Research Projects

  • AI-assisted digital laser frequency stabilization for atomic spectroscopy

    Dr Eric Howard, Dr Cyril Laplane

    High precision measurements in quantum optics and atomic physics rely on the fine control of the experimental parameters and require the locking stabilisation of the frequency of the transmitted signal. This project entails the design, development and characterization of a loop-back control system and digital controller for laser frequency stabilization. The hardware will be based on a RedPitaya STEMlab platform and will be used to lock the laser emission frequency to the cavity resonance of reference and spectral peak maximum for Doppler-free absorption spectroscopy experiments with Rubidium. The student will employ machine learning methods for analysis and optimisation of the interfacing and acquisition of the emission spectra for atomic vapor saturated absorption spectroscopy experiments.

    For more information, contact the project supervisor: Dr Eric Howard
    This project would suit: This Master's project is suited to graduates with a strong background in electronics or optoelectronics and an interest in embedded systems and quantum/atomic physics.
    Macquarie University
    Masters,
  • Advanced digitisation techniques and threshold effects in experimental quantum simulators

    A/Prof Nathan Langford, Dr JP Dehollain, A/Prof Daniel Burgarth, A/Prof Dominic Berry

    This project is part of our exciting new ARC-funded research grant, where we aim to enhance high-tech quantum simulators to meet the demands of computer-modelling intensive industries such as drug and vaccine design. By developing innovative digitisation and control techniques for simulating the behaviour of complex quantum systems, a task that is generally impossible to solve with classical computing technology, this project aims to help shape the design of future quantum computers and maximise the modelling power of current industry-scale processors built by companies like Google, IBM and Australian start-up, Silicon Quantum Computing.

     

    For more information, contact the project supervisor: A/Prof Nathan Langford
    This project would suit: We encourage high performing students to apply who are undertaking an Honours or Master's degree in an appropriate subject area, such as physics or engineering, and strong results in undergraduate courses in quantum physics and other relevant subject areas. The funding for this project is eligible for Australian domestic students only.
    University of Technology Sydney
    PhD,
  • Automated laser beam alignment optimization using machine learning techniques

    Dr Eric Howard, Dr Cyril Laplane

    Complex light fields used in optical tweezers require advanced optical manipulation and control of the laser beam. The project focusses on the design, experimental setup and characterization of a beam auto-aligner system on a Raspberry Pi controlled stepper motor. The system will be used for maintaining and manipulating the intensity distribution of the laser beam and precise optical beamshaping by a spatial light modulator patterned optical trap for cold atoms. The work involves developing a machine learning algorithm for optimization of the “walking the beam” technique, used in most quantum optics experiments and control of structured light for advanced optical manipulation. The algorithm can be used to optimize the laser power into optical fibers, better modulation of the amplitude and phase of light and for controlling of the overlapping beams in a pump-probe experimental setup. The precise control of the laser beam intensity distribution enables the fine tuning of configurable potential wells for future optimized optical trapping experiments.

    For more information, contact the project supervisor: Dr Eric Howard
    This project would suit: This Master's project is suited to graduates with a strong background in electronics or optoelectronics and an interest in embedded systems and quantum/atomic physics.
    Macquarie University
    Masters,
  • Bench-Q. Performance analytics for quantum computing

    Dr Simon Devitt, Prof Michael Bremner

    Performance analytics are are critical component to the classical information processing industry. Knowing how algorithms are expected to perform and how they can be optimised for various hardware platforms is critical. Quantum is no different.

    With automatic error-corrected compilation from high-level circuits written in major languages such as QISKIT, Cirq or Q#, the Bench-Q platform is designed to give detailed breakdowns on the number of qubits and the time needed to complete a given algorithm. With collaborations with major hardware providers such as IonQ, Rigetti, SQC and PsiQuantum, performance analytics can be tailored to each hardware platform such that rapid feedback on hardware resources can be provided to both quantum hardware and software developers.

    For more information, please visit: www.quantumts.org

     

    For more information, contact the project supervisor: Dr Simon Devitt
    This project would suit: Software engineering and development, quantum error correction, designing quantum programming frameworks
    University of Technology Sydney
    PhD,
  • Designing a quantum sneakernet

    Dr Simon Devitt

    We are working on a fundamentally new model of quantum communications that leverages the age old concept of physically carrying around hard drives (in this case, quantum hard drives). We cannot build a functional communications system unless we can make it fast, and loading information onto hard drives that are physically shipped around the world would not be the best way for you to binge the latest Netflix series. A quantum sneakernet does not suffer from this fatal flaw. Flexible and highly complex quantum networking is possible.

     

    For more information, contact the project supervisor: Dr Simon Devitt
    This project would suit: Theory, quantum communications, architecture and system design.
    University of Technology Sydney
    PhD,