# Welcome to the Quantum Photonics Laboratory!

We are an experimental group at RMIT University’s School of Engineering. Our research spans from engineering photonic quantum information and communication technologies to studying quantum effects in biological, chemical and physical systems.

# RECENT NEWS

## QPLab Secures ARC Funding for Centre of Excellence in 2017

The ARC’s seven-year budget will support nine new Centres of Excellence, which involve significant collaboration between universities, publicly funded research organisations, other research bodies, governments and businesses.The aim, according to the ARC, is to undertake highly innovative and potentially transformational research with the aim of achieving international standing and a significant advancement of capabilities and knowledge.

RMIT researchers are chief investigators, leading collaboration across multiple universities, in three successful bids. Dr Alberto Peruzzo (School of Engineering) and Dr Nicolas Menicucci (School of Science) have secured funds for a new round of the Centre for Quantum Computation and Communication Technology. This centre will receive \$33.7 million to implement quantum processors able to run error corrected algorithms and transfer information across networks with absolute security. The new technology is expected to provide a strategic advantage in a world where information security is of paramount importance.

## Self-guided quantum tomography published in Physical Review Letters

In this paper we report on the experimental demonstration of self-guided quantum tomography—a first of its kind iterative method to characterize quantum systems—and show its superior performance and robustness over standard quantum tomography in several photonic one- and two-qubit experiments. Standard quantum tomography requires storing and post-processing data from an exponentially large number of measurements, making this technique inapplicable for quantum states being prepared today. Moreover, it lacks robustness against inevitable statistical noise and measurement errors.

By iteratively learning the quantum state through a stochastic optimization algorithm, self-guided quantum tomography is far more resource efficient—thus can be applied to larger systems—and achieves higher accuracy thanks to its robustness against statistical noise and measurement errors.

Self-guided quantum tomography will likely be soon necessary in quantum experiments where standard quantum tomography is already unfeasible. Applications of the algorithm outside quantum tomography include state preparation and quantum device control. As automation will be critical for future quantum technologies, our demonstration takes an important step towards practical realization of autonomous learning.