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Quantum computers learn to mark their own work

Scientists from the University of Warwick have created a protocol that allows a quantum computer to check its own answers to difficult problems.

Quantum computing has the potential to answer questions beyond the capabilities of classical computing, but the answers given might not be reliable. Determining whether the answer is correct is beyond the scope of existing computers. As such, checking the answer typically involves using many classical computers to tackle the problem.

Samuele Ferracin, Theodoros Kapourniotis and Dr Animesh Datta from the University of Warwick's Department of Physics have proposed an alternative method that involves using the quantum computer to run a number of easy calculations that we already know the answer to and establishing the accuracy of those results.

A statistical boundary is then created on how far the quantum computer can be from the correct answer in the difficult problem that we want it to answer, known as the target computation.

Dr Animesh Datta explained, “A quantum computer is only useful if it does two things: first, that it solves a difficult problem; the second, which I think is less appreciated, is that it solves the hard problem correctly. If it solves it incorrectly, we had no way of finding out. So what our paper provides is a way of deciding how close the outcome of a computation is to being correct.”

The protocol quantifies the effects of noise on the outputs of quantum computers. Noise is defined as anything that affects a quantum machine’s hardware but is beyond the user’s control, such as fluctuations in temperature or flaws in the fabrication. This can affect the accuracy of a quantum computer’s results.

When applied, the test produces two percentages. Firstly, how close it estimates the quantum computer is to the correct result. Secondly, how confident a user can be of that closeness. The researchers explain this is a similar process to that which computer programmers use to check large computer programs, by putting in small functions with known answers. If the program answers enough of these correctly then they can be confident that the whole program is correct.

These findings were published in The New Journal of Physics, today (18 November). The research was supported by the Engineering and Physical Sciences Research Council, part of UK Research and Innovation, in the UK.

Charlotte Hathway

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