We will solicit original research papers for inclusion in the IEEE Quantum Week proceedings. 

We seek contributions on, but not limited to, the following topics: 

1. Reinforcement Learning for Unitary Synthesis: Development of RL agents for decomposing arbitrary unitaries into discrete, hardware-native gate sets.

2. Hardware-Aware Circuit Optimization: Deep Learning models for reducing circuit depth, T-count, or CNOT density while accounting for specific qubit topologies and error rates.

3. Automated Ansatz Discovery: AI-driven search for problem-specific variational circuits, including Evolutionary Algorithms and Generative Models for VQE and QAOA. 

4. Neural Representations of Quantum Circuits: Innovative encoding schemes, such as Graph Neural Networks and Transformers, for processing quantum circuit structures. 

5. AI for Error Correction and Mitigation: Leveraging AI to design optimized surface code layouts or to discover novel error-mitigation strategies.

6. Generative AI for Quantum Coding: The use of Large Language Models and Diffusion Models for automated quantum code generation and transpilation. 

7. Reward Engineering: Strategies for designing efficient reward functions for high-dimensional quantum spaces, such as curriculum learning, and solutions for the sparse reward challenge. 

8. Benchmarking and Open-Source Platforms: Introduction of standardized datasets, ”Gym” environments for quantum RL, and software integration with major SDKs (Qiskit, Cirq, PennyLane).