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Design of a Real-Time Embedded Control System for Quantum Computing Experiments

Richard Rademacher

Published Jan 27, 2020• 0 citations
OpenAlex

Abstract

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

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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
Quantum computing promises to revolutionize computing by providing exponential speed improvements to classically difficult problems. Over the past 30 years, experimental research has progressed from manipulating quantum systems to creating elementary gates in many quantum mechanical systems. One of the most successful media for implementing quantum gates is trapped ions. Current trapped-ion quantum computing architectures have very high gate fidelities and long coherence times, but creating quantum gates with low error rates with trapped ions is challenging since it requires precise trap and laser control. In order to implement quantum gates with trapped ions, I have created a field-programmable-gate-array- (FPGA) based microarchitecture for constructing laser-pulse sequences and controlling ancillary equipment. The microarchitecture is centralized to minimize experimental timing errors and is programmable to provide the generality necessary for implementing a vast range of experiments.
In recent years, the number of hybrid algorithms that combine quantum and classical computations has been continuously increasing. These two approaches to computing can mutually enhance each others’ performances thus bringing the promise of more advanced algorithms that can outmatch their pure counterparts. In order to accommodate this new class of codes, a proper environment has to be created, which enables the interplay between the quantum and classical hardware.For many of these hybrid processes the coherence time of the quantum computer arises as a natural time constraint, making it crucial to minimize the classical overhead. For ion-trap quantum computers however, this is a much less limiting factor than with superconducting technologies, since the relevant timescale is on the order of seconds instead of microseconds. In fact, we show that the operating time-scales of trapped-ion quantum computers are compatible with the execution speed of the Python programming language, enabling us to develop an interpreted scheme for real-time control of quantum computations. In particular, compilation of all instructions in advance is not necessary, unlike with superconducting qubits. This keeps the implementation of hybrid algorithms simple and also lets users benefit from the rich environment of existing Python libraries.In order to show that this approach of interpreted quantum-classical computations (IQCC) is feasible, we bring real-world examples and evaluate them in realistic benchmarks.
As trapped-ion quantum computing scales to larger qubit registers and more complex control protocols, classical control systems face a fundamental tradeoff: sub-microsecond board-level feedback requires tight hardware coupling, whereas maintainability and extensibility require clean, modular software abstractions. This paper presents QuCtrl-BELL (Bell), a compiler-driven software stack for trapped-ion quantum control. The design resolves this tradeoff by decoupling control flow -- including loops, branches, and synchronization -- from hardware state data. A Python-embedded domain-specific language (DSL) is lowered through a six-stage transpilation pipeline covering control flow graph (CFG) construction, static single-assignment (SSA) conversion, liveness analysis, and graph-coloring register allocation. The compiler generates deterministic distributed board-level programs and compact step-table data. A cross-board synchronization protocol supports feedback loops with latency below 700~ns without host intervention. Bell is deployed and evaluated on the QuCtrl-BELL platform (RISC-V + PXIe), demonstrating that a compiler-based infrastructure can provide programmability, deterministic timing, and modularity for scalable trapped-ion quantum 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.
Electronic control systems used for quantum computing have become increasingly complex as multiple qubit technologies employ larger numbers of qubits with higher fidelity targets. Whereas the control systems for different technologies share some similarities, parameters like pulse duration, throughput, real-time feedback, and latency requirements vary widely depending on the qubit type. In this paper, we evaluate the performance of modern System-on-Chip (SoC) architectures in meeting the control demands associated with performing quantum gates on trapped-ion qubits, particularly focusing on communication within the SoC. A principal focus of this paper is the data transfer latency and throughput of several high-speed on-chip mechanisms on Xilinx multi-processor SoCs, including those that utilize direct memory access (DMA). They are measured and evaluated to determine an upper bound on the time required to reconfigure a gate parameter. Worst-case and average-case bandwidth requirements for a custom gate sequencer core are compared with the experimental results. The lowest-variability, highest-throughput data-transfer mechanism is DMA between the real-time processing unit (RPU) and the PL, where bandwidths up to 19.2 GB/s are possible. For context, this enables reconfiguration of qubit gates in less than 2μμμs, comparable to the fastest gate time. Though this paper focuses on trapped-ion control systems, the gate abstraction scheme and measured communication rates are applicable to a broad range of quantum computing technologies.
This thesis presents the development of a superconducting qubit control and readout system based on FPGA technology, with a focus on the design of a pulse sequencer and the integration of frequency-multiplexed control. The system leverages the Quantum Instrumentation Control Kit (QICK), an open-source Xilinx RFSoC-based platform capable of direct digital synthesis and acquisition of microwave signals up to 10 GHz. The QICK firmware was extended to support the simultaneous generation and readout of multiple frequency-multiplexed tones, enabling the control and measurement of several qubits coupled to a shared feedline. This approach optimizes the number of controllable qubits relative to the available ADC and DAC resources on the RFSoC 4x2 board. Furthermore, this work includes the development of Qubase, a Python-based pulse sequencer that allows users to easily define and execute complex sequences of control and measurement pulses through the QICK Python interface. The resulting architecture is both flexible and scalable, providing a foundation for efficient multi-qubit experiments and enabling advanced techniques such as quantum error correction and real-time feedback.