DEV Community: bhagvan kommadi

Quantum Computing for Data Center AI Workload

bhagvan kommadi — Sun, 04 Oct 2026 06:22:27 +0000

Integrating Quantum Processing Units (QPUs) into classical AI data centers expands quantum machine learning beyond theoretical algorithms to address infrastructure-level engineering, rack-scale scaling, and energy efficiency.

As hyperscale data center rack densities climb from $10 to well over $100 for pure GPU/TPU clusters, incorporating quantum accelerators opens up non-obvious paradigms for hybrid classical-quantum data center architectures.

Key Research & Architectural Directions

                     +-----------------------------------+
                     | Hyperscale AI Ingress & Scheduling|
                     +-----------------------------------+
                                       |
                   [ Dynamic Quantum/Classical Router ]
                                       |
              +------------------------+------------------------+
              |                                                 |
   +-----------------------+                         +-----------------------+
   | Classical GPU Cluster |                         | Hybrid QPU Rack       |
   +-----------------------+                         +-----------------------+
   | - Heavy Matrix Ops    |                         | - Quantum Interconnect|
   | - Token Generation    |                         | - Photonic Optical Inter
   | - Gradient Steps      |                         | - Superposition Sampling
   +-----------------------+                         +-----------------------+
              |                                                 |
              +------------------------+------------------------+
                                       |
                     +-----------------------------------+
                     | Photonic Switch & Memory Fabric   |
                     +-----------------------------------+

1. Co-Located QPU-GPU Optical Interconnects (Sub-Nanosecond Hybrid Offloading)

Problem: Quantum processors typically operate as remote cloud endpoints over standard PCI/Ethernet or REST APIs. The latency of classical-to-quantum serialization (>10 ms )destroys the throughput gains needed for real-time model training or dynamic scheduling.
Research Idea: Direct Optical Interconnects between Photonic/Cryogenic QPUs and GPU Tensor Fabrics. Design optical switching backplanes (e.g., using co-packaged silicon photonics) that bridge classical GPU memory directly to quantum optical registers.
Key Mechanism: Enables GPUs to trigger quantum co-processing tasks (such as sampling complex Gibbs distributions or evaluating quantum loss metrics) via unified memory address spaces without crossing traditional PCIe networks.

2. Quantum Annealing & VQC for Data Center Power & Thermal Balancing

Problem: Modern AI clusters face severe thermal throttling, transient power spikes during large-scale pre-training runs, and uneven rack power distribution across megawatt-scale facilities.
Research Idea: Real-Time Microsecond Power & Thermal Co-Optimization using Quantum Annealers. Train a quantum optimization coprocessor directly integrated into the data center’s power management fabric.
Key Mechanism: Classical heuristic solvers (e.g., simulated annealing) take seconds to compute optimal voltage/frequency scaling and workload migration across GPUs. A dedicated quantum annealer or QAOA circuit evaluates non-convex combinatorial energy maps in sub-milliseconds, adjusting rack power draw dynamically to eliminate thermal hotspots before silicon degrades.

3. Quantum-Enhanced Data Compression & Memory Fabric Optimization

Problem: Standard KV-cache accumulation and trillion-parameter model weight movement create massive memory bandwidth bottlenecks, leading to severe power consumption in optical/copper interconnects.
Research Idea: Quantum Random Access Memory (QRAM) Vector Quantization for KV-Cache Retrieval. Utilize quantum state prep circuits to perform approximate nearest-neighbor (ANN) searches and lossy vector compression directly on high-dimensional KV-caches.
Key Mechanism: Maps $d$-dimensional continuous key-value embeddings into quantum superposition states. Querying the state requires O(log d)operations rather than classical O(d) dot products, drastically lowering memory-bus toggles and energy consumption per retrieved context window.

4. Synthetic Training Trajectory Generation via Quantum Generative Models

Problem: Synthetic data generation using classical LLMs is slow, energy-intensive, and prone to mode collapse when creating edge-case multi-agent scenarios.
Research Idea: Quantum Circuit Born Machines (QCBMs) for True Entangled Synthetic Data Pipelines. Implement hybrid quantum-classical generative algorithms (using parameterised quantum circuits) as hardware-level synthetic data generators co-located within the training pipeline.
Key Mechanism: Quantum circuits leverage true physical randomness and entanglement to sample heavy-tailed distributions and complex combinatorial edge cases that classical pseudorandom generators struggle to represent, enriching pre-training corpora without invoking multi-billion parameter autoregressive models.

