Layered architecture for quantum computing

We develop a layered quantum computer architecture, which is a systematic framework for tackling the individual challenges of developing a quantum computer while constructing a cohesive device design. We discuss many of the prominent techniques for implementing circuit-model quantum computing and introduce several new methods, with an emphasis on employing surface code quantum error correction. In doing so, we propose a new quantum computer architecture based on optical control of quantum dots. The timescales of physical hardware operations and logical, error-corrected quantum gates differ by several orders of magnitude. By dividing functionality into layers, we can design and analyze subsystems independently, demonstrating the value of our layered architectural approach. Using this concrete hardware platform, we provide resource analysis for executing fault-tolerant quantum algorithms for integer factoring and quantum simulation, finding that the quantum dot architecture we study could solve such problems on the timescale of days.Comment: 27 pages, 20 figure

Microwave Photonic Signal Processing Using On-Chip Nonlinear Optics

The field of microwave photonics (MWP) emerged as a solution to the challenges faced by electronic systems when dealing with high-bandwidth RF and microwave signals. Photonic devices are capable of handling immense bandwidths thanks to the properties of light. MWP therefore employs such devices to process and distribute the information carried by RF and microwave signals, enabling significantly higher capacity compared to conventional electronics. The photonic devices traditionally used in MWP circuits have mainly comprised bulky components, such as spools of fibre and benchtop optical amplifiers. While achieving impressive performance, these systems were not capable of competing with electronics in terms of size and portability. More recently, research has focused on the application of photonic chip technology to the field of MWP in order to reap the benefits of integration, such as reductions in size, weight, cost, and power consumption. Integrated MWP however is still in its infancy, and ongoing research efforts are exploring new ways to match integrated photonic devices to the unique requirements of MWP circuits. This work investigates the application of on-chip nonlinear optical interactions to MWP. Nonlinear optics enables light-on-light interactions (not normally possible in a linear regime) which open a vast array of powerful functionalities. In particular, this thesis focuses on stimulated Brillouin scattering, resulting from the interaction of light with hypersonic sound waves, and four-wave mixing, where photons exchange energies. These two nonlinear effects are applied to implement MWP ultra-high suppression notch filters, wideband phase shifters, and ultra-fast instantaneous frequency measurement systems. Experimental demonstrations using integrated optical waveguides confirm record results

Commercial air transport hazard warning and avoidance system. Volume 2 - Requirements studies Final report

Operational requirements and cost effectiveness of commercial air transport hazard warning and avoidance syste

Space/time/frequency methods in adaptive radar

Radar systems may be processed with various space, time and frequency techniques. Advanced radar systems are required to detect targets in the presence of jamming and clutter. This work studies the application of two types of radar systems. It is well known that targets moving along-track within a Synthetic Aperture Radar field of view are imaged as defocused objects. The SAR stripmap mode is tuned to stationary ground targets and the mismatch between the SAR processing parameters and the target motion parameters causes the energy to spill over to adjacent image pixels, thus hindering target feature extraction and reducing the probability of detection. The problem can be remedied by generating the image using a filter matched to the actual target motion parameters, effectively focusing the SAR image on the target. For a fixed rate of motion the target velocity can be estimated from the slope of the Doppler frequency characteristic. The problem is similar to the classical problem of estimating the instantaneous frequency of a linear FM signal (chirp). The Wigner-Ville distribution, the Gabor expansion, the Short-Time Fourier transform and the Continuous Wavelet Transform are compared with respect to their performance in noisy SAR data to estimate the instantaneous Doppler frequency of range compressed SAR data. It is shown that these methods exhibit sharp signal-to-noise threshold effects. The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank methods outperform full-rank space-time adaptive processing when the space-time covariance matrix is estimated from a dataset with limited support. The utility of reduced-rank methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that reduced-rank processing has two effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio. A method for evaluating the theoretical conditioned SNR for fixed reduced-rank transforms is also presented