Fock State-enhanced Expressivity of Quantum Machine Learning Models
This work addresses the data-embedding problem in quantum machine learning, offering a more efficient encoding strategy for researchers in quantum computing and photonics, though it is incremental as it builds on existing quantum methods.
The authors tackled the bottleneck of data embedding in quantum machine learning by proposing a photonic-based bosonic data-encoding scheme that maps classical data into high-dimensional Fock space, using fewer encoding layers and no nonlinear optical components, with expressive power controlled by input photons. They developed three NISQ-compatible binary classification methods with varying resource scaling, demonstrating practical applications for supervised tasks.
The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategies are necessary. We propose a photonic-based bosonic data-encoding scheme that embeds classical data points using fewer encoding layers and circumventing the need for nonlinear optical components by mapping the data points into the high-dimensional Fock space. The expressive power of the circuit can be controlled via the number of input photons. Our work shed some light on the unique advantages offers by quantum photonics on the expressive power of quantum machine learning models. By leveraging the photon-number dependent expressive power, we propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources suitable for different supervised classification tasks.