The Quantum Random Number Generator at ANU

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


  • 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