01 - microscopy with quantum illumination · 05 - biphotons for nonlinear imaging (bini) matteo...

Miles Padgett ([email protected])


Digital cameras rely upon detector arrays positioned in the focal plane of an imaging system to record the light intensity. For low-light imaging applications the question exists as to how many photons does it take to form an image? For spatially coherent transverse modes, the phase/amplitude functions are orthogonal which means the modes can be distinguished from one another with only a single-photon, indeed this is the basis of spatial mode multiplexing in optical communications 1. By contrast, spatially incoherent images are rarely orthogonal to one another and hence, fundamentally require more photons to differentiate between them. By using entangled, or heralded, photons and a time-gated camera it has been shown that recognisable images can be inferred with the order of one recorded photon per image pixel 2, 3 However, for direct imaging with classical light sources even this one photon per pixel bound is very hard to reach since the readout noise from even the best cameras exceed this level.

In this project we seek to pioneer a new form of low light, photon-sparse imaging using spatially resolved homodyne or heterodyne detection of a weak image whilst ensuring the camera signal is well above its noise floor. It is a curious phenomenon within optics that the interference between two beams has a higher contrast ratio than the ratio of the two beam intensities (e.g. when the beam intensities are 100:1, the fringe contrast is still ≈10%). This means that it is possible to boost the weak signal such that it is above the noise floor of the detector (i.e. one can measure a single photon, or less, even though the detector noise floor is the equivalent of many photons). The spatially resolved homodyne imaging system will be formed from a weak probe beam that illuminates the object and a much stronger reference beam. The two beams will be combined on a sCMOS camera to create interference fringes, revealing the intensity and phase of the probe beam. Performing local Fourier transforms (or a wavelet analysis) on the fringe pattern gives both the intensity (from the fringe contrast) and phase (from the fringe phase) of the object. The only drawback of this approach is that each of the super pixels on which the FT is performed comprise multiple sensor pixels and hence the number of effective image pixels is far fewer than the number of pixels on the array. However, high-magnification images are typically over-sampled and so this reduction in pixel number is not an issue.

Our modelling has shown that even with a sensor noise of order 10 readout electrons per pixel, a probe beam of one photon per supper pixel is sufficient to give a unity signal to noise, even prior to any image de-noising or application of priors to the reconstruction.

01 - Microscopy with Quantum Illumination

• Mirhosseini, M. et al. High-dimensional quantum cryptography with twisted light. New J Phys 17, 033033–12 (2015).

• Morris, P. A., Aspden, R. S., Bell, J. E. C., Boyd, R. W. & Padgett, M. J. Imaging with a small number of photons. Nat Commun 6, 5913 (2015).

• Imaging through noise with quantum illumination, T Gregory, P-A Moreau, E Toninelli, M J Padgett. Sci. Advances

Images courtesy of Kevin Mitchell


Jon Cooper ([email protected])

02 - Intelligent Imaging Solutions for Infectious Disease Monitoring in Homes of the Future

By way of background, Professors Cooper and Faccio have recently been funded as part of a £5.6m, 5 year, EPSRC Healthcare Technologies 2050 Programme Grant on the use of the quantum sensor technology and quantum-inspired imaging, combined with artificial intelligence (AI), to provide new sensors systems for future “intelligent homes”. The main focus of the Programme grant is around using quantum inspired imaging for the diagnosis and screening for non-communicable chronic diseases, such as dementia, stroke and heart disease.

The aim of this PhD project is to build upon on the key technologies, concepts and collaborations, already established within this Programme Grant, but to adapt the proposed quantum sensor technologies and quantum-inspired for the identification/early diagnosis of infectious diseases. The PhD student will produce a minimal viable technology, built around SPAD sensors, with an appropriate form factor and low cost so that the systems can be deployed in households or community settings (such as care homes).

