QSLAM has come a long way in its history but only recently it became the focus of attention when a successful implementation was made in the past few years. Depending on which aspect was the focus of the work, QSLAM has been interpreted to have different definitions. For example, Safa at al1 introduced Se-qSLAM for Real-Time Place Recognition. Gupta et al2, however, presented an autonomous learning framework, Noise Mapping for Quantum Architectures, for adaptive scheduling of sensor measurements and efficient spatial noise mapping (prior to actuation) across device architectures. Data generation scripts and simulation analysis are accessible on github > qcl-Sydney > quantum slam.

In an article in June 2020, Cosmos magazine writes “ Australian physicists say they have adapted techniques from autonomous vehicles and robotics to efficiently assess the performance of quantum devices: Writing in the journal Quantum Information, a University of Sydney team reports that its new approach has been shown experimentally to outperform simplistic characterization of these environments by a factor of three, with a much higher result for more complex simulated environments.

Afrouzi et al, in their patent on QSLAM describe Q-SLAM to be a Lightweight and Realtime Navigation stack with its differentiation factor being that almost all other variations of Simultaneous localization and mapping such as VSLAM, VIO SLAM, Fast SLAM, etc. heavily rely on non-parametric models. 

1. Safa Ouerghi, Rémi Boutteau, Fethi Tlili, Xavier Savatier. CUDA-based SeqSLAM for Real-Time Place Recognition. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, May 2017, Plzen, Czech Republic. ⟨hal-01712346⟩
2. Swaroop Gupta,∗ Alistair R. Milne, Claire L. Edmunds, Cornelius Hempel, and Michael J. Biercuk ARC Centre of Excellence for Engineered Quantum Systems, School of Physics, The University of Sydney, New South Wales 2006, Australia