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IEEE International Conference on Quantum Computing and Engineering (QCE)

QisDAX: An Open Source Bridge from Qiskit to Trapped-Ion Quantum Devices

Kaustubh Badrike, Aniket S. Dalvi, Filip Mazurek, Marissa D’Onofrio, Jacob Whitlow, Tianyi Chen, Samuel Phiri, Leon Riesebos, Kenneth R. Brown, Frank Mueller

Published Sep 17, 2023• 3 citations
Publisher

Abstract

Quantum computing has become widely available to researchers via cloud-hosted devices with different technologies using a multitude of software development frameworks. The vertical stack behind such solutions typically features quantum language abstraction and high-level translation frameworks that tend to be open source, down to pulse-level programming. However, the lower-level mapping to the control electronics, such as controls for laser and microwave pulse generators, remains closed source for contemporary commercial cloud-hosted quantum devices. One exception is the ARTIQ (Advanced Real-Time Infrastructure for Quantum physics) open-source library for trapped-ion control electronics. This stack has been complemented by the Duke ARTIQ Extensions (DAX) to provide modularity and better abstraction. It, however, remains disconnected from the wealth of features provided by popular quantum computing languages. This paper contributes QisDAX, a bridge between Qiskit and DAX that fills this gap. QisDAX provides interfaces for Python programs written using IBM's Qiskit and transpiles them to the DAX abstraction. This allows users to generically interface to the ARTIQ control systems accessing trapped-ion quantum devices. Consequently, the algorithms expressed in Qiskit become available to an open-source quantum software stack. This provides the first open-source, end-to-end, full-stack pipeline for remote submission of quantum programs for trapped-ion quantum systems in a non-commercial setting.

DOI
10.1109/qce57702.2023.00097

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Quantum computing has become widely available to researchers via cloud-hosted devices with different technologies using a multitude of software development frameworks. The vertical stack behind such solutions typically features quantum language abstraction and high-level translation frameworks that tend to be open source, down to pulse-level programming. However, the lower-level mapping to the control electronics, such as controls for laser and microwave pulse generators, remains closed source for contemporary commercial cloud-hosted quantum devices. One exception is the ARTIQ (Advanced Real-Time Infrastructure for Quantum physics) open-source library for trapped-ion control electronics. This stack has been complemented by the Duke ARTIQ Extensions (DAX) to provide modularity and better abstraction. It, however, remains disconnected from the wealth of features provided by popular quantum computing languages. This paper contributes QisDAX, a bridge between Qiskit and DAX that fills this gap. QisDAX provides interfaces for Python programs written using IBM's Qiskit and transpiles them to the DAX abstraction. This allows users to generically interface to the ARTIQ control systems accessing trapped-ion quantum devices. Consequently, the algorithms expressed in Qiskit become available to an open-source quantum software stack. This provides the first open-source, end-to-end, full-stack pipeline for remote submission of quantum programs for trapped-ion quantum systems in a non-commercial setting.
Duke ARTIQ Extensions (DAX) provides a framework for writing modular control software for ion-trap quantum systems. DAX allows users to interface with the system at the gate level using an intermediate representation called DAX.program. However, DAX does not have the tools needed to simulate these programs to see quantum state evolutions, as DAX only allows for simulations at the device level. We propose a modular simulation framework, DAX.program-sim (DPS), that can simulate quantum programs at the level of quantum operations. This addition to DAX for classical simulation of quantum systems is vital for testing, benchmarking, and verification of quantum hardware. The DPS pipeline is designed to have input identical to the one that runs on the hardware. Our architecture allows flexible backends for simulation, enabling both gate-level simulations and pulse-level simulations with and without noise. We demonstrate this unified workflow, executing the same DAX.program file in simulation as well as on hardware. As a specific example, we run benchmarking circuits using simulators targeting ion trap quantum computers and compare them to hardware results of the same circuits.
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Since the late 2010s, quantum computers have become commercially available, and the number of services that users can run remotely via cloud servers is increasing. In Japan, several domestic superconducting quantum computing systems, including our own, began operation in 2023. However, the design of quantum computing systems, especially in the most critical areas near quantum computers, remains largely undisclosed, creating a significant barrier to entry into the quantum computing field. If this situation continues, progress toward standardization, which is essential for guiding quantum computer development, will stall, and it will be difficult to develop a practical quantum computing system that can perform calculations on a supercomputer scale. To address this issue, we propose Open Quantum Toolchain for OPerators and USers (OQTOPUS), a full-stack quantum computing system developed from research with real quantum computers. OQTOPUS is one of the world's largest open-source software projects, covering operational software from cloud-based execution environment construction to system operation. Furthermore, to perform quantum computing effectively and efficiently, it implements key features, such as transpilers, multi-programming, and error mitigation, in an area as close as possible to a quantum computer, an area that system vendors rarely disclose. Finally, this study presents experimental results of applying OQTOPUS to a real quantum computer. OQTOPUS is publicly available on GitHub and will notably lower the barrier to entry into the quantum computing field, contributing to the formation of a quantum computing developer community through open discussion.
Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of Python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the QC software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of key features including a flexible optimization toolkit, engineering-inspired filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.
Quantum Computers are quickly becoming capable of solving certain tasks substantially faster than classical computers and the promise of quantum-driven advancements in research and economy continues to accelerate the development of quantum technology. However, most software development for quantum computers relies on the tedious manual implementation of quantum circuits on a very low level of abstraction, with tools such as the prominent IBM Qiskit SDK. In 2020, Silq, a quantum language to enable more intuitive and robust quantum development, was presented. While it substantially simplifies the write- and readability of quantum programs, Silq Code can only be run through its simulator on classical hardware. In comparison, Qiskit and its close integration with IBM’s Quantum Experience even enable users to run and evaluate quantum programs on physical quantum hardware. This paper proposes an automatic source-to-source translator for basic Silq Code and the extension of Qiskit by core concepts of Silq’s abstraction layers, such as Quantum Indexing and Quantum Control Flow.
This paper explains the roadmap for running quantum circuit programs based on the Qiskit library in quantum simulators as well as real cloud-based IBM quantum computers. Qiskit is a free and open-source software development platform based on the Python programming language that is used in quantum programming. Qiskit acts as a link between quantum computing’s theoretical foundations and the practical aspects of programming and experimentation. It also allows users to experiment with and develop quantum algorithms, as well as simulate and execute them on simulators and real-world cloud-based quantum devices. It also simplifies the quantum programming process and allows a diverse range of people to participate in the exciting world of quantum computing. The paper, on the other hand, provides the mathematical foundation for analyzing quantum circuits and algorithms using linear algebra principles, as they provide the tools needed to describe and manipulate quantum states and operations. Furthermore, the paper shows quantum circuit design and implementation using real Qiskit codes.