The goal of this thesis is the design and development of the QuTech Central Controller, a system conceived to serve as the hardware/software interface of a quantum computer. This system represents an evolution of the QuMA microarchitecture to control a Surface-17 superconducting quantum processor, even though several architectural mechanisms are used to ensure the compatibility of the design with different quantum hardware technologies. In addition to an expansion of the control microarchitecture, the QuTech Central Controller represents an evolution of the overall system architecture, making use of a different hardware infrastructure to overcome previous scalability limitations. The main contributions of this thesis are a proposed centralized microarchitecture capable of controlling up to 17 qubits, the implementation of this microarchitecture in a device called the QuTech Central Controller and its testing in dynamic quantum information processing experiments with superconducting qubits.

Unitary Decomposition is an algorithm for translating a unitary matrix into many small unitary matrices, which correspond to a circuit that can be executed on a quantum computer. It is implemented in the quantum programming framework of the QCA-group at TU Delft: OpenQL, a library for Python and C++. Unitary Decomposition is a necessary part in Quantum Associative Memory, an algorithm used in Quantum Genome Sequencing. The implementation is faster than other known implementations, and generates $3*2^{n-1}*(2^n-1)$ rotation gates for an n-qubit input gate. This is not the least-known nor the theoretical minimum amount, and there are some optimizations that can still be done to make it closer to these numbers.

Fast sequencing and analysis of (microorganism, plant or human) genomes will open up new vistas in fields like personalised medication, food yield and epigenetic research. Current state-of-the-art DNA pattern matching techniques use heuristic algorithms on computing clusters of CPUs, GPUs and FPGAs. With genomic data set to eclipse social and astronomical big data streams within a decade, the alternate computing paradigm of quantum computation is explored to accelerate genome-sequence reconstruction. The inherent parallelism of quantum superposition of states is harnessed to design a quantum kernel for accelerating the search process. The project explores the merger of these two domains and identifies ways to fit these together to design a genome-sequence analysis pipeline with quantum algorithmic speedup. The design of a genome-sequence analysis pipeline with a quantum kernel is tested with a proof-of-concept demonstration using a quantum simulator.

Quantum algorithms can be described by quantum circuits which consist of quantum bits (qubits) and quantum gates. Such a circuit description assumes that any kind of interaction between qubits is possible. However, quantum chips have limited qubits connectivity only allowing, for instance, nearest-neighbor (NN) interactions. That means, qubits need to be placed in adjacent positions for performing a two-qubit gate. In this thesis, a routing algorithm is proposed where physical qubits or planar-based logical qubits are routed to obey this nearest neighbor constraint in a 2D qubit topology. This algorithm tries to minimize the circuit latency or communication overhead. The proposed routing algorithm is based on a sliding window principle. Different paths, found by using an adapted breadth-first search algorithm, are evaluated based on the interleaving of the corresponding routing instructions, e.g., SWAP operations with previous instructions, and by looking at the disordering of future qubits. The path that will add the lowest number of cycles to the algorithm is then selected and efficiently inserted with the rest of the instructions. This process continues until all the instructions inside the quantum algorithm obey the nearest neighbor constraints. The routing algorithm is tested for several real quantum algorithms taken from QLib and ScaffCC, as well as for random generated benchmarks. Taking different alternative paths into account and evaluating those paths for possible interleaving with previous instructions, always has a positive effect to minimize the number of added cycles. The results concerning the evaluation of the disordering of future qubits could have a positive or negative effect on the circuit latency depending on the quantum circuit.

