An Experimental Microarchitecture for a Superconducting Quantum Processor

Quantum computers promise to solve certain problems that are intractable for classical computers, such as factoring large numbers and simulating quantum systems. To date, research in quantum computer engineering has focused primarily at opposite ends of the required system stack: devising high-level programming languages and compilers to describe and optimize quantum algorithms, and building reliable low-level quantum hardware. Relatively little attention has been given to using the compiler output to fully control the operations on experimental quantum processors. Bridging this gap, we propose and build a prototype of a flexible control microarchitecture supporting quantum-classical mixed code for a superconducting quantum processor. The microarchitecture is based on three core elements: (i) a codeword-based event control scheme, (ii) queue-based precise event timing control, and (iii) a flexible multilevel instruction decoding mechanism for control. We design a set of quantum microinstructions that allows flexible control of quantum operations with precise timing. We demonstrate the microarchitecture and microinstruction set by performing a standard gate-characterization experiment on a transmon qubit.Comment: 13 pages including reference. 9 figure

Evaluation of Mucociliary Clearance by Three Dimension Micro-CT-SPECT in Guinea Pig: Role of Bitter Taste Agonists

Different image techniques have been used to analyze mucociliary clearance (MCC) in humans, but current small animal MCC analysis using in vivo imaging has not been well defined. Bitter taste receptor (T2R) agonists increase ciliary beat frequency (CBF) and cause bronchodilation but their effects in vivo are not well understood. This work analyzes in vivo nasal and bronchial MCC in guinea pig animals using three dimension (3D) micro-CT-SPECT images and evaluates the effect of T2R agonists. Intranasal macroaggreggates of albumin-Technetium 99 metastable (MAA-Tc99m) and lung nebulized Tc99m albumin nanocolloids were used to analyze the effect of T2R agonists on nasal and bronchial MCC respectively, using 3D micro-CT-SPECT in guinea pig. MAA-Tc99m showed a nasal mucociliary transport rate of 0.36 mm/min that was increased in presence of T2R agonist to 0.66 mm/min. Tc99m albumin nanocolloids were homogeneously distributed in the lung of guinea pig and cleared with time-dependence through the bronchi and trachea of guinea pig. T2R agonist increased bronchial MCC of Tc99m albumin nanocolloids. T2R agonists increased CBF in human nasal ciliated cells in vitro and induced bronchodilation in human bronchi ex vivo. In summary, T2R agonists increase MCC in vivo as assessed by 3D micro-CT-SPECT analysis

On Structured Design Space Exploration for Mapping of Quantum Algorithms

Quantum algorithms can be expressed as quantum circuits when the circuit model of computation is adopted. Such a circuit description is usually hardware-agnostic, that is, it does not consider the limitations that the quantum hardware might have. In order to make quantum algorithms executable on quantum devices they need to comply to their constraints, which mainly affect the parallelism of quantum operations and the possible interactions between the qubits. The process of adapting a quantum circuit to meet the quantum chip restrictions is known as mapping. The resulting circuit usually has a higher number of gates and depth, decreasing the algorithm's reliability. Different mapping solutions have been already proposed. Most of them are meant for a specific quantum processor and differ in methodology, approach and features. In addition, they are usually only compared in terms of added gates, circuit depth and compilation time. No thorough comparative analysis of the different mapping solutions performance and features has been performed so far.In this paper, we propose to apply structured design space exploration (DSE) methodologies to the mapping procedures. This will allow not only to have a more in depth and structured analysis of their performance but also to identify what features are key and worth to implement. By using DSE we will be able to: i) determine in what regimes some mapping solutions outperform others; ii) derive optimal mapping strategies for specific quantum algorithms and quantum processors; and iii) perform an scalability analysis. In addition, DSE techniques cannot only be applied to the mapping layer that is key for bridging quantum applications to quantum devices, but also to the full-stack quantum computing system allowing for its crosslayer co-design.</p