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We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell states (12x error reduction), random circuits (15x), and a prototype Anderson Impurity Model ground state solver for materials science applications (up to 6x, non-fault-tolerantly). The logical qubits are implemented via the [[4, 2, 2]] code (C4). Our work constitutes the first complete realization of the benchmarking protocol proposed by Gottesman 2016 [1] demonstrating results consistent with fault-tolerance. In light of recent advances on applying concatenated C4/C6 detection codes to achieve error correction with high code rates and thresholds, our work can be regarded as a building block towards a practical scheme for fault tolerant quantum computation. Our demonstration of a materials science application with logical qubits particularly demonstrates the immediate value of these techniques on current experiments.

Choreographic programming (CP) is a paradigm for implementing distributed systems that uses a single global program to define the actions and interactions of all participants. Library-level CP implementations, like HasChor, integrate well with mainstream programming languages but have several limitations: Their conditionals require extra communication; they require specific host-language features (e.g., monads); and they lack support for programming patterns that are essential for implementing realistic distributed applications. We make three contributions to library-level CP to specifically address these challenges. First, we propose and formalize enclaves and multiply-located values, which enable efficient conditionals in library-level CP without redundant communication. Second, we propose end-point projection as dependency injection, a design pattern that enables library-level CP in host languages without support for monads. Third, we propose census polymorphism, a technique for abstracting over the number of participants in a choreography. We demonstrate these contributions via implementations in Haskell, Rust, and TypeScript., Comment: Submitted to PLDI25

Quantum computers must achieve large-scale, fault-tolerant operation to deliver on their promise of transformational processing power [1-4]. This will require thousands or millions of high-fidelity quantum gates and similar numbers of qubits [5]. Demonstrations using neutral-atom qubits trapped and manipulated by lasers have shown that this modality can provide high two-qubit gate (CZ) fidelities and scalable operation [6-13]. However, the gates in these demonstrations are driven by lasers that do not resolve individual qubits, with universal computation enabled by physical mid-circuit shuttling of the qubits. This relatively slow operation may greatly extend runtimes for useful, large-scale computation. Here we demonstrate a universal neutral-atom quantum computer with gate rates limited by optical switching times, rather than shuttling, by individually addressing tightly focused laser beams at an array of single atoms. We achieve CZ fidelity of 99.35(4)% and local single-qubit RZ gate fidelity of 99.902(8)%. Moreover, we demonstrate non-destructive readout of alkali-atom qubits with 0.9(3)% loss, which boosts operational speed. This technique also enables us to measure a state-of-the-art CZ fidelity of 99.73(3)% when excluding atom-loss events, which may be mitigated through erasure conversion. Our results represent a critical step towards large-scale, fault-tolerant neutral-atom quantum computers that can execute computations on practical timescales.

Choreographic programming (CP) is a paradigm for programming distributed applications as single, unified programs, called choreographies, that are then compiled to node-local programs via endpoint projection (EPP). Recently, library-level CP frameworks have emerged, in which choreographies and EPP are expressed as constructs in an existing host language. So far, however, library-level CP lacks a solid theoretical foundation. In this paper, we propose modeling library-level CP using algebraic effects, an abstraction that generalizes the approach taken by existing CP libraries. Algebraic effects let us define choreographies as computations with user-defined effects and EPP as location-specific effect handlers. Algebraic effects also lend themselves to reasoning about correctness properties, such as soundness and completeness of EPP. We present a prototype of a library-level CP framework based on algebraic effects, implemented in the Agda proof assistant, and discuss our ongoing work on leveraging the algebraic-effects-based approach to prove the correctness of our library-level CP implementation., Comment: Talk proposal for Choreographic Programming 2024

