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Explore a 54-minute lecture examining how physical systems can perform computation through response to data-encoded stimuli, with particular focus on quantum computing challenges. Delve into the critical issue of sampling noise and its impact on computational accuracy and energy costs across physical systems, including fault-tolerant quantum hardware. Learn about the Resolvable Expressive Capacity (REC) metric for quantifying computational capacity under noise conditions, and understand how quantum measurement theory establishes fundamental limits on REC. Discover the concept of Eigentasks - functions that optimize accuracy under sampling noise - and their practical application in supervised learning on superconducting quantum processors. Examine recent advances in processing streaming data that overcome qubit coherence time limitations without requiring error correction, supported by experimental results from superconducting quantum processor implementations.
