About this Research Topic
Over the past decade, quantum computing technologies have experienced rapid development. However, several critical scaling challenges must be overcome before fault-tolerant quantum computation is achievable across any of today’s quantum computing platforms. Concurrently, a significant theoretical task is the accurate estimation of the resources required to execute a useful algorithmic task using quantum computers. The resources which are accounted for can range from qubit and gate counts, to cooling, space requirements, and energy use. Such calculations set concrete goals for hardware developers and, in certain cases, the entire field. These goals also unveil shortcomings in current algorithm design and fault-tolerance schemes which exhibit excessive resource demands which, in turn, spurs advances in these areas. As the field of quantum information science continues to tackle the challenge of scaling quantum computers, such resource estimations will provide essential guidance and direction to the scientific community, as well as several other stakeholders.
The aim of this Research Topic is to collect high-quality and accurate resource estimations for several important quantum computing algorithms. These estimations are expected to be primarily related to fault-tolerant quantum computing. Estimations for the execution of algorithmic tasks on noisy intermediate scale (NISQ) devices will also be considered if a convincing argument for why the task is meaningful/impactful is presented. Manuscripts which identify important areas for improvement in quantum algorithm design, fault tolerance schemes, and quantum computing architectures or hardware are particularly encouraged.
Topics of particular interest for this Research Topic include, but are not limited to:
- Estimations for qubit counts and gate depths of specific quantum algorithm implementations.
- Studies analyzing resource requirements with respect to particular qubit technologies or architectures.
- Hardware-agnostic analysis and resource estimations for the implementation of quantum algorithms.
- Clifford+T optimization strategies and resource estimations for effective error correction decoding, quantitative analysis of execution of algorithms with specific error correction codes.
- Cooling power, space use, time-to-solution, or energy consumption estimations for the implementation of quantum algorithms.
The aim of this Research Topic is to collect high-quality and accurate resource estimations for several important quantum computing algorithms. These estimations are expected to be primarily related to fault-tolerant quantum computing. Estimations for the execution of algorithmic tasks on noisy intermediate scale (NISQ) devices will also be considered if a convincing argument for why the task is meaningful/impactful is presented. Manuscripts which identify important areas for improvement in quantum algorithm design, fault tolerance schemes, and quantum computing architectures or hardware are particularly encouraged.
Topics of particular interest for this Research Topic include, but are not limited to:
- Estimations for qubit counts and gate depths of specific quantum algorithm implementations.
- Studies analyzing resource requirements with respect to particular qubit technologies or architectures.
- Hardware-agnostic analysis and resource estimations for the implementation of quantum algorithms.
- Clifford+T optimization strategies and resource estimations for effective error correction decoding, quantitative analysis of execution of algorithms with specific error correction codes.
- Cooling power, space use, time-to-solution, or energy consumption estimations for the implementation of quantum algorithms.
Keywords: Quantum Computing, Quantum Fault Tolerance, Resource Estimation, Quantum Architectures
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
