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While many companies now provide access to general-purpose quantum computers, they are not currently used to solve real-world problems because they are hampered by qubit number and quality issues. Most of their users are conducting research projects or simply gaining programming experience on the systems in the expectation that a future computer will be useful.
There are quantum systems based on superconducting hardware that are used commercially; they are just not general purpose computers.
D-Wave offers a so-called quantum annealer. The hardware is a large collection of coupled superconducting devices that use quantum effects to achieve energetic ground states for the system. If configured correctly, this final state represents the solution to a math problem. Annealers cannot solve the same full range of mathematical problems as general-purpose quantum computers, such as those from Google, IBM and others. But they can be used to solve various optimization problems.
Although the systems can suffer from errors, the consequences are relatively minor, as they usually give the systems a solution that is mathematically close to an optimal solution.
Unlike general-purpose quantum computers, quantum annealers have not been mathematically shown to be able to consistently outperform traditional computers. But unlike general-purpose quantum computers, they have had high bit counts, good connectivity, and reasonable error rates for several years now. And a number of companies are now using them to solve real problems.
Drug investigations
One of the companies relying on D-Wave’s hardware is POLARISqb, which works in the field of drug discovery, identifying potential drug molecules in software so that companies can test them in biological systems. The common approach is widespread in the pharmaceutical industry: Identify a disease caused by an inappropriate activity of a protein, then find a molecule that alters the protein’s function in a way that alleviates the disease.
If you know the three-dimensional structure of the protein and which parts of the protein are needed for its functions, you can use computer modeling to see how well drug molecules attach to that part. That kind of modeling is computationally expensive, but it’s still cheaper than synthesizing the molecule and testing it on cells. It’s also part of POLARISqb’s process, but it’s coming after using a quantum annealer, which is used to identify molecules for testing with detailed modeling.
“We design a virtual large chemical space and we use a quantum computer to search that chemical space to find the best molecules,” POLARISqb founder Shahar Keinan told Ars. The concept of “best” here goes far beyond molecules that simply clip onto a protein source.
“We’re not just looking for molecules with a single property, we’re looking for molecules that have a whole profile of properties that will give us what we’re looking for,” said Keinan. “The molecule should not be too big or too small; the molecule should be sufficiently soluble, but not too soluble. The molecule should have certain properties, such as a number of hydrogen bond donors and acceptors.” It also needs to be something that can be synthesized relatively easily.