The successful applicant would combine the imaging technologies with AI in order that they can continuously monitor the health of one or more individuals as they move, eat, and sleep. We propose that analysis of these data streams, based upon small but statistically relevant changes in trends in physiological outputs and behaviour will provide proxies for the presence/insurgence of infectious disease. For example, increased skin temperature, changes in blood distribution/flow, increased heart rate, changes in breathing, micromovements and physical agitation, as well alterations in sleep patterns and bathroom visits may be proxies for likely onset of infectious disease.

Although this project is timely in terms of the COVID-19 pandemic, such methods would be equally relevant in providing intelligent early warning in care homes for the annual flu episodes or for non-specific urethritis, where physiological proxies can be linked with behavioural change to provide markers for disease. Appropriate early interventions may halt the spread of disease in a community, by enabling timely medical interventions.

Coupled to these objectives is a longer-term aim that, through demonstration of cloud connectivity, the student will also be able to show how such systems can be used to monitor disease progression, including its spread within a community. The future vision is that of networked houses and homes that continuously monitor health based on the measurement of these small but statistically relevant “markers”. In the longer term, nationwide connectivity could then provide an additional dimension to the data and its statistical relevance (e.g. as a flu season progresses across a region).

Daniele Faccio ([email protected])



Alex Turpin ([email protected])

Scientific Question: Is it possible to develop an optical neural network that can be both trained optically and used for data and image processing?

Background: Machine learning (ML) and deep learning (DL) algorithms are nowadays used in a wide variety of different scenarios such as autonomous vehicles, healthcare technologies, and computer vision. However, these algorithms require largescale data processing and an increasingly demand for computational resources. The main consequence is the big amount of energy resources used by ML and DL algorithms, which, together with the limitations brought by Moore’s law for electronic computing where the scale of an electronic transistor is already approaching its physical limit encourages the community to look for energy-efficient scalable alternatives.

Research aim: With this project the student will investigate new routes for scalable and energy efficient all-optical neural networks (ONNs) based on optical computing. Optical computing offers low power consumption, processing at the speed of light, and high-throughput capability, which is a basic requirement for high-performance computing. Optics also paves the way naturally to fundamental operations, such as differentiation, multiplication, integration, Fourier transforms, and convolutions, either via linear (e.g. diffractive optics) or non-linear (e.g. sum frequency generation) optical operations. However, all optical computing proposals to date have used algorithms pre-calculated digitally on a computer and then implemented experimentally, which weakens the low power consumption promises of using optics for computing.

Methodology: In contrast to previous approaches, in this project the student will train and implement ML algorithms (specifically neural networks) in experiments using optical elements. The student will use digital holography with spatial light modulators (SLMs) and diffractive optics to tackle this ambitious goal. SLMs consist in arrays of pixels that can modify the phase of the light’s electric field, which allows spatial engineering of a coherent light beam. SLMs will allow to process light-encoded data-sets and to transform them towards analysing and extracting information, in the same way as electronic neural networks do. The combination of complex media, such as multimode optical fibres (MMFs) with SLMs brings the opportunity to build completely tuneable elements that perform optical computations for both classical and quantum light, something we have world-leading expertise on in both our group and QuantIC. Using cameras and other sensing technologies will allow us to measure and update the response of the system in real time when changing the SLMs pixels (weights) of the ONNs, thus providing real-time optical ML, where most of the computations are performed at the speed of light.

Outcome: The main deliverable of the PhD project will be a new technology combining optics and other optoelectronic elements allowing for designing, training, and implementing neural networks in a reconfigurable manner. The idea is to encode data in the light field, pass it through one or more reconfigurable optical elements (such as SLMs), and use diffractive optics and sensing elements to update the SLM pixels, that will act as weights of the ONN. This will allow developing intelligent optical systems performing data-processing and image-processing tasks directly on the hardware.

03 - Towards All-Optical Neural Networks

Daniele Faccio ([email protected])

Rod Murray-Smith ([email protected])


04 - Superlattice Detectors for MWIR Single Photon Detection

In the visible wavelength range, cheap and high performing cameras are widely available. In the mid-infrared (MIR) imaging market however, most available imagers need cryogenic cooling, and uncooled MIR sensing and imaging technologies are of much interest. Type-II superlattice (T2SL) detectors have been proposed as suitable candidates for MIR uncooled detection, and other structures are also under investigation. Using semiconductor material design and growth to realise new MIR imaging and sensing devices the project will advance the state-of-the-art to investigate MWIR single photon detection.

