quantum-driven-neuromorphic-million-qubit

name	quantum-driven-neuromorphic-million-qubit
description	Quantum-Driven Neuromorphic Computing methodology for million-qubit-scale workloads — synergistic integration of quantum computing and neuromorphic architectures for large-scale computational tasks.
trigger_words	["quantum neuromorphic","million-qubit","quantum-driven neuromorphic computing","neuromorphic quantum computing","large-scale quantum","quantum workload scaling"]
version	1
arxiv_id	2606.12968
arxiv_url	https://arxiv.org/abs/2606.12968
authors	["Adams Ivanov","Samer Rahmeh","Erick Giovani Sperandio Nascimento","Daniela Herrmann"]
submitted_date	2026-06-11T00:00:00.000Z
announced_date	2026-06

Quantum-Driven Neuromorphic Computing for Million-Qubit-Scale Workloads

Overview

Methodology for synergistically integrating quantum computing with neuromorphic architectures to handle computational workloads at the million-qubit scale. Addresses scalability challenges in both quantum computing (noise, coherence, error correction overhead) and neuromorphic computing (hardware constraints, training efficiency, network architecture design).

Core Methodology

1. Hybrid Architecture Design

Quantum Layer Integration:

• Use quantum processors for computationally intensive subtasks requiring quantum advantage
• Leverage neuromorphic processors for pattern recognition, temporal processing, and energy-efficient computation
• Implement quantum-classical interfaces via pulse-level control and neuromorphic spike encoding

Neuromorphic-Quantum Encoding:

• Convert quantum state information into spike-based representations
• Use spiking neural networks (SNNs) to process quantum measurement outcomes
• Implement spike-timing-dependent plasticity (STDP) for adaptive quantum circuit parameter tuning

2. Scalability Framework

Error Correction Integration:

• Embed quantum error correction (QEC) cycles within neuromorphic feedback loops
• Use SNNs for real-time syndrome decoding and error detection
• Implement neuromorphic-aware fault tolerance protocols

Workload Distribution:

• Partition computational tasks between quantum and neuromorphic processors
• Use quantum processors for sampling, optimization, and quantum simulation
• Use neuromorphic processors for classification, temporal reasoning, and adaptive control

3. Million-Qubit Scaling Strategy

Hierarchical Processing:

• Implement multi-level quantum processor arrays with neuromorphic coordination layers
• Use neuromorphic networks for inter-processor communication routing
• Optimize quantum circuit compilation via neuromorphic reinforcement learning

Noise-Aware Neuromorphic Feedback:

• Monitor quantum noise patterns via neuromorphic sensing layers
• Implement adaptive noise mitigation using spike-based feedback control
• Use neuromorphic networks for decoherence prediction and compensation

Key Components

Quantum Neuromorphic Interface

 :
     ():
        .quantum_processor = quantum_processor  
        .snn_controller = snn_controller  
        .spike_encoder = QuantumSpikeEncoder()  
        .error_detector = NeuromorphicQECDecoder()  
    
     ():
        
        spike_pattern = .spike_encoder.encode(workload.quantum_state)
        
        
        control_signals = .snn_controller.process(spike_pattern)
        
        
        quantum_result = .quantum_processor.execute(control_signals)
        
        
         .error_detector.detect_error(quantum_result):
            corrected_result = .error_detector.correct(quantum_result)
        
         corrected_result