Noise: an unexpected ally of quantum computing

Quantum computers are distinguished by the use of qubits instead of bits. This allows them to store and process much more information at a much faster rate, taking advantage of quantum properties such as overlap and entanglement.

However, there is an important limitation for the full development of these computers: the noise. This causes the appearance of errors that propagate when they are executed complex algorithmswhich limits the promising potential of quantum computing to revolutionize many fields of science and technology.

Teams around the world have been working intensively for years to overcome this barrier, focusing their efforts mainly on techniques for the bug fixes or mitigationsand in the design of simpler algorithms that adapt to the constraints.

Now, researchers at the Autonomous University of Madrid (UAM) have turned the question around. In a recent article published in the journal Scientific Reportspropose an alternative solution: use the noise to improve the results of quantum algorithms.

‘Quantum reservoir computation’

The scientific team has shown that the presence of noise in quantum computers can be beneficial for the results of an important algorithm known as quantum reservoir computing. The predoctoral researcher ilk sunday and the teacher Florentine Borondo from UAM, along with Dr. gabriel carlo of the National Atomic Energy Commission (Argentina).

This algorithm makes predictions of machine learning using quantum systems with random parameters to extract useful information from the studied system. In this way, you can solve very diverse problems, such as quantum chemical calculations or time series forecasts, as well as aiding in drug discovery.

“The idea behind the quantum reservoir computing is to use the hilbert space, where quantum states live, to extract essential properties from the studied data. So, using quantum properties like superposition and entanglement, we can extract useful information from the data and provide it to a machine learning modelthat makes the final prediction”, detail the authors.

A new perspective on quantum computing

The study concludes that some types of noise, such as the so-called amplitude damping noiseimprove the quality of the results of quantum reservoir computing. So not only is it unnecessary to correct this kind of noise, but it can also be beneficial for quantum calculations.

However, other sources of errors, such as so-called depolarizing noisecan degrade the results in all cases, so it is essential prioritize your fix in quantum computers.

The study also provides a theoretical demonstration which helps to explain this phenomenon. Through the mathematical formalism of density matrices and quantum channels, the authors illustrate how noise amplitude damping allows you to more effectively explore the space of quantum operators. This makes it easier to extract more complex and valuable properties from the data, which are then used to predict the target variable.

The discovery offers a new perspective on the physical mechanisms inherent in the quantum devices. Furthermore, it provides solid practical guidelines for a successful implementation of quantum information processing in today’s technology.