Quantum computers have huge promise to solve problems which lie beyond the capabilities of even the world's fastest conventional computer using quantum effects to compute in a fundamentally new way. However, quantum effects are fragile. Quantum systems are vulnerable to noise and error due to interactions with the world around them, and this noise and error tends to render a quantum computer no more powerful, at best, than a conventional classical computer. While progress in engineering prototype quantum computers to reduce this error is impressive, and clever algorithmic tricks are being developed to help minimise its effects, to run the most valuable large-scale computations on a quantum computer, the noise needs to be removed almost entirely. This can be done using the techniques of quantum error correction, where quantum data is stored in an error correcting code. The leading quantum error correcting code is called the surface code - a particularly simple code with a repeated regular structure which can be realised on a two-dimensional surface. The surface code is now very well studied, and the full details of how it can be used to store data, correct errors and implement logical gates are well studied. It is the leading approach to large scale quantum computation and most industrial roadmaps for building such a device are based on it. However, the surface code has a key disadvantage. It is a highly inefficient way of storing information, and very large numbers of quantum bits (qubits) are required to use it for even a relatively modest computation. For every quantum bit used for computation, thousands or more are needed for the error correction. There has thus been an ongoing search, over the last 25 years since the surface code was discovered, to find more efficient codes, which nevertheless share some of the practical advantages of the surface code. Potential candidates for such codes have very recently been discovered. They are in a family of codes known as Quantum Low-Density Parity Check codes (or QLPDC codes). Classical LDPC codes are widely used due to their efficient encoding and useful properties, for example in the error correction used in 5G mobile networks. The quantum analogues of these have been studied for nearly 20 years, but only in 2022 was a quantum code discovered which is as efficient in data storage as the best classical codes. This code, and codes like it, promise to revolutionise the path to large-scale fault tolerant quantum computation, dramatically reducing the number of quantum bits, and therefore hastening the development of large-scale quantum computers. To realise this promise, however, much work needs to be done. Unlike the simple structure of the surface code, these new highly efficient codes are very complicated. It is therefore far from clear whether their benefits can be realised in practical hardware. Furthermore, little is known about the best way to use such codes for computation. This aim of this project is to fully assess the feasibility of novel QLDPC codes for large-scale quantum computation. We shall do this by designing detailed models of the measurements which detect errors, and discover new ways to realise quantum gates on these codes. The research will be facilitated by the design of software which will translate the complicated abstract descriptions of the codes into a specification of the building blocks (gates and measurements) which will be realised on the device. We will determine the resources required, in terms of number of qubits and gates, to achieve a set of benchmark computations provided by our project partners, and use these to enable to a fair practical comparison between the resources required to construct a useful quantum computer based on these novel codes compared to the standard surface code.

Digital signal processing is a powerful technique for storing, analysing and manipulating digital signals. Ultimately, the quality of the signal to be processed is determined by the performance of the analogue-to-digital converter (ADC) which is used to sample the original analogue signal in the first place and produce a digital representation of it. Electronic ADCs are embedded ubiquitously in numerous everyday items, such as mobile phones, digital thermometers and computer mice to name a few. As the speed of electronic ADCs continues to increase, more and more sophisticated applications including medical imaging and cognitive radar can benefit from the use of ADCs and digital signal processing. Photonics has been used to increase the performance of electronic ADCs since the 1970s, forming what is now generally termed the photonic ADC. Most photonic ADCs with sampling rates as high as 1 THz (1,000,000,000,000 Hz) have invariably employed mode-locked lasers as they can produce very high power optical pulses with very short pulse widths and low jitters, both in the femto second region. Such ultra-short and stable optical pulses are ideal for sampling microwave and millimetre-wave signals at a sampling rate which is beyond what is achievable using conventional electronic ADCs. However, most mode-locked laser sources are bulky, expensive and require constant stability adjustments. Therefore they have not found widespread commercial application to date. Furthermore, the repetition rates of most mode-locked laser pulse sources cannot be readily adjusted and as a result, the sampling rates of photonic ADCs using such sources are fixed and cannot be varied to suit the input signal frequency and bandwidth. In this application, we seek support to investigate a new, high-performance photonic sampling technique based on an optical comb generator instead of the traditional mode-locked lasers. In this novel approach, continuous sampling at flexible sampling frequencies are possible, unlike the mode-locked laser approach. We have also calculated that the combined jitter level due to the linewidth of a typical DFB laser and the phase noise of a mm-wave generator to be used in this technique is less than 5 fs (RMS) and the corresponding effective number of bits (ENOB) of resolution is 10 which is superior to the state-of-the-art CMOS electronic ADC and the all-optical ADC at the same 40 GHz sampling frequency. Such high-performance photonic sampling technique is expected to attract wide attention from both the research community and the industry.