System Integration Breakdown

Research Frontier	Classical Data Center Bottleneck	Quantum Acceleration Mechanism	Data Center KPI Impact
Cluster Interconnects	High latency & electrical resistance of copper/PCIe buses.	Silicon Photonic Quantum Switches & direct QPU-GPU memory mapping.	Sub-microsecond offloading latency, dramatically reduced I/O energy.
Facility Management	Non-convex power spiking & localized GPU thermal throttling.	Continuous QAOA/Quantum Annealing for load-balancing across racks.	15–25% reduction in cooling energy, eliminated thermal throttling drops.
Context Memory Scaling	Linear bandwidth explosion (O(N)) from huge KV-caches.	QRAM-style quantum state search (O(log N)) & state vector superposition.	Sub-linear memory access energy; expanded context capacity per node.
Model Pre-Training	Massive energy consumption during classical Monte Carlo / Synthetic generation.	Quantum Circuit Born Machines (QCBM) sampling multi-variable joint distributions.	Lower Joules-per-token cost for generating high-entropy synthetic data.

Self Aware & Self Defending Networks

bhagvan kommadi — Sat, 03 Oct 2026 14:18:36 +0000

Self-Aware, Self-Defending Adaptive Network is a network that protects itself from security attacks in smart cities implementation.An intelligent agent that learns and understands the threat level posed by attack across a smart city network. The AI system uses a new form of machine learning to monitor every detail of a network to identify and isolate security threats. The threats are such as malware, application high-jacking, sabotage and illicit access, hacking and unauthorized use. An autonomic system is composed of ensemble of autonomic components which are dynamically added and removed. Nodes within such an ensemble should cooperate to achieve system goals. These systems are expected to self-adapt with little or no human-interaction. Self-adaptive network modifies its own behavior in response to changes in its operating environment. By operating environment, we mean anything observable by the network, such as user interaction, network devices and sensors, or instrumentation.

Introduction

Adaptive network based on 5g with AI is used for automated service provisioning. The network providers automate manual service lifecycle processes. These process are automated using in packet/optical networks. Packet/Optical network are built using an software defined networking based automation platform. The automation platform is multi-layer and multi-vendor based which adopts DevOps processes. For instance, a network provider can automate the delivery of its wavelength services and plans. They can automate to extend this platform to other services using a phased approach.

Proactive network assurance is another area where AI based adaptive network can be used. The network providers want to identify and correct as many network issue that they can foresee and predict. This helps in increasing network reliability and deliver with specified SLAs. AI based adaptive platform improves the customer experience. This platform will have features related to pre-emptive network maintenance across the optical, Ethernet and IP Wide Area Networks. AI based automation platform will have the network health prediction capabilities. Along the same lines, Machine learning based analytics can predict the likelihood of a network node’s failure in a given timeframe for repair.

AI based Adaptive network can help in Fiber capacity analysis and optimization. Policy based matching of channel and wavelength capacity improve the efficiency and adaptive planning of optical networks. Providers can predict signal variability by combining real-time network telemetry data and traffic forecasting with AI based predictive analytics. This helps in improving the system margin utilization and reduce cost-per-bit.

AI-Defined Infrastructure

AI Defined infrastructure can manage planning, build, run and maintain tasks. In Planning tasks, AIDI is used for analyzing the demand trends and predicting the infra requirements. Using the requirements, planning can be done appropriately. We can also ensure the infrastructure is according to the requirements.

In build task, the necessary resources can be deployed as per the workload requirements. Resources can be deallocated when there is no need. The infrastructure components can be configured easily. In the run and maintain tasks, AIDI can be used to analyze the data patterns. The data patterns help in indicating the behavior of the system. The behavior of the system helps in making the model of the system behavior. AI based training helping in building this model with quality parameters. The quality parameters which are used for the model are availability, scalability and storage.

