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. The von Neumann bottleneck and the fragility of qubits necessitate a paradigm shift towards quantum neuromorphic architectures, where noise is leveraged as a resource for adaptation rather than suppressed as a error. Solid-state platforms, including superconducting circuits, quantum dots, and polaritonic systems, provide a hardware palette for prototyping artificial quantum neurons and synapses; tunable couplers exist, but quantum memristive devices and autonomous learning remain theoretical. Theoretical frameworks like quantum reservoir computing exploit a system’s natural dynamics for temporal processing, offering a path to quantum advantage that avoids the overhead of classical optimization loops. Quantum biology demonstrates that functional quantum effects can persist in warm, noisy environments, providing a design strategy for building robust quantum systems that manage decoherence through environmental engineering rather than complete isolation. The long-term vision is a scalable neuromorphic quantum computer whose fault tolerance could emerge from its adaptive network architecture, enabling real-time learning and operation in practical environments beyond isolated laboratory conditions.