The performance of supercomputers is not growing anymore at the rate it once used to. Several years ago a break with historical trends appeared. First the break appeared at the lower end of worldwide supercomputer installations, but now it affects a significant number of systems with average performance. Power consumption is becoming the most significant problem in computer system design. The traditional power reduction trends do not apply any more for the current semiconductor technology, and the performance of general-purpose devices is limited by their power consumption. Server and system design is in turn limited by their allowable power consumption, which is bounded for reasons of cost and practical cooling methods. To further increase performance, the use of specialized devices, in specialized server designs, optimized for a certain class of workloads, is gaining momentum. Data movement has been demonstrated to be a significant drain of energy, and is furthermore a performance bottleneck when data is moved over an interconnect with limited bandwidth. With data becoming an increasingly important asset for governments, companies, and individuals, the development of systems optimized on a device and server level for data-intensive workloads, is necessary. In this work, we explore some of the fundamentals required for such a system, as well as key use-cases...@en

Quantum computers can accelerate solving some problems which are inefficiently solved by classical computers, such as quantum chemistry simulation. To date, 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 current experimental quantum processors. Bridging this gap, we propose and build a prototype of flexible control microarchitecture, named QuMA, 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.@en

Efforts to realize a sufficiently large controllable quantum processor are actively being pursued globally. These quantum devices are programmed by specifying the manipulation of quantum information via quantum algorithms. This doctoral research provides an application perspective to the design requirements of a quantum accelerator architecture. Early quantum algorithms focused specifically on the theoretical study of the benefits in computational resource cost by harnessing quantum phenomena. With small-scale quantum processors being available, the focus is now towards applying quantum algorithms to develop applications with high impact in societal, industrial, and scientific fields. No quantum devices exist that can execute quantum algorithms that can demonstrate a provable advantage for a real world use case. However, a proof of concept application pipeline can be demonstrated on a simulator framework. The research question of this dissertation is to identify high-impact long-term applications of quantum computation and formulate the corresponding logic. Three specific use cases are studied. Use case 1 involves `quantum-accelerated genome sequence reconstruction'. Faster sequencing pipeline would enable novel downstream applications like personalized medical treatment. Two different reconstruction methods, ab initio reference alignment, and de novo read assembly, are studied to identify the computational bottleneck. Corresponding quantum techniques are formulated, based on quantum search and heuristic quantum optimization, respectively. A new algorithm, quantum indexed bidirectional associative memory (QiBAM), is explicitly designed to address the requirements for approximate alignment of DNA sequences. We also proposed the quantum accelerated sequence reconstruction (QuASeR) strategy to perform de novo assembly. This is formulated as a QUBO and solved using QAOA on a gate-model simulator, as well as, on a quantum annealer. Use case 2 involves `quantum automata for algorithmic information'. A framework for causal inference based on algorithmic generative models is developed. This technique of quantum-accelerated experimental algorithmic information theory (QEAIT) can be ubiquitously applied to diverse domains. Specifically for genome analysis, the problem of identifying bit strings capable of self-replication is presented. We developed a new quantum circuit design of a quantum parallel universal linear bounded automata (QPULBA) model. This enables a superposition of classical models/programs to be executed, and their properties can be explored. The automaton prepares the universal distribution as a quantum superposition state which can be queried to estimate algorithmic properties of the causal model. Use case 3 involves `universal reinforcement learning in quantum environments'. This theoretical framework can be applied for automated scientific modeling. A universal artificial general intelligence formalism is presented that can model quantum processes. The developed quantum knowledge seeking agent (QKSA) is an evolutionary general reinforcement learning model for recursive self-improvement. It uses resource-bounded algorithmic complexity of quantum process tomography algorithms. The cost function determining the optimal strategy is implemented as a mutating gene within a quine. The utility function for an individual agent is based on a selected quantum distance measure between the predicted and perceived environment. This dissertation researches foundational techniques and develops innovative applications of quantum computation and algorithmic information. These were applied specifically for causal modeling in genomics and reinforcement learning. Further exploration of the synergies among these interdisciplinary concepts would improve our understanding of various scientific disciplines like computation, intelligence, life, and cosmology.@en