The anomalies in the network can be identified by the AIDI based platform. intrusion detection, fraud points, fault points, infrastructure abuse and failure are the anomalies identified. The platform can detect the threat and act to rectify and fix the problem. It has features to react or proactively act based on the single or group of infrastructure components. Errors can be fixed completely by autonomous actions. AIDI helps in reducing the cost of IT infrastructure. The cost is reduced by using the most optimal components.

Networking Challenges

Network providers are now rethinking about their operations with Artificial Intelligence. AI can be used to achieve the long desired goal of end-to-end automation. Automation might remove humans from the equation. The network providers want their networks and operations to become adaptive. This is to respond to a changing competitive landscape and consumer demands. These demands require a coherent combination of human-controlled and supervised automated operational processes. They might also need analytics-driven intelligence, and a programmable infrastructure.

The evolution to 5G and IOT adoption is putting massive pressure on today’s networks. There is need to increase the capacity by orders of magnitude. On the related front, the networks need to have the ability to respond to unpredictability in traffic patterns.The optical network which is at the heart of communications helps in interconnecting people, data centers, and devices in the network. The network need to meet today’s web-scale demands.

Operators are having challenges in handling bandwidth demands. They are managing the demands by deploying, managing, and sparing different hardware. They are using cost-optimized solutions per specific application. They select the hardware based on the best-guess fiber characterization data. Lack of network visibility and efficiency is forcing operators to operate at suboptimal capacity. These factors are making the operators lose revenue resulting in costly network overbuilds.

Adaptive Networks - 5g

Adaptive Network platform based on AI will have three important components which are software control, programmable infrastructure and analytics driven intelligence. The software control forms the basis of adaptive operations. The basis is supported by the automated creation and deployment of network services. These network services are deployed for scale and speed using software defined network, Network functions virtualization and open APIs.

The programmable infrastructure is a hybrid future generation network. It comprises of open, software defined network enabled physical networks and cloud-native virtual network functions. It will provide advanced telemetry that delivers real-time data on the health of the network. The programmable infrastructure will provide the ability to match the changing capacity needs.

Another component named Analytics-driven intelligence enables intelligent automation. The intelligent automation enhances autonomous decision making and supporting software-control. They can achieved through policy management, rule engines, AI, machine learning and telemetry. We need to have a robust storage repository. The repository will record, process and aggregate real-time and historical large-scale and raw data streams. The data streams such as log files and telemetry data are recorded in the repository. The raw data will be processed, normalized and used for advanced data models and analytics algorithms. These algorithms are used to generate actionable insights.

Network providers will apply different kinds of machine-learning techniques. These techniques are based on the operational use cases and benefits. The techniques used are supervised learning, reinforced learning and unsupervised learning. Supervised machine-learning based algorithms are trained to identify patterns such as degrading network performance. They can also be used to predict an outcome like port failure and trigger remediation actions such as auto-adjust network bandwidth and add new capacity. This technique is commonly used and suitable for use cases in which historical data and outcomes are known. Reinforced learning involves continuous calibration of these algorithms based on previous feedback on actions. Unsupervised learning algorithms use grouping and clustering techniques to organize data. This helps to understand the structures and enable the discovery of patterns. The patterns discovered are related to previously unknown and unnoticed scenarios such as identify new user, service traffic behavior and profiles. These patterns are used to improve forecasting in network planning.

Self-Aware, Self-Defending Adaptive Network

Self-Aware, Self-Defending Adaptive Network system is intelligent agent based and monitors network activity, content and behavior. Network activity, content and behavior based information is used identify and counteract different forms of cyber threats.

The intelligent agent framework learns and understands the threat level posed by node in a network. The adaptive network platform uses machine learning techniques to monitor the network to identify and isolate cyber security threats. The security threats identified are malware, application high-jacking, sabotage and illicit access, hacking and unauthorized use. It enables make all assets self-aware, self-protecting and adaptive to any threat which is external or internal. This approach eliminates the chance for zero-day attacks. This is because, the platform can detect anomalous packet behavior and content. The self learning adaptive network system learns through use and becomes intelligent over time.

The self aware, self defending adaptive network system can recognize every packet’s behavior and content. The behavior and content is used to determine if the pattern conforms to expectations or is anomalous. This helps in deciding to check and find if it is a threat. This adaptive and associative network detects the relationship of every byte in the system. It is capable of identifying known threat patterns. It can identify and isolate anomalous patterns. The anomalous patterns which are identified might be related to a zero-day attack, non-compliant use of the network or a sabotage.

