Suggested Topics for Survey Papers:

1. Comparison of different programming languages for quantum computing development.
2. Overview of quantum software development tools and platforms for building quantum applications, such as IBM Qiskit, Microsoft Quantum Development Kit, and Google Cirq.
3. Analysis of the challenges and limitations of quantum compiler designs for large-scale quantum systems, and potential solutions for overcoming them.
4. Overview of quantum compilers and their role in translating high-level quantum programs to hardware-specific quantum instructions.
5. Comparison of different quantum compilation techniques for near-term and long-term quantum applications.
6. Overview of quantum computing architectures, including hardware and software components.
7. Comparison of different types of qubits, such as superconducting qubits, ion traps, and topological qubits.
8. Comparison of different approaches to building quantum simulators, such as analog and digital simulators.
9. Comparison of different approaches to building quantum computers, e.g., gate-based quantum computing and measurement-based quantum computing.
10. Comparison of different quantum computing hardware architectures and their potential for scaling up quantum systems.
11. Analysis of the challenges and limitations of current quantum computing technology and potential solutions for overcoming them.
12. Overview of quantum computing architectures for near-term and long-term applications, such as gate-based and annealing-based architectures.
13. Comparison of different approaches to building quantum communication networks, such as quantum repeaters and entanglement-based networks.
14. Analysis of the potential impact of quantum computing on cloud computing architectures and services.
15. Survey of quantum benchmarking and characterization techniques for evaluating the performance of quantum computing systems.
16. Comparison of different approaches to quantum software verification and validation.
17. Analysis of the potential impact of quantum architecture on quantum communication and networking.

1. Review of quantum error correction techniques and their potential impact on the development of practical quantum computers.
2. Comparison of different quantum error mitigation techniques and their effectiveness in reducing errors in quantum computing systems.
3. Comparison of different approaches to fault-tolerant quantum computing and their potential for scaling up quantum systems.
4. Survey of quantum error correction decoders and their implementation in practical quantum computing systems.
5. Analysis of the tradeoffs between decoding complexity, decoder performance, and hardware requirements in practical quantum computing systems.
6. Survey on the applicablity and possible extension of classical decoding algorithms for quantum error correction codes.
7. Comparison of different quantum error correction decoding techniques, such as minimum-weight perfect matching, lookup table decoding, and machine learning-based decoding.
8. Analysis of the tradeoffs between decoding complexity, decoder performance, and hardware requirements in practical quantum computing systems.

1. Overview of machine learning techniques for quantum error correction and fault tolerance.
2. Comparison of different approaches to using machine learning for quantum error mitigation and suppression.
3. Analysis of the potential impact of machine learning on quantum control and optimization.
4. Comparison of different approaches to using machine learning for quantum circuit design and optimization.
5. Survey of machine learning techniques for quantum state reconstruction and characterization.
6. Analysis of the potential impact of machine learning on quantum simulation and quantum chemistry.
7. Comparison of different machine learning techniques for optimizing quantum hardware components, such as qubit gates and control systems.

1. Analysis of the potential impact of quantum computing on drug discovery and material science.
2. Survey of quantum algorithms for specific applications, such as optimization, cryptography, and machine learning.
3. Survey of quantum simulation algorithms and their potential for simulating complex systems.
4. Survey of quantum-inspired classical algorithms and their potential for solving complex problems.

These are just a few examples of potential survey paper topics for a graduate course on quantum computing systems. Students can also tailor the topic to their specific interests and expertise within the field.