Software tools for quantum control: improving quantum computer performance through noise and error suppression
Harrison Ball, Michael J. Biercuk, Andre R. R. Carvalho, Jiayin Chen, Michael Hush, Leonardo Andreta de Castro, Li Li, Per Liebermann, Harry J. Slatyer, Claire Edmunds, Virginia Frey, Cornelius Hempel, Alistair R. Milne
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.
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.
A quantum processor, like any computing device, requires the development of both hardware and the necessary set of software solutions, starting with quantum algorithms and ending with means of accessing quantum devices. As part of the roadmap for the development of the high-tech field of quantum computing in the period from 2020 to 2024, a set of software solutions for quantum computing devices was developed. This software package includes a set of quantum algorithms for solving prototypes of applied tasks, monitoring and benchmarking tools for quantum processors, error suppression and correction methods, tools for compiling and optimizing quantum circuits, as well as interfaces for remote cloud access. This review presents the key results achieved, among which it is necessary to mention the execution of quantum algorithms using a cloud-based quantum computing platform.
Despite rapid progress in quantum computing in the last decade, the limited usability of quantum computers remains a major roadblock toward its wider adoption. Current noisy intermediate-scale quantum (NISQ) computers produce highly erroneous program outputs for quantum-advantage-proven algorithms - that is, algorithms that are infeasible or orders of magnitude slower on classical supercomputing and high-performance computing (HPC) clusters. Unfortunately, currently, quantum computing programmers lack robust system software tools and methods to make meaningful use of erroneous program executions on quantum computers. This lack of capability is the core motivation behind the fundamental question this dissertation poses: "Can we build system software tools for programmers to make the quantum program execution and output meaningful on NISQ machines?" This dissertation answers this question in the affirmative - experimentally demonstrating on real-system quantum computers that it is possible to extract near-accurate program output from noisy executions on today's erroneous quantum computers, ironically using classical HPC resources and knowledge. This dissertation demonstrates how to achieve this goal without requiring user intervention, domain knowledge about quantum algorithms, or additional quantum hardware support. Unfortunately, as this dissertation uncovers, progressing toward making quantum computers usable is a double-edged sword. In the near future, only a few entities in the world may have access to powerful quantum computers, and these quantum computers will be used to solve previously-unsolved large-scale optimization problems, possibly without an explicit trust model between the service provider and the customer. Therefore, this dissertation envisions that the solutions to such large-scale optimization problems will be considered sensitive and will need to be protected. This dissertation takes the first few steps toward preparing us for that future - by developing a novel method that intelligently obfuscates near-accurate program output and quantum circuit structure to preserve a customer's privacy under a specified computation model and resource availability. The approaches introduced in this dissertation open up new research avenues for hybrid quantum-classical computing. This dissertation also aims to lower the barrier to entry for quantum computing research for the experimental computer systems and HPC community by open-sourcing multiple novel datasets and software frameworks implemented for real-system quantum computers.--Author's abstract
The connection and eventual integration of High-Performance Computing (HPC) with Quantum Computing (QC) represents a transformative advancement in computational technology, promising significant enhancements in solving complex, previously intractable problems. This manuscript provides a comprehensive overview of the current state of HPC-QC interfacing, detailing architectural methodologies, software stack developments, middleware functionalities, and hardware integration strategies. It critically assesses existing hardware-level integration models, ranging from standalone and loosely-coupled architectures to tightly-integrated and on-node systems. The software ecosystem is analyzed, highlighting prominent frameworks such as Qiskit, PennyLane, CUDA-Q, and middleware solutions like Pilot-Quantum, essential for seamless hybrid computing environments. Furthermore, the manuscript discusses practical applications in optimization, machine learning, and many-body dynamics, where hybrid HPC-QC systems can offer substantial advantages. It also describes existing challenges, including hardware limitations (coherence, scalability, connectivity), software maturity, communication overhead, resource management complexities, and cost factors. Finally, future directions towards tighter hardware and software integration are discussed, emphasizing ongoing research developments and emerging trends that promise to expand the capabilities and accessibility of hybrid HPC-QC systems.
The goal of this paper is to deliver the overview of the current state of the art, provide experience report on developing quantum software tools, and outline the perspective for developing quantum programming tools supporting symbolic programming for the needs of quantum computing technologies. The main focus of this paper is on quantum computing technologies, as they can in the most direct way benefit from developing tools enabling the symbolic manipulation of quantum circuits and providing software tools for creating, optimizing, and testing quantum programs. We deliver a short survey of the most popular approaches in the field of quantum software development, pointing their strengths and weaknesses. This helps to formulate a list of desirable characteristics which should be included in quantum computing frameworks. Next, we describe a software architecture and its preliminary implementation supporting the development of quantum programs using symbolic approach, encouraging the functional programming paradigm, and, at the same, time enabling the integration with high-performance and cloud computing. The described software consists of several packages developed to address different needs, but nevertheless sharing common design concepts. We also outline how the presented approach could be used in tasks in quantum software engineering: quantum software testing and quantum circuit construction.
Abstract Quantum error control and mitigation techniques help improve how quantum computers handle errors, making algorithms run more efficiently despite noisy hardware. These strategies work at the software level and are built into programs before they run on quantum machines. Unlike quantum error correction, which actively detects and fixes errors during computation, error mitigation does not use real-time corrections but instead reduces the impact of errors after computations. Error mitigation can also help reduce the extra resources needed for full error correction. This chapter explains how these techniques relate to each other, provides an overview of key error mitigation methods and their limitations, and highlights leading academic and technological players in this field. It also covers current trends, such as integrating error-aware programming, benchmarking performance, and ensuring access to quantum hardware for testing. To advance quantum computing, it is recommended that research ecosystems support specialized centers and contribute to key strategic areas in the field.
This study examines the current technological components of quantum computing through a multi layered approach. It begins by comparing quantum and classical computers across fundamental aspects such as information representation, computational power, fault tolerance, and computational complexity, with the key differences summarized in a technical comparison table. Subsequently, the study analyzes the primary architectural approaches in quantum hardware, including superconducting qubits, trapped ion systems, and photonic platforms. On the software side, quantum programming languages such as Qiskit, Cirq, and PennyLane are evaluated in terms of their features, supported infrastructures, and learning curves. The significance of variational quantum algorithms tailored for the noisy intermediate-scale quantum (NISQ) era namely, the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) along with hybrid computing systems, is also emphasized. Overall, this work aims to provide a comprehensive overview of the current state of modern quantum computing from both hardware and software perspectives, highlighting key developments and ongoing progress in the field.