Quantum Computing: Current Stage and the Nearest Quantum Future

Google has the most powerful computer right now, but it's still very noisy. On a quantum scale (extremely small), energy moves, packaged in mini-packets (they are called quanta). They scatter, jump off and, interacting with a quantum computer, create errors. If we could cool the quantum system to absolute zero, we would completely eliminate these errors, but we cannot. At the final temperature, one has to rely on suppression methods using error correction codes in order to increase the operating time of a quantum computer before it is “bombed” with unwanted quanta energy.
Quantum computers can be used to model chemicals for the pharmaceutical industry. For example, we can make a quantum-mechanical description of caffeine and simulate it on a couple of hundred qubits. How does it work? Caffeine, like other molecules, is entirely based on quantum mechanics. Quantum mechanics also define mechanics and a quantum processor. So we are just trying to “tune” the physics of the quantum chip to imitate the physics of caffeine.

Nowadays the biggest achievement in molecular modeling is lithium hydride (LiH) and beryllium hydride (BeH2). Thanks to the recently developed algorithm of Variational quantum engine solver, these molecules were successfully modeled on an IBM processor using only 6 qubits.

Quantum computers will help to discover new drugs and materials. They will be able to study all variants of interaction with drugs and calculate the probability of success for each due to a more accurate understanding of the structure of DNA and protein folding. That is why large technology companies are very interested in this area. Some of them have already bought D-Wave calculators for 2 thousand spins - for $ 15 million each. Such cost is because of that they require a cooling system to −273 degrees Celsius. By the way, they can be rented. It is not a universal quantum computer, but it is a quantum computer in the sense that it uses quantum effects to accelerate optimization and Gibbs sampling (a joint distribution sample generation algorithm) used in deep learning. At the same time, IBM provides free online access to its two chips.

Spins are used to characterize adiabatic computers (they are also often called qubits because in a sense they are one and the same). Quantum calculators are suitable for optimization problems (unconditional optimization, binary optimization) and for training neural networks. Volkswagen used the D-Wave computer to optimize taxi routes in Beijing. But chemical simulation is the goal of IBM and Google computers. In the long run, we need both types - we need to optimize the routes of the aircraft, financial portfolios and more, and we also need the best medicines and materials.

As for machine learning, it is not yet clear when the quantum revolution will occur. So far we do not even know which of the main available processors will be the first in calculations with which a classical computer cannot cope. However, we believe that this will happen in the next few years. After the success of a project to optimize Beijing taxi routes (the company managed to “untie” traffic jams created by thousands of cars), Volkswagen plans to use quantum computers to simulate the complex chemistry of autonomous vehicle batteries, as well as machine learning cars to recognize the environment.

There are other types of quantum computers. For example, the Canadian Xanadu is not based on qubits (although it can imitate qubits), but on continuous variable photon systems. Perhaps one of these projects will become the very breakthrough technology that the world is waiting for.

In the next 3-5 years, we will have more and more types of quantum computers based on superconductors, rather than photonics. D-Wave and Google and IBM chips are based on superconductors. This technology is closest to what we already have: you can use the existing capacity to create computers.