Quantum Dot Cellular Automata Check Node Implementation for LDPC Decoders

The quantum dot Cellular Automata (QCA) is an emerging nanotechnology that has gained significant research interest in recent years. Extremely small feature sizes, ultralow power consumption, and high clock frequency make QCA a potentially attractive solution for implementing computing architectures at the nanoscale. To be considered as a suitable CMOS substitute, the QCA technology must be able to implement complex real-time applications with affordable complexity. Low density parity check (LDPC) decoding is one of such applications. The core of LDPC decoding lies in the check node (CN) processing element which executes actual decoding algorithm and contributes toward overall performance and complexity of the LDPC decoder. This study presents a novel QCA architecture for partial parallel, layered LDPC check node. The CN executes Normalized Min Sum decoding algorithm and is flexible to support CN degree dc up to 20. The CN is constructed using a VHDL behavioral model of QCA elementary circuits which provides a hierarchical bottom up approach to evaluate the logical behavior, area, and power dissipation of the whole design. Performance evaluations are reported for the two main implementations of QCA i.e. molecular and magneti

Silicon CMOS architecture for a spin-based quantum computer

Recent advances in quantum error correction (QEC) codes for fault-tolerant quantum computing \cite{Terhal2015} and physical realizations of high-fidelity qubits in a broad range of platforms \cite{Kok2007, Brown2011, Barends2014, Waldherr2014, Dolde2014, Muhonen2014, Veldhorst2014} give promise for the construction of a quantum computer based on millions of interacting qubits. However, the classical-quantum interface remains a nascent field of exploration. Here, we propose an architecture for a silicon-based quantum computer processor based entirely on complementary metal-oxide-semiconductor (CMOS) technology, which is the basis for all modern processor chips. We show how a transistor-based control circuit together with charge-storage electrodes can be used to operate a dense and scalable two-dimensional qubit system. The qubits are defined by the spin states of a single electron confined in a quantum dot, coupled via exchange interactions, controlled using a microwave cavity, and measured via gate-based dispersive readout \cite{Colless2013}. This system, based entirely on available technology and existing components, is compatible with general surface code quantum error correction \cite{Terhal2015}, enabling large-scale universal quantum computation

Hybrid integration methods for on-chip quantum photonics

The goal of integrated quantum photonics is to combine components for the generation, manipulation, and detection of nonclassical light in a phase-stable and efficient platform. Solid-state quantum emitters have recently reached outstanding performance as single-photon sources. In parallel, photonic integrated circuits have been advanced to the point that thousands of components can be controlled on a chip with high efficiency and phase stability. Consequently, researchers are now beginning to combine these leading quantum emitters and photonic integrated circuit platforms to realize the best properties of each technology. In this paper, we review recent advances in integrated quantum photonics based on such hybrid systems. Although hybrid integration solves many limitations of individual platforms, it also introduces new challenges that arise from interfacing different materials. We review various issues in solid-state quantum emitters and photonic integrated circuits, the hybrid integration techniques that bridge these two systems, and methods for chip-based manipulation of photons and emitters. Finally, we discuss the remaining challenges and future prospects of on-chip quantum photonics with integrated quantum emitters. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

Magnetic Cellular Nonlinear Network with Spin Wave Bus for Image Processing

We describe and analyze a cellular nonlinear network based on magnetic nanostructures for image processing. The network consists of magneto-electric cells integrated onto a common ferromagnetic film - spin wave bus. The magneto-electric cell is an artificial two-phase multiferroic structure comprising piezoelectric and ferromagnetic materials. A bit of information is assigned to the cell's magnetic polarization, which can be controlled by the applied voltage. The information exchange among the cells is via the spin waves propagating in the spin wave bus. Each cell changes its state as a combined effect of two: the magneto-electric coupling and the interaction with the spin waves. The distinct feature of the network with spin wave bus is the ability to control the inter-cell communication by an external global parameter - magnetic field. The latter makes possible to realize different image processing functions on the same template without rewiring or reconfiguration. We present the results of numerical simulations illustrating image filtering, erosion, dilation, horizontal and vertical line detection, inversion and edge detection accomplished on one template by the proper choice of the strength and direction of the external magnetic field. We also present numerical assets on the major network parameters such as cell density, power dissipation and functional throughput, and compare them with the parameters projected for other nano-architectures such as CMOL-CrossNet, Quantum Dot Cellular Automata, and Quantum Dot Image Processor. Potentially, the utilization of spin waves phenomena at the nanometer scale may provide a route to low-power consuming and functional logic circuits for special task data processing

Neuromorphic, Digital and Quantum Computation with Memory Circuit Elements

Memory effects are ubiquitous in nature and the class of memory circuit elements - which includes memristors, memcapacitors and meminductors - shows great potential to understand and simulate the associated fundamental physical processes. Here, we show that such elements can also be used in electronic schemes mimicking biologically-inspired computer architectures, performing digital logic and arithmetic operations, and can expand the capabilities of certain quantum computation schemes. In particular, we will discuss few examples where the concept of memory elements is relevant to the realization of associative memory in neuronal circuits, spike-timing-dependent plasticity of synapses, digital and field-programmable quantum computing