with either their charge or polarization acting as a representation of 0 and/or 1. Each of these particles is known as a qubit; the nature and behavior of these particles (as expressed in quantum theory) form the basis of quantum computing.
Being in early stages of research and development phase, quantum computing will need significant advances before it finds its place in the real world. However, there are many possible applications that show the significance of quantum computers.
One of the possible applications of quantum computing is in the field of machine learning. In machine learning, data models have to be trained by a very large database of images and text. Conventional computers take a very long time to train an AI algorithm as it requires a lot of data processing. Quantum computers could empower machine learning by enabling AI programs to search through gigantic datasets concerning medical research, consumer behavior and financial markets. They could also reduce AI algorithm training time to be very short.
Today’s supercomputers have a very fast processing speed, but not fast enough when we consider the modern scientific problems and quantum models. A quantum computer, theoretically, can solve a problem in a matter of minutes than a super-computer that would take years to solve. The fast processing speed of quantum computers over super-computer was evident when, in 2019, Google announced that its quantum machine, code-named ’Sycamore’, solved a problem in 200 seconds that took IBM’s supercomputer ’Summit’ 2.5 days to complete.
Another very important application of quantum computers is in quantum simulations. Since chemistry and nanotechnology rely on understanding quantum systems and such systems are impossible to
simulate efficiently by classical methods, many believe quantum simulation will be one of the most important applications of quantum computing. Quantum simulation could also be used to simulate the behavior of atoms and particles at unusual conditions such as the reactions inside a collider.
Although quantum computing is indeed far better than classical computers, there are some technical challenges in large scale quantum computers. Notable problems are quantum decoherence, limited qubits in quantum computer, and huge cost of building quantum computer. One of the greatest challenges is controlling or removing quantum decoherence: isolating the system from its environment as interactions with the external world cause the system to decohere. As a result of this process, quantum behavior is apparently lost, just as energy appears to be lost by friction in classical mechanics.
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