This dissertation focuses on the unique challenges of designing scalable spin-qubit quantum processor architectures. Spin qubits are distinguished by their small size and compatibility with established semiconductor technology, positioning them as a promising platform for scalable quantum computing. However, the field faces significant hurdles, including interconnect bottlenecks, limited qubit counts, and operational constraints, which must be dealt with to unlock its full potential.

To address these challenges, we introduce the SpinQ framework, the first native compilation and design-space exploration tool specifically tailored to the distinct physical constraints and scalability characteristics of spin-qubit quantum processor architectures. SpinQ enables comprehensive performance evaluation of key sources of compilation overhead, provides architecture-specific optimization insights, and estimates hardware performance when mapping quantum circuits onto spin-qubit devices.

A key contribution of this dissertation is the introduction of several novel compilation techniques, which enable all the backend capabilities of SpinQ. One of them is the beSnake routing algorithm. It efficiently handles complex routing scenarios involving the movement of qubits through shuttles and SWAPs in spin-qubit processors. It can adapt seamlessly to various qubit connectivities while being noise-aware and capable of dynamically adjusting the level of routing optimization to meet specific user requirements. By fully utilizing the freedom offered by qubit shuttling operations in spin-qubit topologies, beSnake acts as an essential tool in efficiently mapping large-scale quantum circuits of up to 1,000 qubits.

This work also includes a comprehensive evaluation of spin-qubit architectures through Design Space Exploration (DSE), leveraging a custom automation tool known as ArtA (Artificial Architect) integrated within SpinQ. The reported exploration encompasses nearly 30,000 potential spin-qubit designs. As an overall system performance metric, an enhanced Estimated Success Probability (ESP) formula is proposed, capturing operational errors, crosstalk, and decoherence-induced effects. This augmented metric enables the identification of optimal design parameter choices and the trade-offs among them, ultimately providing crucial insights into architectural decisions that can improve the performance of arbitrary quantum algorithms.

Additionally, the dissertation introduces a novel benchmark method by utilizing SpinQ based on multipartite maximally-entangled states (MME), directly linking the entanglement capabilities of spin-qubit devices to their potential for quantum error correction. These benchmarks provide a more in-depth and nuanced evaluation of device performance compared to conventional ways. Notably, the findings reveal that smaller and less connected layouts, when paired with effective compilation techniques, can achieve performance values comparable to those of more complex architectures. These results highlight the importance of co-designing hardware and software to unlock the full potential of spin-qubit quantum processors. ... Read more

In the fast-paced field of quantum computing, identifying the architectural characteristics that will enable quantum processors to achieve high performance across a diverse range of quantum algorithms continues to pose a significant challenge. Given the extensive and costly nature of experimentally testing different designs, this paper introduces the first Design Space Exploration (DSE) for quantum-dot spin-qubit architectures. Utilizing the upgraded SpinQ compilation framework, this study explores a substantial design space comprising 29,312 spin-qubit-based architectures and applies an innovative optimization tool, ArtA (Artificial Architect), to speed up the design space traversal. ArtA can leverage 17 optimization configurations, significantly reducing exploration times by up to 99.1% compared to a traditional brute force approach while maintaining the same result quality. After a comprehensive evaluation of best-matching optimization configurations per quantum circuit, ArtA suggests specific as well as universal architectural features that provide optimal performance across the examined circuits. Our work demonstrates that combining DSE methodologies with optimization algorithms can be effectively used to generate meaningful design insights for quantum processor development. ... Read more

The design and benchmarking of quantum computer architectures traditionally rely on practical hardware restrictions, such as gate fidelities, control, and cooling. At the theoretical and software levels, numerous approaches have been proposed for benchmarking quantum devices, ranging from, inter alia, quantum volume to randomized benchmarking. In this work, we utilize the quantum information-theoretic properties of multipartite maximally entangled quantum states, in addition to their correspondence with quantum error-correction codes, permitting us to quantify the entanglement generated on near-term bilinear spin-qubit architectures. For this aim, we introduce four metrics that ascertain the quality of genuine multipartite quantum entanglement, along with circuit-level fidelity measures. As part of the task of executing a quantum circuit on a device, we devise simulations, which combine expected hardware characteristics of spin-qubit devices with appropriate compilation techniques; we then analyze three different architectural choices of varying lattice sizes for bilinear arrays, under three increasingly realistic noise models. We find that if the use of a compiler is assumed, sparsely connected spin-qubit lattices can approach comparable values of our metrics to those of the most highly connected device architecture. Even more surprisingly, by incorporating crosstalk into our last noise model, we find that, as error rates for crosstalk approach realistic values, the benefits of utilizing a bilinear array with advanced connectivity vanish. Our results highlight the limitations of adding local connectivity to near-term spin-qubit devices, and can be readily adapted to other qubit technologies. The framework developed here can be used for analyzing quantum entanglement on a device before fabrication, informing experimentalists on concomitant realistic expectations. ... Read more

As quantum computing devices increase in size with respect to the number of qubits, two-qubit interactions become more challenging, necessitating innovative and scalable qubit routing solutions. In this work, we introduce beSnake, a novel algorithm specifically designed to address the intricate qubit routing challenges in scalable spin-qubit architectures. Unlike traditional methods in superconducting architectures that solely rely on swap operations, beSnake also incorporates the shuttle operation to optimize the execution time and fidelity of quantum circuits and achieves fast computation times of the routing task itself. Employing a simple breadth-first search approach, beSnake effectively manages the restrictions created by diverse topologies and qubit positions acting as obstacles for up to 72% qubit density. It also has the option to adjust the level of optimization and to dynamically tackle parallelized routing tasks, all the while maintaining noise awareness. Our simulations demonstrate beSnake's advantage over an existing routing solution on random circuits and real quantum algorithms with up to 1000 qubits, showing an average improvement of up to 80% in gate overhead, 54% in depth overhead, and up to 8.33 times faster routing times. ... Read more

Despite Noisy Intermediate-Scale Quantum devices being severely constrained, hardware- and algorithm-aware quantum circuit mapping techniques have been developed to enable successful algorithm executions. Not so much attention has been paid to mapping and compilation implementations for spin-qubit quantum processors due to the scarce availability of experimental devices and their small sizes. However, based on their high scalability potential and their rapid progress it is timely to start exploring solutions on such devices. In this work, we discuss the unique mapping challenges of a scalable crossbar architecture with shared control and introduce SpinQ, the first native compilation framework for scalable spin-qubit architectures. At the core of SpinQ is the Integrated Strategy that addresses the unique operational constraints of the crossbar while considering compilation scalability and obtaining a O(n) computational complexity. To evaluate the performance of SpinQ on this novel architecture, we compiled a broad set of well-defined quantum circuits and performed an in-depth analysis based on multiple metrics such as gate overhead, depth overhead, and estimated success probability, which in turn allowed us to create unique mapping and architectural insights. Finally, we propose novel mapping techniques that could increase algorithm success rates on this architecture and potentially inspire further research on quantum circuit mapping for other scalable spin-qubit architectures. ... Read more