To illustrate a self learning adaptive network, we look at human neuronal network of the neocortex. This biologically inspired intelligent framework operates like a human brain. The human brain can learns autonomically by detecting patterns at the moment of stimulation. The adaptive network stores each unique byte pattern like human brain. Each time the pattern is detected, the values such as time of stimulation, place of stimulation, syntax of patterns, packet payload and addressing are stored. The stored data model has an n-dimensional representation of the semiotic value of every pattern.

Intent Based Networks

Intent Based Networks are used to capture the intent related to business. This helps in bridging the gap between business operations and IT. The benefits of intent based networking are related to the ability to automate network management and orchestration in an intelligent manner. They represent a solution set which is the confluence of a key technologies such as machine learning, Artificial Intelligence, Software Defined Networking and Internet of Things technologies.

Conclusion

Cognitive Network Management is the emerging network infrastructure technology. Network operators have deployed and operated network on 5g with existing Self-organizing Network technologies. Artificial Intelligence mixed with Software Defined Networking and advanced analytics, takes the autonomous, intelligent network operation and control to next level. While AI has not yet made extensive inroads into the realm of networks, cellular systems in particular will soon achieve early and extensive success through the combination of Machine Learning, SDN, and advanced data analytics technologies.

The potential economic and social benefits can be achieved because of self-awareness, self-configuration, self-optimization, self-healing, and self-protection. We might realize these benefits during the implementation of 5G networks. The network systems can be integrated with technologies such as Machine Learning, Software defined networking, Network Function Virtualization, Network Slicing, Quality of Service Management, and emerging security methodologies.

Today’s networking challenges

Network providers are now able to rethink their operations with AI to achieve their long-desired goal of end-to-end automation, but most of them do not want to cede control to networks that decide their own direction and remove humans from the equation altogether. Most network providers want their networks and operations to become more ‘adaptive’ to respond to an ever-changing competitive landscape and consumer demands, which requires a coherent combination of human-controlled and -supervised automated operational processes, analytics-driven intelligence, and an underlying programmable infrastructure.

Network operators know it all too well: Streaming video, cloud computing, the Internet of Things (IoT), and the evolution to 5G are putting massive pressure on today’s networks, requiring capacity increases by orders of magnitude and the ability to respond to even greater unpredictability in traffic patterns.

The optical network sits at the heart of communications, interconnecting people, data centers, and an increasing number of devices across any distance, from across the street to across the ocean. Yet, for all the critical functions and agility they need to provide to meet today’s web-scale demands, most networks are weighed down with manual processes and hardware inflexibility.

Operators are working to keep up with bandwidth demands by deploying, managing, and sparing different hardware for different areas of the network using cost-optimized solutions per specific application. They select their hardware based on upfront link engineering determined with best-guess fiber characterization data. Lack of network visibility and little hardware flexibility limit network efficiency, forcing operators to operate at suboptimal capacity, leaving revenue on the table and resulting in costly network overbuilds. With the speed at which technology shifts are occurring in the industry, using this operating model is no longer an option.

New technologies promise to drive discontinuity in both cost and power reduction. Higher baud rate, programmable, coherent technology can reduce transport costs and put operators within reach of their business goals. But if operators cannot get accurate, real-time link data from the network to determine the right channel capacity rate at any point in time, they cannot take advantage of the savings associated with the new technology.

What if the network could self-monitor and adapt to application demands in real time, adjusting capacity across paths as needed based on traffic requirements and system margin? Evolving to such an autonomous network would drive new levels of efficiencies and speed in achieving business goals.

autonomous network

lays the foundation for tomorrow’s self-driving, autonomous networks. With AI, operators can take advantage of improved transport economics, gain new insights and control into the efficiencies of their network, and use new levels of automation to compete and win in the new on-demand economy.

For the operator to be able to optimize network capacity, the system itself must be able to monitor and gather critical information in real time. Using deep, systems-level expertise, platform has embedded unique, real-time link monitoring capabilities, never before available, into Ai. Operators can now understand exactly how much margin is currently present in the network, as well as the optimal capacity they can deploy.