Protection of homeland territory, offshore and overseas assets and related national economic and political interests are strategically important priorities for the UK and the world community. Worldwide economical and political crisis over the last few years has deepened this challenge and the UK witnesses the consequences of it, resulting in increased illegal immigration entries, piracy, and threats to commercial and national assets. Technological advances become quickly available to criminals so that flexibility of contra-measures, including development of deployable sensor networks, re-use of existing communication technologies with multi-mode operation, advanced signal processing is required to tackle the modern challenges. This requires targeted R&D of high-performance cost-effective electronic security (ESS) systems, including practical implementation and development of efficient digital signal processing algorithms. ESS is one of the world's largest (and growing) markets worth about $62 bn a year with UK companies fundamentally involved at the hi-tech end of this industry. An essential segment of the ESS market relates to perimeter/border protection solutions to provide situational awareness and, importantly, real-time recognition and identification of intruders, based on reliable all weather, day and night operation in complex environmental conditions. There is no single solution, so that general approach is to use all technologies and systems available, which can complement each other by providing additional information or data fusion. Widely used for surveillance, electro-optical or mm wave real time imaging systems are not efficient in the absence of line-of-sight and poor transparency of propagation media: walls, foliage, fog, smoke, snow, etc. In contrast, relatively low frequency radio signals penetrate such obstacles and this is the reason why all long-range surveillance and security missions are entrusted to radars. In traditional radar which process the reflections from the target, a target is viewed as a set of bright points, scintillating in amplitude and changing position with aspect angle, as it is composed of many scatterers. Thus even in high performance radar, automatic target recognition remains the most difficult task. At the same time the value of virtually all wide area surveillance radar is substantially reduced by the absence of reliable target classification functionality. This project addresses an important application area - that of low observable or, so-called 'difficult' target imaging in low-cost deployable radio frequency (RF) forward scatter (FS) perimeter protection radar networks. This radar has already proven its excellent detection and target parameter estimation ability. The highly sought-after recognition capability for such a radar network will be provided by combining, for the first time, the Target Shadow Profile Reconstruction (TSPR) technique with MIMO approaches. The novel imaging approach will be based on accurate solution of inverse diffraction problem to reconstruct the target silhouette by a network of distributed RF sensors, configured as a multi-tier chain of RF transmitters and receivers. Each pair of separated transmitter and receiver forms a section of an 'electronic fence', so that each crossing of the baseline is registered and processed in real time. A multi-tier configuration will provide crossings of multiple baselines by the same target allowing multi-perspective images, so that non-coherent MIMO will be exploited for enhanced imaging capability. Coherent synchronized virtual MIMO array will be also investigated on its ability to form an improved multi-perspective target shape outline. The reconstructed target profiles will be a base for the automatic target recognition (ATR).The introduction of target imaging by FS sensors will facilitate implementation of the fully functional radar system for perimeter protection and surveillance.

Hybrid autonomous systems are those where groups of people are in direct, ongoing interaction with groups of autonomous robots or autonomous software. One prominent current example involves rush-hour traffic made up of a mixture of cars driven by people and cars driven by smart algorithms. However, emerging technologies in robotics, AI and ICT mean that hybrid autonomous systems of this kind will become increasingly common in a much wider set of situations: Emerging technologies in robotics, AI and ICT mean that hybrid autonomous systems of this kind will become increasingly common in a much wider set of situations: - a mixture of autonomous and human-operated drones making deliveries or monitoring public spaces; - a mixture of human traders and autonomous trading agents buying and selling stocks; - a mixture of autonomous and human-operated trains and trams providing efficient, integrated public transport; - autonomous systems assisting with search and rescue missions in disaster areas that are difficult or dangerous to access; - robot carers assisting care workers with the provision of social care in the home In each of these cases smooth, reliable, safe interaction amongst machines and people will be key to success. But how can we guarantee that self-driving cars won't cause a crash or gridlock? How can we understand how autonomous systems will respond to new situations (both acute shocks and long-term gradual changes in their environment), or changes in the way that people interact with them? Consequently, as we enter this new design space, a crucial challenge for the engineers of hybrid autonomous systems across all of these settings is ensuring that the system behaviour is Robust and Resilient and that it meets Regulatory demands: the R3 Challenge. T-B PHASE directly addresses this R3 Challenge for Hybrid Autonomous Systems Engineering, by bringing together expertise in robotics, AI, and systems engineering at the University of Bristol and Thales in a five-year project that targets fundamental autonomous system design problems in the context of three real-world Thales use cases: Hybrid Low-Level Flight, Hybrid Rail Systems, and Hybrid Search & Rescue. Bristol and Thales have a long-standing track record of research collaboration, and by jointly pursuing fundamental research questions in the context of highly practical design problems, alongside a programme of engagement with industry, the public and regulatory bodies, T-B PHASE will significantly advance our capability to operate confidently in one of the most important emerging areas for modern engineering.

Thales are the leading company in Europe for high performance long wavelength infra-red (LWIR) imagers. Thales has been developing thermal imagers for more than 40 years, and is currently working on a unique polarimetric thermal imaging camera concept - the Polarimetric Catherine MP. Thermal imagers provide day and night imaging capability with good object discrimination (for example, telling the difference between animals and vehicles). Further development work has been identified to progress the current camera capabilities. This work includes advanced signal, data and image processing development, some of which are already underway in house. The proposed project is integral part of this effort as it will address fundamental issues about the operation and performance of the detector, as well as investigating a novel approach to utilising the camera data (thermal and polarisation imagery) for deployment as part of a multi-modal imaging system. This will be achieved primarily through the application of existing expertise in Bayesian inference, imaging and polarisation in STFC-funded research groups (Astronmy and Institute of Gravitational Research) at the University of Glasgow. Algorithms will be developed with an aim to diagnosing and improving flat-fielding and polarimetric contrast. These algorithms will be tested using simulated data and test data acquired through experimentation and test field imaging. This project will coordinate and support in-house R&D of Thales polarimetric imagers and help the company gain a better understanding at all levels of this technology and maximise its application in different markets