Quantum autoencoders to denoise quantum measurements

Researchers at Leibniz Universität Hannover in Germany have recently developed quantum machine-learning algorithms that can be used to denoise quantum data. These algorithms could help to produce more reliable data using quantum clocks or other measurement tools based on entangled quantum states.

Just like traditional machine-learning algorithms, quantum machine-learning algorithms depend on a series of variational parameters that need to be optimized before an algorithm can be used to analyze data. To learn the correct parameters, the algorithm needs first to be trained on data related to the task it is designed to complete (e.g., pattern recognition, image classification, etc.).

As an initial step in their research, the team optimized their algorithms, training them to effectively denoise quantum data. As denoised reference states are hard to obtain or unavailable experimentally, the researchers used a trick that is often used when optimizing classical autoencoders, which are a type of unsupervised machine-learning algorithms.

The researchers have carried out numerous simulations in which they produced noisy entangled quantum states. First, they used these ‘experimental’ outputs to optimize the variational parameters of the autoencoder. Once this training phase was complete, they were able to evaluate their autoencoders’ performance in denoising quantum measurements.

The algorithms require a quantum computer that can process the specific experimental output (i.e., quantum data). For instance, if a researcher is trying to use the autoencoders to denoise data based on trapped ions, but her quantum computer uses superconducting qubits, she will also need to use a technique that can map states from one physical platform to the other.

In the future, the quantum autoencoders developed by these two researchers could be used to improve the reliability of measurements collected using quantum-enhanced tools, particularly those using many-body entangled states. In addition, they could serve as interfaces between different quantum architectures. (Phys.org)

The paper has been published in Physical Review Letters.

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