An evolving role from classical to quantum computing, developing applications, tools, and interfaces that use quantum hardware and algorithms.
Software Engineers in quantum computing develop the programming infrastructure, tools, and applications that enable practical implementation of quantum algorithms. These engineers create the essential software layers that connect theoretical quantum approaches with functional computing systems, both quantum and classical.
A typical Software Engineer in this sector may work primarily with specialised quantum programming frameworks and software development kits (SDKs) such as Qiskit, Cirq, Q#, PennyLane, and Amazon Braket. They develop software that addresses the particular requirements of quantum computation, including circuit construction, gate operations, measurement processes, and the probabilistic nature of quantum results.
A central challenge in quantum software engineering involves developing appropriate abstractions that manage quantum complexity while providing necessary control for algorithm implementation. This includes creating programming interfaces, compilers, optimizers, and simulators that support quantum algorithm development and testing. Engineers must consider both current NISQ (Noisy Intermediate-Scale Quantum) limitations and future requirements of fault-tolerant systems.
Software Engineers in this field design and implement hybrid quantum-classical systems that integrate quantum processing units with classical computing resources. This requires developing effective interfaces between these fundamentally different computational paradigms, including data preparation pipelines, job scheduling systems, and result processing workflows.
These engineers implement testing methodologies adapted to quantum computation’s probabilistic nature. This includes developing simulation environments, verification techniques, and benchmarking approaches that can validate quantum software despite the challenges of working with inherently probabilistic systems and hardware limitations.
Performance optimization constitutes a significant aspect of quantum software engineering. This includes circuit optimization, implementing error mitigation techniques, and developing transpilation methods that map abstract quantum algorithms to specific hardware configurations with their particular connectivity constraints and gate sets.
As quantum hardware evolves, Software Engineers in this field continuously adapt software systems to leverage new capabilities while maintaining compatibility with existing code bases. Their work provides the essential software infrastructure required for quantum computing to transition from theoretical concepts to practical applications.
The following are a hand-picked selection of articles and resources relating to the Software Engineer’s role the creation of effective quantum computing applications. These include experts in the field, active practitioners, and notable perspectives.
Beverland, M. E., et al. (March 14, 2022). “The software stack for fault-tolerant quantum computers.” arXiv.org. https://arxiv.org/abs/2203.07629
Ushijima-Mwesigwa, H., et al. (March 22, 2023). “An Overview of the Quantum Software Stack.” ACM Transactions on Quantum Computing, 4(1). https://dl.acm.org/doi/10.1145/3578335
Yuan, X., et al. (Sept. 5, 2023). “Testing, verification, and debugging of quantum software.” arXiv.org. https://arxiv.org/abs/2309.02079
The Linux Foundation. (Accessed July 20, 2025). “QIR Alliance.” qir-alliance.org. https://qir-alliance.org/
Sivarajah, S., et al. (March 23, 2020). “TKET: A Retargetable Compiler for NISQ Devices.” arXiv.org. https://arxiv.org/abs/2003.10611
Amazon Web Services. (June 29, 2022). “Run hybrid quantum-classical algorithms faster with Amazon Braket Hybrid Jobs.” AWS Quantum Computing Blog. https://aws.amazon.com/blogs/quantum-computing/run-hybrid-quantum-classical-algorithms-faster-with-amazon-braket-hybrid-jobs/
Partnering to advance methods and approaches to quantum-classical integration for advanced research.
Partnering to deploy the world's first diamond-based quantum accelerator in a supercomputing environment, creating Australia's first quantum-supercomputing hub.
Simulating chemistry for next-generation lithium-sulfur batteries, demonstrating the use of quantum computing for materials discovery in the automotive industry.
The results of a 12-month aircraft loading optimization project that tackled the computationally intensive challenge of optimizing aircraft cargo loading.
A partnership to run a quantum computing innovation challenge for automotive applications.