Qwerty is a high-level quantum programming language built on bases and functions rather than circuits. This new paradigm introduces new challenges in compilation, namely synthesizing circuits from basis translations and automatically specializing adjoint or predicated forms of functions. This paper presents ASDF, an open-source compiler for Qwerty that answers these challenges in compiling basis-oriented languages. Enabled with a novel high-level quantum IR implemented in the MLIR framework, our compiler produces OpenQASM 3 or QIR for either simulation or execution on hardware. Our compiler is evaluated by comparing the fault-tolerant resource requirements of generated circuits with other compilers, finding that ASDF produces circuits with comparable cost to prior circuit-oriented compilers.

Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution.

In this paper, we present an FPGA design of an extremely low latency scientific machine learning application at the edge. Real-time prediction of errant high-energy particle beams at scientific facilities such as Spallation Neutron Source (SNS) is crucial to avoid damages to the equipment. Machine learning techniques are becoming increasingly effective to detect subtle signatures of the errant beams in the noisy sensor signals. However, to minimize potential damage done by errant beam, real-time errant beam detection has to be completed with extremely low latency, usually less than 1 microsecond. By stream processing the input features and employing out-of-order execution of decision nodes among the decision trees, we demonstrate that our highly efficient FPGA implementation can achieve 60 nanoseconds of computing latency for complex random forest models with 10,000 input features.

Domain specific computing is an idea that has been proposed as a path forward given the slowing of Moore’s Law and the breakdown of Dennard scaling [3]. Two fundamental questions include: (1) how does one define a domain; and (2) how does one go about architecting hardware that performs well for that domain? We present our preliminary work towards answering these questions.