This paper describes the verification of the classical software and hardware stack that is used to control cold atom- and superconducting-based quantum computing hardware. The paper serves both as an introduction to quantum computing and to how classical device verification techniques can be employed there. Two main challenges in building a quantum control stack are generating precise deterministic-timing operations at the edge and scaled-out processing in the middle layer. Both challenges are to do with a certain kind of functional performance correctness. And, as usual, the design lives under tight power, memory and latency constraints. The quantum control stack is a complex interaction of algorithms, software runtimes and digital hardware. We take inspiration from modern software approaches to engineering, such as continuous integration and hardware automation, to quickly ship experimental features to customers in the field.
This paper describes the verification of the classical software and hardware stack that is used to control cold atom- and superconducting-based quantum computing hardware. The paper serves both as an introduction to quantum computing and to how classical device verification techniques can be employed there. Two main challenges in building a quantum control stack are generating precise deterministic-timing operations at the edge and scaled-out processing in the middle layer. Both challenges are to do with a certain kind of functional performance correctness. And, as usual, the design lives under tight power, memory and latency constraints. The quantum control stack is a complex interaction of algorithms, software runtimes and digital hardware. We take inspiration from modern software approaches to engineering, such as continuous integration and hardware automation, to quickly ship experimental features to customers in the field.
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 computing is transitioning from laboratory research to industrial deployment, yet significant challenges persist: system scalability and performance, fabrication yields, and the advancement of algorithms and applications. We emphasize that in building quantum computers -- spanning quantum chips, system integration, instruction sets, algorithms, and middleware such as quantum error correction schemes -- design is everywhere. In this paper, we advocate for a holistic design perspective in quantum computing, a perspective we argue is pivotal to unlocking innovative co-design opportunities and addressing the aforementioned key challenges. To equip readers with sufficient background for exploring co-optimization opportunities, we detail how interconnected computational methods and tools collaborate to enable end-to-end quantum computer design. This coverage encompasses critical stages -- such as chip layout design automation, high-fidelity system-level simulation, Hamiltonian derivation for quantum system modeling, control pulse simulation, decoherence analysis, and physical verification and testing -- followed by quantum instruction set design. We then proceed to quantum system and software development, including quantum circuit synthesis, quantum error correction and fault tolerance, and logic verification and testing. Through these discussions, we illustrate with concrete examples -- including co-optimizing quantum instruction sets with algorithmic considerations, customizing error correction circuits to hardware-specific constraints, and streamlining quantum chip design through tailored code design, among others. We hope that the detailed end-to-end design workflow as well as these examples will foster dialogue between the hardware and software communities, ultimately facilitating the translation of meaningful research findings into future quantum hardware implementations.
Superconducting qubits are pivotal in advancing quantum computing, poised for scale but limited by the complexity and fidelity of their control and readout systems, relying on RF and signal processing infrastructure. This survey serves as a comprehensive and technically grounded review of control and readout architectures tailored for superconducting qubits. Synthesizing insights from device physics, circuit design, microwave engineering, signal processing, and cryogenic integration, this work details the practicalities of RF pulse generation, signal synthesis, and readout signal analysis for quantum systems. it covers key requirements, parameters, and pulse engineering techniques, including commonly used envelopes like Gaussian and DRAG designs. Moving to the system level, this survey systematically classifies and critically analyses current architectural strategies (covering key areas like frequency conversion, waveform management, and system infrastructure) and technology platforms (including adaptive classical control stacks, cryogenic CMOS circuits, and novel interconnects and interfaces), evaluating their trade-offs in performance. Extensive literature analysis identifies prevailing limitations such as wiring complexity, thermal budget constraints, latency, and power consumption, while highlighting underexplored opportunities for on-chip signal processing and novel interconnects, drawing analogies to advanced communication systems design. By consolidating diverse control paradigms and critically evaluating their trade-offs, this survey provides a unified foundation for designing next-generation quantum control stacks. Finally, a forward-looking roadmap outlines key trends in monolithic integration, cryo-compatible digital architectures, and physics-informed hardware co-design, offering both a retrospective synthesis and a prospective vision for quantum hardware engineering beyond the NISQ era.
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.
Quantum computing is a new and promising technology with the potential of exponentially powerful computation - if only a large-scale one can be built. There are several challenges in building a large-scale quantum computer - fabrication, verification, and architecture. The power of quantum computing comes from the ability to store a complex state in a single bit. This also what makes quantum systems difficult to build, verify, and design. Quantum states are fragile, so fabrication must be precise, and bits must often operate at very low temperatures. Unfortunately, the complete state may not be measured precisely, so verification is difficult. Imagine verifying an operation that is expected to not always get the same answer, but only an answer with a particular probability! Finally, errors occur much more often than with classical computing, making error correction the dominant task that quantum architectures need to perform well. We provide a basic tutorial of quantum computation for the system designer and examine the fundamental design and verification issues in constructing scalable quantum computers. We find the primary issues to be the verification of precise fabrication constraints, the design of quantum communication mechanisms, and the design of classical control circuitry for quantum operation.
Quantum computers promise to solve certain problems that are intractable for classical computers, such as factoring large numbers and simulating quantum systems. To date, research in 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 experimental quantum processors. Bridging this gap, we propose and build a prototype of a flexible control microarchitecture 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. We design a set of quantum microinstructions that allows flexible control of quantum operations with precise timing. We demonstrate the microarchitecture and microinstruction set by performing a standard gate-characterization experiment on a transmon qubit.