Quantum computing operates without the separation typical of classical systems. In these quantum environments, data is typically integrated directly into the qubits, with computations conducted through operations, or gates, performed on these qubits. Recent demonstrations indicate that in the realm of supervised machine learning, where systems learn to classify objects based on pre-labeled data, quantum systems can outperform their classical counterparts, even when the underlying data resides on classical hardware.
This approach to machine learning utilizes variational quantum circuits, which involve a two-qubit gate operation enhanced by an additional factor that can be maintained on the classical side of the hardware. This factor is transferred to the qubits via control signals, effectively mimicking the communication processes seen in neural networks. The two-qubit gate operation is akin to the exchange of information between two artificial neurons, while the factor represents the weighting applied to these signals.
This innovative method was explored by a team from the Honda Research Institute, in collaboration with the quantum software company Blue Qubit.
Pixels to qubits
The primary aim of this research was to facilitate the transfer of data from classical systems into quantum systems for analysis. During their investigations, the researchers evaluated their methodologies across two distinct quantum processors.
The specific challenge they focused on was image classification, using images from the Honda Scenes dataset, which comprises photographs captured over approximately 80 hours of driving in northern California. Each image is labeled with descriptive information about the scene content. The key question the researchers aimed to address with their machine learning model was straightforward: Is it snowing in the scene?