Choreographic programming (CP) is an emerging paradigm for programming distributed applications that run on multiple nodes. In CP, the programmer writes one program, called a choreography, that is then transformed to individual programs for each node via a compilation step called endpoint projection (EPP). While CP languages have existed for over a decade, library-level CP -- in which choreographies are expressed as programs in an existing host language, and choreographic language constructs and EPP are provided entirely by a host-language library -- is in its infancy. Library-level CP has great potential, but existing implementations have portability, efficiency, and practicality drawbacks that hinder its adoption. In this paper, we aim to advance the state of the art of library-level CP with two novel techniques for choreographic library design and implementation: endpoint projection as dependency injection (EPP-as-DI), and choreographic enclaves. EPP-as-DI is a language-agnostic technique for implementing EPP at the library level. Unlike existing library-level approaches, EPP-as-DI asks little from the host language -- support for higher-order functions is all that is required -- making it usable in a wide variety of host languages. Choreographic enclaves are a language feature that lets the programmer define sub-choreographies within a larger choreography. Within an enclave, "knowledge of choice" is propagated only among the enclave's participants, enabling the seamless use of the host language's conditional constructs while addressing the efficiency limitations of existing library-level CP implementations. We implement EPP-as-DI and choreographic enclaves in ChoRus, the first CP library for the Rust programming language. Our case studies and benchmarks demonstrate that the usability and performance of ChoRus compares favorably to traditional distributed programming in Rust.

The Glasgow Haskell Compiler is known for its feature-laden runtime system (RTS), which includes lightweight threads, asynchronous exceptions, and a slew of other features. Their combination is powerful enough that a programmer may complete the same task in many different ways -- some more advisable than others. We present a user-accessible actor framework hidden in plain sight within the RTS and demonstrate it on a classic example from the distributed systems literature. We then extend both the framework and example to the realm of dynamic types. Finally, we raise questions about how RTS features intersect and possibly subsume one another, and suggest that GHC can guide good practice by constraining the use of some features., Comment: To appear at Haskell Symposium 2023

The Lamport diagram is a pervasive and intuitive tool for informal reasoning about "happens-before" relationships in a concurrent system. However, traditional axiomatic formalizations of Lamport diagrams can be painful to work with in a mechanized setting like Agda. We propose an alternative, inductive formalization -- the causal separation diagram (CSD) -- that takes inspiration from string diagrams and concurrent separation logic, but enjoys a graphical syntax similar to Lamport diagrams. Critically, CSDs are based on the idea that causal relationships between events are witnessed by the paths that information follows between them. To that end, we model happens-before as a dependent type of paths between events. The inductive formulation of CSDs enables their interpretation into a variety of semantic domains. We demonstrate the interpretability of CSDs with a case study on properties of logical clocks, widely-used mechanisms for reifying causal relationships as data. We carry out this study by implementing a series of interpreters for CSDs, culminating in a generic proof of Lamport's clock condition that is parametric in a choice of clock. We instantiate this proof on Lamport's scalar clock, on Mattern's vector clock, and on the matrix clocks of Raynal et al. and of Wuu and Bernstein, yielding verified implementations of each. The CSD formalism and our case study are mechanized in the Agda proof assistant., Comment: This revision is as published in PACMPL through OOPSLA, but with [authorversion] set. Compared to the previous version, the introduction has been almost entirely rewritten

When building a new application we are increasingly confronted with the need of reusing and integrating pre-existing knowledge. Nevertheless, it is a fact that this prior knowledge is virtually impossible to reuse as-is. This is true also in domains, e.g., eHealth, where a lot of effort has been put into developing high-quality standards and reference ontologies, e.g. FHIR1. In this paper, we propose an integrated methodology, called iTelos, which enables data and knowledge reuse towards the construction of Interoperable Electronic Health Records (iEHR). The key intuition is that the data level and the schema level of an application should be developed independently, thus allowing for maximum flexibility in the reuse of the prior knowledge, but under the overall guidance of the needs to be satisfied, formalized as competence queries. This intuition is implemented by codifying all the requirements, including those concerning reuse, as part of a purpose defined a priori, which is then used to drive a middle-out development process where the application schema and data are continuously aligned. The proposed methodology is validated through its application to a large-scale case study., Comment: DSAI SPRINGER BOOK. arXiv admin note: text overlap with arXiv:2105.09418

