
Case Study : Floquet Deep Learning Optimiser (DLO) combined with the Moku Go multi-instrument system to auto-align Optical Cavity systems
Establishing suitable control settings for both laboratory and/or industrial equipment is a critical part of ensuring their ability to consistently function as intended. The DLO allows this to be completed in an highly-automated manner particularly in cases where :-
- The target system operates restrictively under a sparse sampling constraint
- Where it is monetarily expensive to acquire sample data
- High dimensionality environments
- The target system runs in a cycle and may be automated
- Hard parameter bounds exist, precluding many traditional learning algorithms
Combining academic excellence and commercial software
development in the Quantum space


Aqacia
- Academic Quantum Credentials – Ping Koy Lam, Ben Buchler, Aaron Tranter
- Direct links to A*Star (Singapore)
- Quantum clientbase – SQC, Diraq, Nomad Atomic
- AI/ML Clientbase – PV Lab
- Founding role – Canberra Quantum Hub
2pi Software
- Software Engineering and Cloud Powerhouse
- Early to Quantum (QRNG)
- Blue Chip clientbase – AEC, CSIRO, ACT Gov, Bega Cheese
- Leading partnerships – AWS, GitLab
- 11 years Commercial Experience