Experimental Quantum Embedding for Machine Learning

Abstract

The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Ref.[10] has advanced the proposal to embed classical data into quantum ones: these live in the more complex Hilbert space where they can get split into linearly separable clusters. Here, we implement these ideas by engineering two different experimental platforms, based on quantum optics and ultra-cold atoms respectively, where we adapt and numerically optimize the quantum embedding protocol by deep learning methods, and test it for some trial classical data. We perform also a similar analysis on the Rigetti superconducting quantum computer. Therefore, we find that the quantum embedding approach successfully works also at the experimental level and, in particular, we show how different platforms could work in a complementary fashion to achieve this task. These studies might pave the way for future investigations on quantum machine learning techniques especially based on hybrid quantum technologies.

New trends in quantum machine learning (a)

“…As one can see in Fig. 3 our results [49], even if they contain some noise, are good and able to achieve a good classification boundary between the two classes. That is quite remarkable as this scheme is implemented on a real NISQ device via the cloud.…”

“…In Ref. [49] we train an embedding of that dataset and test it with 10 validation points on a real quantum processor. The process has been carried out in the IBM quantum platform using the Valencia QPU (Quantum Processing Unit) composed of 5 qubits.…”

“…there is not a simple threshold allowing us to separate the two classes. In panel (b) an example of the embedding circuit including the final SWAP test to compute the overlap between the two embeddings[49].…”

Quantum Noise Sensing by generating Fake Noise

“…In recent years, machine learning (ML) has overtaken the computational world, providing many powerful tools to tackle very complex tasks as domotic systems, autonomous cars, face/voice recognition, and medical diagnostics. It did not take long to realize that ML can be beneficial also to quantum computation, and many quantum adaptation of famous ML algorithms have been studied and discussed [8][9][10][11]. As a matter of fact, a whole new branch of quantum computation, dubbed quantum machine learning (QML) [12,13], has risen, exploiting the good behaviour of hybrid quantum-classical computational schemes to look for possible quantum advantages in ML tasks [14] and also to solve genuinely quantum problems [13].…”

Quantum state preparation protocol for encoding classical data into the amplitudes of a quantum information processing register's wave function

“…There have been a number of proposals for using quantum computers to perform machine learning tasks. There have also been a few proof-of-principle experimental demonstrations of such tasks [8][9][10][11].…”