MAChine learning via hybrid integrated QUANTUM photonics
Quantum technologies are expected to seriously affect various scientific and daily aspects of modern society. Relevant examples are the simulation of quantum systems, materials engineering, nano-technologies and internet commerce. Machine Learning - how a computer can learn from data - is a lively research area that has advanced quickly over the past few years: its applications are ubiquitous and range from e-commerce, healthcare, to neuroimaging, particle physics, fundamental science.
The aim of MAC QUANTUM is to experimentally address the connection between quantum information and machine learning. By exploiting a hybrid integrated photonic platform, we provide an experimental breakthrough in this research area, considering both "hardware" and "software" contributions, and pursuing three objectives:
1. To develop novel hybrid integrated quantum optical circuits. The goal is to increase the effective Hilbert space dimension, which can be efficiently manipulated for information processing purposes.
2. To demonstrate that integrated photonics can be fruitfully adopted to implement quantum machine learning. We will carry out a variety of on-chip demonstrations of quantum machine learning by exploiting quantum interference/entanglement as building blocks to manipulate vector states in large dimension Hilbert space.
3. To exploit machine learning for new insights in quantum physics. On this purpose MAC QUANTUM will extend our capability to engineer quantum states, unitary evolutions and generalized quantum measurements. We will develop an approach able to self-design the manipulation of quantum states depending on the desired task.
This innovative project goes far beyond the state of the art and promises to capture truly new scientific horizons at the frontier of quantum information, quantum physics, quantum control, machine learning and integrated photonics.