Quantum computing, as exciting as it sounds, is really difficult technology to understand and is even harder to make use of, as it is based upon quantum physics. However, a new development in the field of physics may result in computing revolution.
History is repeating itself, just like in 1943 Thomas J Watson thought that computers –being so big in size – would never be able to make its own market, however, with advancements in 1971 microprocessors came into existence and everything changed.
Quantum computing is now following a similar path.
At present, Google, IBM, Microsoft, Rigetti and many other companies own quantum computing systems, which are as big as a room like past century’s supercomputers. They require an overwhelming amount of operating power and thus they can be only used in labs. Even scholars attest that these systems are not feasible for users.
Lately, physics research work of team of international scientists got published which may prove to be a breakthrough in quantum computing. In a paper titled as “Using Machine Learning for Scientific Discovery in Electronic Quantum Matter Visualization Experiments”, the group looked for feasibility of creating a room-temperature superconductor. Highly qualified researchers are ready to find out why superconductors require very low temperatures for conducting.
“Cuprates” – a physics problem with superconductor – is beyond understanding for now. It states that cuprate enters a peculiar state known as “psuedogap” when its temperature is reduced in order to make it conduct. Researchers are not able to find out what happens at that state. According to the published Nature’s article:
“Complex interactions between electrons and atoms make the pseudogap theoretically difficult to describe, and its chaotic nature challenging to observe. Some physicists call the state the cuprates’ ‘dark matter’, yet explaining the pseudogap may be key to understanding superconductivity.”
The team of international scientists developed a machine learning model that can tell if the image illustrated above, will support the first or second hypothesis:
First hypothesis states that psuedogap is due to strong connections between particles.
Second hypothesis states that psuedogap is due to the weak interactions between the waves.
So what did the model tell us? Artificial intelligence (AI) thinks that a phenomenon behind psuedogap is more closely related to particle-interactions hypothesis than the wave-interactions one. Unfortunately, there was no other hypothesis for this, so this result should not be considered as final. Neural networks had to choose between the two, however, they are not intelligent enough to generate something on their own.
Nonetheless, understanding the working of superconductivity in detail could result in the creation of a “microprocessor” for quantum computers. Wrangling quantum bits are not similar to ordering logic gates, but this innovation will have a considerable influence on the uncertainty present that hinders the future progress of quantum computers.
It is not basically the answer to “how do we make quantum computers work without needing to keep them at near perfect-zero temperatures” question, but it is just the beginning. Optimists can see this as a half full glass which will soon be overflowing with quantum computing.