Quantum computers promise to revolutionize information processing, offering game-changing opportunities in fields such as cryptography, machine learning, drug discovery, etc. However, building large-scale quantum computers presents significant challenges, requiring both fundamental research and technological breakthroughs. Among the various platforms proposed for the realization of the core of the quantum hardware, qubits realized in semiconductor nanostructures stand out. One of their key advantages lies in their compatibility with standard semiconductor manufacturing, enabling, among other capabilities, their co-integration with control and readout electronics, thus improving scalability and reducing interconnect complexity. In this review, we discuss recent advances in silicon-based spin qubits and present an overview of the progress and challenges in developing large-scale quantum computing systems based on this architecture.

In this paper, we present the characterisation of ring oscillator (RO) test circuits fabricated in GlobalFoundries’ (GF) 22nm fully depleted silicon-on-insulator (FD-SOI) process and operating from 200 K down to 4 K. We investigate ROs using NAND, NOR, and inverter standard cell libraries, including low, regular, and high threshold-voltage versions. Alongside temperature variations, we also consider a change in the power supply of ±5% from a nominal 0.8 V. The ROs demonstrate the combined effect of increased mobility, increased threshold voltage, and leakage current processes at cryogenic temperatures on their operation. By averaging over 81 fabricated ROs using three different delay gates and 2 different flavours, we report a statistical characterisation of their properties in the FD-SOI technology over temperature and supply voltage.

Conflict-free replicated data types (CRDTs) are a promising tool for designing scalable, coordination-free distributed systems. However, constructing correct CRDTs is difficult, posing a challenge for even seasoned developers. As a result, CRDT development is still largely the domain of academics, with new designs often awaiting peer review and a manual proof of correctness. In this paper, we present Katara, a program synthesis-based system that takes sequential data type implementations and automatically synthesizes verified CRDT designs from them. Key to this process is a new formal definition of CRDT correctness that combines a reference sequential type with a lightweight ordering constraint that resolves conflicts between non-commutative operations. Our process follows the tradition of work in verified lifting, including an encoding of correctness into SMT logic using synthesized inductive invariants and hand-crafted grammars for the CRDT state and runtime. Katara is able to automatically synthesize CRDTs for a wide variety of scenarios, from reproducing classic CRDTs to synthesizing novel designs based on specifications in existing literature. Crucially, our synthesized CRDTs are fully, automatically verified, eliminating entire classes of common errors and reducing the process of producing a new CRDT from a painstaking paper proof of correctness to a lightweight specification.

Despite decades of research and practical experience, developers have few tools for programming reliable distributed applications without resorting to expensive coordination techniques. Conflict-free replicated datatypes (CRDTs) are a promising line of work that enable coordination-free replication and offer certain eventual consistency guarantees in a relatively simple object-oriented API. Yet CRDT guarantees extend only to data updates; observations of CRDT state are unconstrained and unsafe. We propose an agenda that embraces the simplicity of CRDTs, but provides richer, more uniform guarantees. We extend CRDTs with a query model that reasons about which queries are safe without coordination by applying monotonicity results from the CALM Theorem, and lay out a larger agenda for developing CRDT data stores that let developers safely and efficiently interact with replicated application state.

Microsoft's internal big-data infrastructure is one of the largest in the world---with over 300k machines running billions of tasks from over 0.6M daily jobs. Operating this infrastructure is a costly and complex endeavor, and efficiency is paramount. In fact, for over 15 years, a dedicated engineering team has tuned almost every aspect of this infrastructure, achieving state-of-the-art efficiency (>60% average CPU utilization across all clusters). Despite rich telemetry and strong expertise, faced with evolving hardware/software/workloads this manual tuning approach had reached its limit---we had plateaued. In this paper, we present KEA, a multi-year effort to automate our tuning processes to be fully data/model-driven. KEA leverages a mix of domain knowledge and principled data science to capture the essence of our cluster dynamic behavior in a set of machine learning (ML) models based on collected system data. These models power automated optimization procedures for parameter tuning, and inform our leadership in critical decisions around engineering and capacity management (such as hardware and data center design, software investments, etc.). We combine "observational'' tuning (i.e., using models to predict system behavior without direct experimentation) with judicious use of "flighting'' (i.e., conservative testing in production). This allows us to support a broad range of applications that we discuss in this paper. KEA continuously tunes our cluster configurations and is on track to save Microsoft tens of millions of dollars per year. At the best of our knowledge, this paper is the first to discuss research challenges and practical learnings that emerge when tuning an exabyte-scale data infrastructure.

The twenty-first century has been dominated by the need for large scale data processing, marking the birth of big data platforms such as Cosmos. This paper describes the evolution of the exabyte-scale Cosmos big data platform at Microsoft; our journey right from scale and reliability all the way to efficiency and usability, and our next steps towards improving security, compliance, and support for heterogeneous analytics scenarios. We discuss how the evolution of Cosmos parallels the evolution of the big data field, and how the changes in the Cosmos workloads over time parallel the changing requirements of users across industry.

The mid 1990s has seen a significant growth in both the popularity and development of WWW based courseware and delivery. However, such efforts have been very variable in both effectiveness and comprehensiveness. This paper identifies a set of general requirements necessary in designing a pedagogically successful WWW based educational experience, which can be used in the assessment of WWW based educational courses. To illustrate how such requirements can be successfully addressed, the evaluation of a WWW based course which was trialed over a three-year period with approximately 300 students is presented.