As semiconductor power density is no longer constant with the technology process scaling down, modern CPUs are integrating capable data accelerators on chip, aiming to improve performance and efficiency for a wide range of applications and usages. One such accelerator is the Intel Data Streaming Accelerator (DSA) introduced in Intel 4th Generation Xeon Scalable CPUs (Sapphire Rapids). DSA targets data movement operations in memory that are common sources of overhead in datacenter workloads and infrastructure. In addition, it becomes much more versatile by supporting a wider range of operations on streaming data, such as CRC32 calculations, delta record creation/merging, and data integrity field (DIF) operations. This paper sets out to introduce the latest features supported by DSA, deep-dive into its versatility, and analyze its throughput benefits through a comprehensive evaluation. Along with the analysis of its characteristics, and the rich software ecosystem of DSA, we summarize several insights and guidelines for the programmer to make the most out of DSA, and use an in-depth case study of DPDK Vhost to demonstrate how these guidelines benefit a real application., Comment: This paper has been accepted by ASPLOS'24. Please refer to the linked DOI for the official version of this paper

The ever-growing demands for memory with larger capacity and higher bandwidth have driven recent innovations on memory expansion and disaggregation technologies based on Compute eXpress Link (CXL). Especially, CXL-based memory expansion technology has recently gained notable attention for its ability not only to economically expand memory capacity and bandwidth but also to decouple memory technologies from a specific memory interface of the CPU. However, since CXL memory devices have not been widely available, they have been emulated using DDR memory in a remote NUMA node. In this paper, for the first time, we comprehensively evaluate a true CXL-ready system based on the latest 4th-generation Intel Xeon CPU with three CXL memory devices from different manufacturers. Specifically, we run a set of microbenchmarks not only to compare the performance of true CXL memory with that of emulated CXL memory but also to analyze the complex interplay between the CPU and CXL memory in depth. This reveals important differences between emulated CXL memory and true CXL memory, some of which will compel researchers to revisit the analyses and proposals from recent work. Next, we identify opportunities for memory-bandwidth-intensive applications to benefit from the use of CXL memory. Lastly, we propose a CXL-memory-aware dynamic page allocation policy, Caption to more efficiently use CXL memory as a bandwidth expander. We demonstrate that Caption can automatically converge to an empirically favorable percentage of pages allocated to CXL memory, which improves the performance of memory-bandwidth-intensive applications by up to 24% when compared to the default page allocation policy designed for traditional NUMA systems., Comment: This paper has been accepted by MICRO'23. Please refer to the https://doi.org/10.1145/3613424.3614256 for the official version of this paper

Genito-Pelvic Pain/Penetration-Disorder (GPPPD) is a common disorder but rarely treated in routine care. Previous research documents that GPPPD symptoms can be treated effectively using internet-based psychological interventions. However, non-response remains common for all state-of-the-art treatments and it is unclear which patient groups are expected to benefit most from an internet-based intervention. Multivariable prediction models are increasingly used to identify predictors of heterogeneous treatment effects, and to allocate treatments with the greatest expected benefits. In this study, we developed and internally validated a multivariable decision tree model that predicts effects of an internet-based treatment on a multidimensional composite score of GPPPD symptoms. Data of a randomized controlled trial comparing the internet-based intervention to a waitlist control group (N =200) was used to develop a decision tree model using model-based recursive partitioning. Model performance was assessed by examining the apparent and bootstrap bias-corrected performance. The final pruned decision tree consisted of one splitting variable, joint dyadic coping, based on which two response clusters emerged. No effect was found for patients with low dyadic coping ($n$=33; $d$=0.12; 95% CI: -0.57-0.80), while large effects ($d$=1.00; 95%CI: 0.68-1.32; $n$=167) are predicted for those with high dyadic coping at baseline. The bootstrap-bias-corrected performance of the model was $R^2$=27.74% (RMSE=13.22).

Choreographic programming is an emerging paradigm for programming distributed systems. In choreographic programming, the programmer describes the behavior of the entire system as a single, unified program -- a choreography -- which is then compiled to individual programs that run on each node, via a compilation step called endpoint projection. We present a new model for functional choreographic programming where choreographies are expressed as computations in a monad. Our model supports cutting-edge choreographic programming features that enable modularity and code reuse: in particular, it supports higher-order choreographies, in which a choreography may be passed as an argument to another choreography, and location-polymorphic choreographies, in which a choreography can abstract over nodes. Our model is implemented in a Haskell library, HasChor, which lets programmers write choreographic programs while using the rich Haskell ecosystem at no cost, bringing choreographic programming within reach of everyday Haskellers. Moreover, thanks to Haskell's abstractions, the implementation of the HasChor library itself is concise and understandable, boiling down endpoint projection to its short and simple essence.