It’s almost impossible to avoid reading about advances in quantum computing these days. Despite this, we’re still some way off having fully fault-tolerant, large-scale quantum computers as of right now. One practical difficulty is that even the best present-day quantum computers suffer from noise that can often cause them to return erroneous results.

Research in this field can be broadly divided into two areas: a) designing quantum algorithms with potential practical advantages over classical algorithms (the software) and b) physically building a quantum computer (the hardware).

One of the main approaches to algorithm design is to minimise the number of operations or runtime in an algorithm. One intuitively expects that reducing the number of operations would decrease the chance of errors – the key to constructing a reliable quantum computer.

However, this is not always the case. In a recent paper, the research team found that minimising the number of operations in a quantum algorithm can sometimes be counterproductive, leading to an increased sensitivity to noise. Essentially, running a faster algorithm in non-ideal conditions can result in more errors than if a slower algorithm had been used.

The authors proved that there’s a trade-off between an algorithm’s number of operations and its resilience to noise. This means that, for certain types of errors, slower algorithms might actually be better in some real-world conditions.

These results bring together research on quantum hardware and software. The mathematical framework developed will enable quantum algorithms to be designed with the limitations of current real quantum computers in mind.

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