Quantum neuromorphic computing: harnessing noise and adaptation for scalable quantum processors
Abstract
The von Neumann bottleneck and qubit fragility limit scalable computation. This review article synthesizes recent progress in a bio-inspired quantum approach that treats noise, dissipation, and dynamics as resources, drawing from neuroscience, neuromorphic design, and quantum biology. We survey the experimental state of the art: proof‑of‑concept components (tunable couplers and qubit excitations) have been demonstrated across superconducting, semiconducting, and photonic platforms; fully autonomous quantum learning remains theoretical. Near-term strategies harness fixed high-dimensional dynamics with engineered couplers, while long-term visions aim for autonomous, physics-embedded learning. By organizing and critically comparing these developments, we chart the emerging field of quantum neuromorphic computing and identify key challenges and opportunities on the path toward scalable, adaptive quantum processors.
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