Abstract: Agents often execute complex strategies - continually adapting their reactions to input stimuli to synergize with past actions. As society pushes to automate ever more complex tasks, the computational resource requirements of such agents is growing in tandem – contributing to chip shortages, and a growing energy footprint of such technologies. Indeed with the rapid advances in large language models, the memory resources costs required by such technologies has been doubling every 3-4 months and energetic costs are growing in tandem. In this talk we determine the fundamental limits on the amount of memory and energy required for executing a complex strategy classically. We demonstrate that a quantum agent can use less memory and lower energetic cost to execute such tasks, implying that it is more efficient to make decisions quantum mechanically.
More details: https://agenda.infn.it/event/42049/