The student will join a team of researchers with expertise in semiconductor device engineering, infrared imaging and sensing, and material design, fabrication, and characterisation. Wafers will be grown by either MBE or MOVPE according to the most suitable methods for the materials in use. Materials will be III-V semi conductors based on but not limited to alloys on Ga, As, In, and Sb, grown using the most appropriate epitaxial method. The student will carry out device fabrication in world-class James Watt Nanofabrication Centre at the University of Glasgow and will characterise the devices that they make in the laboratories of Imperial College or the University of Glasgow to access an excellent range of equipment. The project is multidisciplinary in nature and you will have the opportunity to work with industry and academia. The project will also provide opportunities to publish your research in leading journals and to attend international conferences in the field.

This challenging and exciting project is suitable for a highly motivated UK student with a first class degree in electronic engineering, physics or a related subject. Prior research experience is particularly valued.

David Cumming ([email protected])

Richard Hogg ([email protected])

Chris Phillips ([email protected])


05 - Biphotons for nonlinear imaging (BINI)

Matteo Clerici Doug Paul ([email protected])


We are looking for a talented and passion-driven candidate to fulfil a 3.5-years PhD Scholarship at the University of Glasgow. The ideal candidate is a Physics or Engineering graduate, with 2:1 or higher (or equivalent) degree. The PhD student will work in the UNO (Ultrafast Nonlinear Optics) group, led by Dr Clerici, will have access to state-of-the-art research infrastructures, and will enjoy the active student life of the Glasgow West End.

Nonlinear imaging delivered transformative results to our science and technology. One example is multiphoton microscopy, which is used to study biological structures with 3D resolution. Now, quantum optics may deliver yet another improvement to our ability to look at the microscopic world. With this PhD you will discover how biphoton fields can enhance nonlinear imaging. It has been predicted that the temporal correlations between twin photons generated by parametric downconversion can significantly increase the two-photon absorption cross-section. In the presence of a resonant nonlinearity, such as two-photon ab-sorption in a fluorophore or a semiconductor, biphoton states are absorbed with a cross-section orders of magnitude higher than classical radiation at the same wavelength. This concept is currently being tested experimentally and is one of the most exciting topics in quantum imaging also due to the possibility it entails of improving bioimaging. This PhD project expands on such concept exploring the impact of biphotons on thereal, rather than the imaginary part of optical nonlinearities. The down-converted field is composed only of photon pairs (biphotons) that are strongly correlated in space and time. For this reason, under proper conditions, they effectively behave as a single particle for the light-matter interaction.As a consequence, they can be absorbed with a cross-section approaching that of one-photon processes yet being in a transparent spectral region of the material. The very same concept is expected to hold also for other two-photon processes, such as those underpinning parametric interactions in third-order nonlinear media, such as self and cross-phase modulation, parametric amplification, and Raman scattering. With this PhD project you will investigate the biphotoninduced enhancement of Kerr nonlinearities for nonlinear imaging applications.

Application. The Scholarship covers the student fees for UK residents (see EPSRC definition) andprovides a stipend at the UKRI/EPSRC rate (https://www.ukri.org/skills/funding-for-researchtraining/)for 3.5 years. To apply, please send your CV and a brief personal statement to [email protected]. After a pre-selection, successful applicants will be interviewed (either in person or via conference call).

The Scholarship is available from October 1st, 2020. We encourage you to get in contact with us as soon as possible.

This is an exciting opportunity to develop complementary skills in optics and photonics sponsored by QuantIC, the Quantum Hub for Imaging (https://quantic.ac.uk/). For further information visit the group page at www.glasgow.ac.uk/uno. Do not hesitate to contact us ([email protected]). We are happy to discuss over the email or phone your questions.

01 - microscopy with quantum illumination · 05 - biphotons for nonlinear imaging (bini) matteo...

Documents