From Error Correction to Engineered Noise: A Bio-Inspired Path to Scalable Quantum Computing

The von Neumann bottleneck and the fragility of qubits represent fundamental roadblocks to efficient and scalable computation.Confronting these twin crises requires more than incremental fixes; it demands a bio-inspired path forward that treats noise,dissipation and networked dynamics as design resources rather than enemies. Bio-inspired quantum computing adaptsprinciples from neuroscience and biology to quantum hardware and algorithms, embracing neuromorphic architectures in which qubits act as artificial neurons, learning paradigms from quantum reservoir and spiking networks, and design lessons from quantum biology that show how coherence and decoherence can be co-opted for function. Recent experimental progress across superconducting, semiconducting and photonic platforms has yielded prototypes of quantum neurons and tunablesynapses, and our comparative analysis of hardware and theory identifies a practical near-term trajectory—harness fixed high-dimensional dynamics with engineered couplers and classical readouts while developing autonomous, physics-embeddedlearning for the longer term. This convergence points beyond co-processors towards a new form of intelligent systems, capableof autonomous learning in real-world environments.