Error reduction strategies in the NISQ era

Working with quantum computers shows us that results are subject to errors. In order to get a feeling again what this means, let's take a simple example we have been using before and compare its outcome from an error-corrected quantum computer with current, noisy hardware.

This example uses the Bernstein-Vazirani algorithm that efficiently determines a secret code using just one call of a given black box (i.e. it solves the quantum combination lock). Instead of setting the last qubit to $|1\rangle$ from the start, we apply an X gate here instead. Check this section for more details.

The result of current quantum computers are subject to noise. Noise describes unwanted external disturbances that can cause errors in quantum computations and thus reduce the accuracy of the results. It's as if someone is making a lot of noise while you're trying to concentrate on a task 🙇‍♀️. It makes it harder to perform the task correctly.

When running the same quantum algorithm on an ideal quantum computer and a noisy quantum computer, the results obtained from the noisy one may be less accurate and less reliable than those from the ideal one. In fact, overcoming the problem of noise is one of the biggest challenges in building and using quantum computers, and researchers are actively working on improving quantum computing hardware.
But there are also techniques to reduce the impact and mitigate the effects of noise, thus advancing the overall reliability of quantum computing.

About this module

In this module you will learn ...

• ... what strategies exist to reduce the errors in quantum computation in NISQ devices.

• ... apply error reduction strategies when using IQM hardware.

Our modules are exploratory in nature. Thus, you will actively experience the different ideas of quantum computing, its algorithms and its applications.