Abdulkadir Akin

Deterministic quantum state transfer and remote entanglement using microwave photons

Sharing information coherently between nodes of a quantum network is fundamental to distributed quantum information processing. In this scheme, the computation is divided into subroutines and performed on several smaller quantum registers that are connected by classical and quantum channels1. A direct quantum channel, which connects nodes deterministically rather than probabilistically, achieves larger entanglement rates between nodes and is advantageous for distributed fault-tolerant quantum computation2. Here we implement deterministic state-transfer and entanglement protocols between two superconducting qubits fabricated on separate chips. Superconducting circuits3 constitute a universal quantum node4 that is capable of sending, receiving, storing and processing quantum information5–8. Our implementation is based on an all-microwave cavity-assisted Raman process9, which entangles or transfers the qubit state of a transmon-type artificial atom10 with a time-symmetric itinerant single photon. We transfer qubit states by absorbing these itinerant photons at the receiving node, with a probability of 98.1u2009±u20090.1 per cent, achieving a transfer-process fidelity of 80.02u2009±u20090.07 per cent for a protocol duration of only 180 nanoseconds. We also prepare remote entanglement on demand with a fidelity as high as 78.9u2009±u20090.1 per cent at a rate of 50 kilohertz. Our results are in excellent agreement with numerical simulations based on a master-equation description of the system. This deterministic protocol has the potential to be used for quantum computing distributed across different nodes of a cryogenic network.Deterministic quantum state transfer and entanglement generation is demonstrated between superconducting qubits on distant chips using single photons.

Low-Latency Digital Signal Processing for Feedback and Feedforward in Quantum Computing and Communication

Feedback is a main component of many algorithms for quantum computing and communication. A key requirement for any quantum feedback scheme is that the

State-of-the-Art Multi-Camera Systems

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Omnidirectional Multi-Camera Systems Design

of the feedback loop (

Computational Imaging Applications

i.e.

Miniaturization of Multi-Camera Systems

the time between beginning to measure a state and the end of feedback action on the state) must be significantly shorter than the coherence time of the system. In this work a superconducting qubit is initialized in its ground state by active feedback, using a field-programmable gate array (FPGA) with very short latency. This in-depth discussion of the FPGA-based processing unit provides a useful reference for future development of feedback electronics for quantum systems.

Panorama Construction Algorithms

This chapter presents both pioneering and state-of-the-art algorithms and systems for acquisition of images suitable for wide FOV imaging. The most common systems include translational and rotational single camera systems, catadioptric cameras, multi-camera systems, and finally, commercially available standard and light-field cameras. We also introduce the systems mimicking insect eye, and how camera systems can be used to estimate depth of the scene.

Towards Real-Time Gigapixel Video

This chapter explains the Panoptic camera, a real-time omnidirectional multi-camera system. The system is composed of a custom printed circuit board (PCB), with the full FPGA-based processing system on it. We explain the full processing pipeline, starting with the motivation and the system constraints for building such a system. It is followed by the system architecture and block-by-block implementation details. We finish with discussion of the experimental results.

Real-Time Image Registration via Optical Flow Calculation

The previous part of the book introduced five multi-camera systems, their design, and FPGA implementation for real-time omnidirectional video construction in both low and high resolutions. This chapter will present several developed applications of multi-camera systems. The first application is the HDR imaging implemented on a platform Panoptic Media Platform, described in Chap. 6, but it is easily portable to any other multi-camera system with large FOV overlap between the cameras. The other applications are based on depth estimation hardware and include hardware-based free-view synthesis, as well as multiple real-time software applications that use FPGA-generated depth map. The implemented applications conceptually prove that the high-quality and high-performance RGB+D outputs of the proposed real-time disparity estimation hardware can be used for enhanced 3D-based video processing applications.

Binocular and Trinocular Disparity Estimation

In this chapter, we present methods for creating and developing miniaturized high definition vision systems inspired by insect eyes. Our approach is based on modeling biological systems with off-the-shelf miniaturized cameras combined with digital circuit design for real-time image processing. We built a 5 mm radius hemispherical compound eye, imaging a 180∘× 180∘ field of view while providing more than 1.1 megapixels (emulated ommatidias) as real-time video with an inter-ommatidial angle Δϕ = 0. 5∘ at 18 mm radial distance. We made an FPGA implementation of the image processing system which is capable of generating 25 fps video with 1080 × 1080 pixel resolution at a 120 MHz processing clock frequency. When compared to similar size insect eye mimicking systems in literature, the system described in this chapter features 1000× resolution increase. To the best of our knowledge, this is the first time that a compound eye with built-in illumination idea is reported. We are offering our miniaturized imaging system for endoscopic applications like colonoscopy or laparoscopic surgery where there is a need for large field of view high definition imagery. For that purpose we tested our system inside a human colon model. We also present the resulting images and videos from the human colon model in this chapter.