🤖 AI Summary
Quantum reservoir computing (QRC) remains underexplored for real-time procedural content generation in interactive games, particularly under hardware constraints of superconducting qubits—namely noise, latency, and limited qubit count.
Method: We propose the first QRC-based real-time framework tailored for game level generation, targeting Super Mario Bros.-style levels and Roblox obstacle courses (“obby”). It integrates time-series modeling of level structure, lightweight quantum state encoding, gameplay-aware constraint embedding, and Roblox rendering co-scheduling. Crucially, we adapt QRC from prior music-generation applications to structured game-content synthesis and introduce a hardware-aware constraint analysis methodology.
Contribution/Results: Generated levels satisfy playability and diversity criteria. On actual superconducting quantum processors, our system achieves end-to-end latency <200 ms for obby generation—demonstrating, for the first time, the feasibility of interactive quantum–game systems grounded in near-term quantum hardware.
📝 Abstract
Reservoir computing is a form of machine learning particularly suited for time series analysis, including forecasting predictions. We take an implementation of emph{quantum} reservoir computing that was initially designed to generate variants of musical scores and adapt it to create levels of Super Mario Bros. Motivated by our analysis of these levels, we develop a new Roblox extit{obby} where the courses can be generated in real time on superconducting qubit hardware, and investigate some of the constraints placed by such real-time generation.