Researchers have now looked into using a Single-qubit classifier with the use of a data re-uploading technique to have the potential to achieve a performance that is on par with classical counterparts under the same set of training conditions.
The development of quantum neural networks as a technology has increased throughout time, especially after the 20th century. Similar to artificial neural networks (ANN), a fresh, practical, and helpful idea known as quantum neural networks have lately been put out (QNN).
Each neuron in the neural network receives information from every neuron in the layer before it. The single-qubit classifier, in contrast, takes data from the prior processing unit and the input (introduced classically). Everything is processed collectively, and the computation’s final result is a quantum state that encodes several uploads of incoming data as well as processing parameters.
When combined with a classical subroutine, a single qubit provides enough processing power to build a universal quantum classifier. This may come as a surprise given that a single qubit only provides a simple superposition of two states and single-qubit gates only rotate in the Bloch sphere. The essential component to get around these restrictions is to allow for numerous re-uploading of data. Then, a quantum circuit can be structured as a collection of single-qubit processing and data re-uploading components.
The study published last November 23, 2022, conducted by researchers from Cornell University, Tapia, Scarpa & Pozas-Kerstjens, titled “Fraud detection with a single-qubit quantum neural network” has tested the applicability of a simple quantum neural network built from a single qubit and employing data re-uploading techniques in the context of NISQ applications was investigated, to a real-world dataset.
The researchers focused on a design that will use the fewest quantum resources while still solving a real-world task with outcomes comparable to equivalent conventional structures. They were able to present a number of different layer formulations, with each of them having the capability to be trained in a variational fashion using a fidelity-based loss function.
The results of the study were promising, however, further exploration of the concept would still be necessary, including the extension of practical architectures beyond binary classification problems. Conclusively, according to the researchers, traditional computing is still considered favorable as it has the ability to deal with large amounts of data in the context of deep neural networks. The study was published last November 23, 2022, under
However, the concept of single qubits quantum neural network would lead to an advantage once validation is conducted on the ability of the qubit to learn the necessary relations between data and associated labels in an effective manner, future architectures involving multiple qubits could refine specific quantum properties, such as the quantum entanglement.
