By the end of 2018, many computing companies, including Google, were asking, “Will 2019 be the year when we finally use quantum computing for real-world applications?”
Although scientists have been working on a quantum computer for over 20 years, they have only managed to make prototypes or “prologues” of the real thing. No current machine can use quantum mechanics unconventionally to solve a problem faster than a conventional computer can for the same problem. Moreover, there is no written computer science paper to support this claim.
Recently, Google appeared to have reached a quantum supremacy milestone. They are hoping to find methods to create instruction while comprehending the produced vast amount of data using quantum mechanics.
However, supercomputers are still faster, able to perform efficiently without requiring any of the ridiculous accommodations a quantum computer needs.
Why quantum then?
Scientists believe that the most challenging problems of our time are solvable instantaneously if the quantum machine can become a reality. With such a quantum processor, we can solve a mathematical problem within an hour, rather than days, after launching the program. The results of the program will be probability tables showing the most likely solutions.
Image: Quantum computing will be the next evolutionary step for AI
Unlike a supercomputer, a fully-functional quantum computer has no lag-time, even with the overwhelming weight of each additional output.
Real-World Applications of Quantum Computers
With such a derail in the advancement of these technologies, do they have any real-world applications?
By exploiting the same extreme seismology sensitivity, quantum mechanics can detect the presence of oil and gas deposits in an area. QuantIC, the quantum imaging technology hub, worked with M Squared, a commercial photonics tools provider, to demonstrate the possibility of these findings back in July 2017.
By measuring disturbances in a gravitational field, QuantIC demonstrated the presence of deeply hidden objects using a quantum gravimeter. The team believes that if this gravimeter becomes both practicable and portable, it can act as an early warning system for tsunamis and predicting seismic events.
With a concept called quantum key distribution (QKD), we can replace our current encryption in communication with quantum keys prone to the effects of entanglement. In other words, a third party attempting to read messages in communications by hacking into the keys would theoretically destroy everyone’s message in the keys entanglement.
Therefore, the produced values of the entangled qubits are subject to quantum effects wherever applied. Based on a huge assumption, scientists haven’t tested the theory in the real world.
For modern classical computers, they require excessive amounts of time to break an encryption code due to the basis of quantity in the vast numbers. However, for a functional quantum computer, it can correctly identify and isolate these large numbers in mere moments.
In 1994, Peter Shor, an MIT professor, invented a quantum algorithm that factored values. Using rather small quantities, experimenters were later able to test this algorithm successfully by building low-qubit quantum systems.
To be particularly sensitive, a quantum computer requires supercooled atoms, suspended in a state. Scientists are therefore employing the phenomenon to create a quantum accelerometer that could produce the exact data for movement.
France’s Laboratoire de Photonique Numérique et Nanosciences, built a hybrid component, pairing the accelerometer with a classical one. Using a high -pass filter, the labticians then subtracted the conventional data from the quantum data resulting in extremely accurate quantum compass. The compass eliminated the scale factors by mostly associated itself with gyroscopic components.
Many proponents believe that quantum systems will be able to learn various patterns of states in massive, synchronized waves rather than succeeding, progressive scans in the near future.
Nevertheless, experts are admitting that it may take several more years before the feasibility of quantum machine learning, after the creation of operational quantum computers.
While the theory of quantum computing continues to develop with remarkable progress on the experimental side, there are no computers currently using quantum mechanics to solve a problem quicker than a conventional computer.