Introducing a new paradigm for optical networks, i provides operators with new visibility into and control over the efficiency of their networks. For the first time in optical networks, users will be able to access real-time link monitoring information to determine the optimal capacity for each channel—across any path, for any network fill—and tune to match that capacity with a single technology that can address any application, from metro Data Center Interconnect (DCI) to trans-Pacific submarine.

Adaptive Network

An adaptive network is a network that can self-configure, self-monitor, self-heal and self-optimize by constantly assessing network pressures and automatically reallocating resources, but is bound by the rules and policies set by the network provider and is under constant human supervision. These programmable networks will use continuous learning and optimization to dynamically adapt to changing service demands and traffic patterns; help network providers to reduce costs by enabling high levels of process automation and enhanced, AI- and machine learning (ML)-led and assisted decision making; and provide a high-quality customer experience with more predictive and proactive operations and differentiated SLAs.

AI-driven autonomous networking is a high-profile topic among network providers and their suppliers. AI/ML will be central to providers’ efforts to create more-agile and leaner network operations, but that alone will not solve providers’ critical networking challenges. An adaptive network embraces AI with human oversight and uses it in combination with a software-control layer and a programmable infrastructure (see section 3). These networks will put providers in control, rather than asking them to relinquish control to AI, by allowing providers to decide the strategic direction; set the constraints on the autonomous decision making with rules and policies based on business and operational objectives; and supervise autonomous processes and intervene as necessary. Overall, they will enable providers to complement human intelligence with artificial intelligence, where the strengths of one will compensate for the weaknesses of the other, and respond quickly and cost-effectively to customer demands and the competitive landscape.

Automating networks and their operations is not a new idea; providers have been pursuing the autonomous network vision for years. Many providers have adopted various tools and technologies to apply software- controlled, automated processes in various operational scenarios to certain degrees, such as for fault/alarm management, traffic management and RAN optimization, and more recently for Layer 1–3 service provisioning with software defined networking (SDN). However, these have typically been tactical solutions that have been implemented as disjointed, fragmented ‘automation islands’ for specific domains and services. Therefore, the overall level of automation in operations remains low, a long way from the desired goal of end-to-end automation and simplification across multiple networks and services.

Network providers are now presented with the opportunity to change this. The main building blocks of an adaptive network, such as SDN/NFV-based software control and automation, enhanced analytics-driven intelligence with AI and machine learning, and more-programmable network infrastructure (see Figure 1 and section 3 for detailed discussion of these components), are now available. They will enable providers to adopt a more strategic, embedded automation approach to make the network truly adaptive.

The Adaptive Network is a new approach that expands on autonomous networking concepts to transform the static network into a dynamic, programmable environment driven by analytics and intelligence.

Since the introduction of the first Public Switched Telephone Network, networks have continually evolved. Through the various stages of development—from fixed endpoints in the early Internet to today’s broadband networks that connect mobile users to massive data centers and bandwidth behemoths like Netflix, Amazon, and Facebook—networks have adjusted to accommodate new demands.

The once-static infrastructure is undergoing a more profound transformation than ever before. The latest incarnation is autonomous networking, which is a trend that has been building for some time. The autonomous network runs without much human intervention. It can configure, monitor and maintain itself independently.

But, even though it’s a significant advance, autonomous networking is still too restrictive and too rigid. So platform has defined a new approach to the evolution of networking—the Adaptive Network—that’s geared toward providing a network that can grow with a company as its business needs and markets change.

The Adaptive Network is remaking the network into a dynamic, programmable infrastructure built on analytics and automation.

The Adaptive Network allows providers to evolve their current infrastructures into more of a communications loop that relays information from network elements, instrumentation, users, and applications to a software layer for review, analysis, and action—rather than bogging down the network itself.

The Adaptive Network includes three important layers:

Programmable infrastructure: This includes the network’s physical and virtual elements, as well as the telemetry gathered from them. The programmable infrastructure layer is highly intelligent and interprets data so the network can make decisions—whether that means routing traffic around a circuit that's down or investigating and correcting an issue with latency or lower-than-expected capacity on a specific link. Programmable infrastructure requires a flexible grid; a reconfigurable photonic layer to give the ability to reroute channels of variable spectral occupancy across any path, and across any optical spectrum in the network; and telemetry from the IP layer correlated with routing data. In addition, a programmable infrastructure needs tunable coherent transponders to efficiently map a flexible number of client signals to the variable line capacity. In turn, that requires a centralized purpose-built Optical Transport Network (OTN) or packet switching architecture.

