arXiv:2210.14341 · IEEE International Conference on Quantum Computing and Engineering (QCE)
Modular software for real-time quantum control systems
Leon Riesebos, Brad Bondurant, Jacob Whitlow, Junki Kim, Mark G. Kuzyk, Tianyi Chen, Samuel Phiri, Ye Wang, Chao Fang, Andrew Van Horn, Jungsang Kim, Kenneth R. Brown
Real-time control software and hardware is essential for operating quantum computers. In particular, the software plays a crucial role in bridging the gap between quantum programs and the quantum system. Unfortunately, current control software is often optimized for a specific system at the cost of flexibility and portability. We propose a systematic design strategy for modular real-time quantum control software and demonstrate that modular control software can reduce the execution time overhead of kernels by 63.3% on average while not increasing the binary size. Our analysis shows that modular control software for two distinctly different systems can share between 49.8% and 91.0% of covered code statements. To demonstrate the modularity and portability of our software architecture, we run a portable randomized benchmarking experiment on two different ion-trap quantum systems.
Real-time control software and hardware is essential for operating quantum computers. In particular, the software plays a crucial role in bridging the gap between quantum programs and the quantum system. Unfortunately, current control software is often optimized for a specific system at the cost of flexibility and portability. We propose a systematic design strategy for modular real-time quantum control software and demonstrate that modular control software can reduce the execution time overhead of kernels by 63.3% on average while not increasing the binary size. Our analysis shows that modular control software for two distinctly different systems can share between 49.8% and 91.0% of covered code statements. To demonstrate the modularity and portability of our software architecture, we run a portable randomized benchmarking experiment on two different ion-trap quantum systems.
Modern quantum computers rely heavily on real-time control systems for operation. Software for these systems is becoming increasingly more complex due to the demand for more features and more real-time devices to control. Unfortunately, testing real-time control software is often a complex process, and existing simulation software is not usable or practical for software testing. For this purpose, we implemented an interactive simulator that simulates signals at the application programming interface level. We show that our simulation infrastructure simulates kernels 6.9 times faster on average compared to execution on hardware, while the position of the timeline cursor is simulated with an average accuracy of 97.9% when choosing the appropriate configuration.
We present a modular operating system that bridges high-level quantum programming interfaces with low-level hardware execution. The system is built around a three-level abstraction that separates user request, hardware execution, and post-processing, delivering flexible compilation and post-processing such as expectation value evaluation, with minimal overhead. Quantum circuits are submitted through RESTful HTTP APIs and compiled into pulse-level instructions for lower-level system. The control system is composed of individual modules responsible for compilation, dispatch, monitoring, and analysis, coordinated through asynchronous WebSocket connections for real-time feedback.
Quantum computers can accelerate solving some problems which are inefficiently solved by classical computers, such as quantum chemistry simulation. To date, quantum computer engineering has focused primarily at opposite ends of the required system stack: devising high-level programming languages and compilers to describe and optimize quantum algorithms, and building reliable low-level quantum hardware. Relatively little attention has been given to using the compiler output to fully control the operations on current experimental quantum processors. Bridging this gap, we propose and build a prototype of flexible control microarchitecture, named QuMA, supporting quantum-classical mixed code for a superconducting quantum processor. The microarchitecture is based on three core elements: (i) a codeword-based event control scheme, (ii) queue-based precise event timing control, and (iii) a flexible multilevel instruction decoding mechanism for control.
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.
This thesis describes the design of a real-time control system for trapped ion quantum computer experiments. It is framed in the context of the QuantumIon project, a project at the University of Waterloo’s Institute for Quantum Computing that aims to provide a scalable, remote-operation ion trap for a wide variety of quantum research without the need for ‘expert’ ion-trap knowledge. The target users span the range of ion-trap researchers,algorithms researchers, performance benchmarking researchers, and quantum simulation researchers.The control system features a user programming language, remote access to a compiling server, a sub-nanosecond time sequencing engine, arbitrary waveform generation for pulse shaping, and fully adjustable internal parameters. This platform affords the user extraordinary flexibility for many research use cases without requiring physical access. High-speed precision timing is achieved through the use of FPGA technology, while internal consistency (necessary for usability by non-experts) is achieved through an abstraction layer approach. Supercomputing-grade network infrastructure is employed to meet the strict timing requirements. An extensive suite of calibration tools and results is available to monitor machine-dependent parameters of the experiment. A sophisticated symbolic algebra system is used to create powerful calculations of precision timing sequences. Extensive automation is employed to remove the need for physical access, thus providing quantum computing to a wide audience. Under this model even the lowest-level control is avail-able to support innovative new designs, while a “library” of pre-defined sequences is also available to leverage “best practice” gates for those wishing rapid results. Finally, the user language itself is designed to be portable, allowing bindings to current popular classical languages such as Matlab and Python, and can be expanded for use in quantum-specific languages such as Cirq, Quill, and QASM .Through this approach the control system for QuantumIon is a flexible, powerful, scalable, and robust platform that is expected to be in use for a long time
This paper presents a novel approach to quantum control, termed hardware-software co-optimization, aiming to enhance the efficiency of quantum computation. The core idea revolves around developing a dynamic optimization framework that adapts control pulses based on real-time feedback from the quantum hardware. This framework integrates quantum control algorithms with the specific characteristics of the quantum device, such as coherence time and gate fidelity. The optimization process is achieved through a collaborative system where the optimizer adjusts the control pulses, and the quantum computing software executes the computations. We demonstrate the potential of this approach to improve the overall performance of quantum algorithms by mitigating the impact of hardware imperfections. The proposed methodology offers a pathway to more robust and efficient quantum control, bridging the gap between theoretical algorithms and practical hardware implementation. Key mathematical elements explored include pulse shaping, error mitigation strategies, and adaptive control algorithms.