What it is:

Hybrid computing is a revolutionary computational approach that integrates classical computing, quantum computing, and neuromorphic computing into a unified system. Each of these paradigms contributes unique strengths — classical systems handle general-purpose tasks efficiently, quantum systems excel at solving complex probabilistic problems, and neuromorphic architectures mimic the human brain to process information efficiently and intuitively.
This combination enables highly adaptive, efficient, and powerful problem-solving capabilities that transcend the limits of traditional computing architectures. Hybrid computing aims to achieve synergy between these paradigms, enabling tasks that would otherwise be computationally infeasible for classical systems alone.

Applications:

Hybrid computing has diverse real-world and research-driven applications across scientific, industrial, and technological domains:

1. Drug Discovery and Molecular Simulation: Quantum models predict molecular behavior, while classical systems optimize simulations for large datasets.
2. Climate Modeling: Neuromorphic components process complex climate patterns, while quantum modules handle stochastic modeling for precise predictions.
3. AI Training and Optimization: Quantum computing accelerates AI model training by solving optimization problems exponentially faster.
4. Cybersecurity and Cryptography: Hybrid computing systems develop next-generation cryptographic algorithms resistant to quantum attacks.
5. Financial Forecasting and Risk Analysis: Quantum algorithms paired with classical AI predict market trends more accurately.
6. Smart Infrastructure and IoT: Neuromorphic units handle low-power edge analytics while quantum systems perform centralized data optimization.

Future potential:

The future of hybrid computing lies in creating seamless interfaces between classical, quantum, and neuromorphic processors, allowing each to handle tasks aligned with their strengths.
By 2035, hybrid computing is expected to become the backbone of large-scale scientific and industrial computation, driving:

• AI–Quantum Fusion: AI systems trained and optimized by quantum processors for complex decision-making.
• Sustainable Computing: Energy-efficient models using neuromorphic design to minimize power consumption.
• Personalized Healthcare and Genomics: Faster data processing for real-time diagnosis and genetic sequencing.
• Next-Gen Cloud Infrastructure: Cloud computing will evolve into “quantum-aware” hybrid clouds capable of dynamically allocating tasks across different processor types.
  In the long run, hybrid computing will redefine scientific research, industrial design, and national-level technological capabilities, making it a cornerstone of the digital future.

Current Research Areas under “Hybrid Computing” Technology

Research in hybrid computing is multidisciplinary, covering both hardware integration and algorithmic innovations. Some major areas include:

1. Quantum-Classical Integration:
   Developing hybrid architectures that allow quantum processors to collaborate with classical CPUs and GPUs for faster problem-solving.
2. Neuromorphic-Quantum Interaction:
   Exploring how brain-inspired neuromorphic chips can interface with quantum computers for adaptive learning and dynamic data processing.
3. Quantum Machine Learning (QML):
   Using quantum algorithms to enhance traditional AI and deep learning models for faster training and improved generalization.
4. Hybrid Cloud Platforms:
   Building scalable frameworks that integrate quantum computing services into classical cloud infrastructures (e.g., hybrid quantum–cloud models).
5. Energy-Efficient Hybrid Architectures:
   Designing systems that combine low-power neuromorphic computation with high-speed quantum algorithms to reduce overall energy consumption.
6. Cross-Paradigm Programming Models:
   Developing new programming languages and compilers that can manage workloads across multiple computing paradigms seamlessly.
7. Hybrid Security Systems:
   Creating advanced cryptographic methods that use both classical and quantum approaches to ensure data integrity and post-quantum security.
8. Simulation and Optimization:
   Using hybrid computing models to simulate large-scale systems like materials, biological processes, and urban networks more accurately.

Key Journals that Accept Papers on Hybrid Computing Technology

Open Access Journals

1. Frontiers in Quantum Science and Technology
   • Focus: Quantum-classical integration, hybrid algorithms, neuromorphic computing.
   • Indexing: Scopus
   • Model: Open Access
2. Journal of Cloud Computing: Advances, Systems, and Applications
   • Focus: Hybrid cloud, energy-efficient systems, distributed quantum computing.
   • Indexing: Scopus
   • Model: Open Access
3. Computational and Mathematical Methods
   • Focus: Quantum-enhanced algorithms, hybrid AI systems.
   • Indexing: Scopus
   • Model: Open Access

Hybrid Journals

1. IEEE Transactions on Emerging Topics in Computing (TETC)
   • Focus: Hybrid system architectures, cross-domain computation.
   • Indexing: Scopus, CSI Tools recognized
   • Model: Hybrid
2. ACM Transactions on Quantum Computing (TQC)
   • Focus: Quantum-classical integration, computational frameworks.
   • Indexing: Scopus
   • Model: Hybrid
3. Future Generation Computer Systems (FGCS)
   • Focus: Hybrid cloud systems, AI–Quantum convergence.
   • Indexing: Scopus, Elsevier
   • Model: Hybrid

Paid/Subscription-Based Journals

1. Journal of Parallel and Distributed Computing (JPDC)
   • Focus: Parallel hybrid architectures, distributed AI.
   • Indexing: Scopus
   • Model: Subscription
2. Neural Networks (Elsevier)
   • Focus: Neuromorphic computing and hybrid cognitive models.
   • Indexing: Scopus, CSI-recognized
   • Model: Paid Access
3. Quantum Information Processing (Springer)
   • Focus: Hybrid quantum algorithms, security, and information theory.
   • Indexing: Scopus
   • Model: Paid Access

Summary

Type	Example Journals	Indexing	Model
Open Access	Frontiers in Quantum Science and Technology, Journal of Cloud Computing, Computational and Mathematical Methods	Scopus	Open
Hybrid	IEEE TETC, ACM TQC, FGCS	Scopus / CSI	Hybrid
Paid/Subscription	JPDC, Neural Networks, Quantum Information Processing	Scopus / CSI	Paid