The availability of inexpensive and powerful processors provides the means for the computation ecosystem to change its fundamental paradigm towards the Internet of Things (IoT) where ubiquitous nanosystems add intelligence to every object that surrounds us. The new trend for most of those systems is to autonomously operate into a “zero-power” regime, i.e., manage their energy budget in such a way that they can provide the required functionality without any service until they become obsolete. Considering that these systems are most of the time inactive, the static power is the dominant power consumption component, thus the most effective way to fulfill the “zero-power” operation requirement is to diminish the energy consumption into the so called sleep/idle mode. The semiconductor community has been addressing the static power reduction issue at device level, but for the CMOS technology the effectiveness of such approach is limited by the interdependence between static power consumption and device performance. In view of this observation this thesis focuses on improving the energy efficiency of electronic products, battery-powered, and autonomous ones by making use of emerging leakage proof technologies in conjunction with the versatile CMOS counterpart. First, we performed a design space exploration to identify the most promising NEMFET geometries and to evaluate their potential performance in terms of switching delay, current capability, and leakage. Moreover we compared those parameters of interest with the ones offered by traditional transistors utilized in up to date CMOS technologies. Second, we assessed the NEMFET potential when utilized as sleep transistor in circuits featuring 2D cell based power gating, and find out if NEMFETs constitute a viable alternative to High - VTH FETs in sleep mode circuits. Furthermore, we proposed a novel 3D power management approach that attempts to alleviate issues associated with the NEMS utilization as sleep transistor in CMOS power gated integrated circuits. Given the two designs, we evaluated the 2D and 3D NEMFET based power management implementations energy efficiency when embedded into a computation platform executing a bio-medical sensing application. Third, we introduced a NEMFET based logic family tailored to the implementation of ultra-low energy functional units and processors. Fourth, we proposed a memory cell that relies on a NEMFET based inverter designed in such a way that no short circuit current can occur. Finally, we proposed and evaluated the “zero-energy” operation scenario potential of an improved version of the 3D-Stacked NEMS based power management architecture.@en

Developments in sequencing technology have drastically reduced the cost of DNA sequencing. The raw sequencing data being generated requires processing through computationally demanding suites of bioinformatics algorithms called genomics pipelines. The greatly decreased cost of sequencing has resulted in its widespread adoption, and the amount of data that is being generated is increasing exponentially, projected to soon rival big data fields such as astronomy. Therefore, acceleration and optimization of such genomics pipelines is becoming increasingly important. The BWA-MEM genomic mapping algorithm is a critical first step of many genomics pipelines, as it maps the raw input sequences onto a reference genome, thereby reconstructing the sample's original genetic assembly. A major part of overall BWA-MEM execution time is spent performing Seed Extension, an algorithm closely related to the Smith-Waterman pairwise sequence alignment algorithm. The standard approach for the heterogeneous acceleration of the Smith-Waterman algorithm is to map it onto a systolic array architecture to compute elements of the similarity matrix in parallel. In order for systolic arrays to operate at high efficiency, they require long sequences to be aligned to one another. The BWA-MEM algorithm, in contrast, typically generates very short sequences that then require pairwise alignment through the Seed Extension algorithm. Therefore, in this dissertation, various techniques to improve the efficiency of systolic arrays for short sequence lengths are proposed. The Variable Logical Length, the Variable Physical Length, and the Variable Logical and Physical Length systolic array architectures are proposed to eliminate the dependence of systolic array efficiency on read sequence length. To eliminate its dependence on reference sequence length, a streaming, implicit synchronizing architecture is introduced. Together, these techniques result in a maximally-efficient systolic array. A Seed Extension kernel has been implemented on both FPGA and GPU with a threefold kernel-level improvement to execution time, resulting in the first FPGA-accelerated and the first GPU-accelerated implementation of BWA-MEM with an overall end-to-end twofold application-level speedup. Moreover, a Smith-Waterman implementation has been developed on FPGA using the above efficiency improvements to the systolic array architecture, resulting in an implementation that has a performance of 214 GCUPS and that is able to attain 99.8% efficiency, which is the highest reported efficiency and performance of any FPGA-accelerated Smith-Waterman implementation to date. Finally, various aspects of these designs are evaluated, including power-efficiency and design-time.@en