Analytics and intelligence: The programmable infrastructure produces significant amounts of data. Some of it is big data that indicate trends that the network learns and adjusts for over time. Big data can inform the network on how to adjust in the long term, which traffic patterns to look out for, and which parts of the network could be vulnerable. Then there’s small data—things that are happening at a fairly rapid pace. It could be a flicker on a circuit or an immediate request from a customer. Such events require a speedy response from the network, and those moves will be made by the analytics. But once the decisions have been made, a human operator or pre-defined policies could step in and approve or change things as necessary. In a truly autonomous network, there would be no operator influence at this point.

Software control and automation: Research shows the undisputed number one cause of network outages is human error, with estimates as high as 32 percent, according to Dimension Data's 2014 Network Barometer report. Effective automation of network tasks, such as loading access controllers and provisioning routers, or automated calculation and configuration of TE tunnels to optimize traffic and relieve congestion, can eliminate those errors and keep the network running at peak performance. The ability for automation to work across multiple vendors is critical. Some technologies are good at working with one set of devices from a single vendor, but few networks are built on a single vendor’s gear. Networks have to interoperate, using APIs, to function efficiently and move data efficiently and swiftly from point to point.

The development of the Adaptive Network is a watershed moment for the networking world. It’s a cohesive evolution that supports all aspects of intelligent automation—such as intent-based orchestration, analytics, and programmable domain control. It’s a microservices-based architecture that delivers extensibility and scale. Plus, it takes a DevOps integration approach to provide operational and service agility.

The Adaptive Network is a new approach that expands on autonomous networking concepts to transform the static network into a dynamic, programmable environment driven by analytics and intelligence.

Self-Aware, Self-Defending Adaptive Network Appliance Software (SASDANAS) system that acts as an intelligent agent to monitor network activity, content and behavior to augment the capacity of human analysts to identify and counteract all forms of cyber threats.

SASDANAS is an intelligent agent that learns and understands the threat level posed by every byte-pattern across a network. The software system uses a new form of machine learning to monitor every detail of a network to identify and isolate cyber security threats – including malware, application high-jacking, sabotage and illicit access, hacking and unauthorized use. It enables the Air Force to make all cyber assets self-aware, self-protecting and adaptive to any external or internal threat. The approach eliminates the opportunity for zero-day attacks because it detects all anomalous packet behavior and content. Furthermore, SASDANAS provides the Air Force with a first-mover advantage as the system learns through use and thus becomes more intelligent over time.

SASDANAS is a 64-bit multithread, massively parallel application that is deployable through a REpresentational state transfer (REST) architecture. Each instance of SASDANAS may be deployed in series and/or in parallel. This architecture provides the USAF the greatest degree of flexibility when deploying into field operations. This approach enables the USAF to use SANDANAS in either: a) moving-windows approach to read every packet as it flows across the network; or, b) identifying threats by capturing an image of the topology of network at byte- or packet-level of detail to understand the behavior and content of network. Each instance of SASDANAS will have the capacity to understand up to 18 exabytes of data at a time. Speed of SASDANAS is dependent on available memory and processing capacity. When deployed in parallel, SASDANAS has the theoretical capacity to monitor the activity of the entire Internet.

Unlike current approaches to cyber security, SASDANA uses a new technology called a HoloSemantic DataSpace (HSDS) to detect, classify and store every byte pattern. The HSDS is thus able to recognize every packet’s behavior and content to determine if the byte-pattern conforms to expectations or is anomalous and therefore subject to further scrutiny to determine if it is a threat. The HSDS is an adaptive, associative network that detects the relationship of every byte that is fed into the system. Thus, the HSDS is capable of identifying both known threat patterns while concurrently identifying and isolating anomalous patterns that may signify a zero-day attack or non-compliant use of the network (e.g., sabotage).

The HSDS is a newly discovered form of neuronal network that mimics the neurophysiology of the neocortex. It is commercially trademarked as a “biologically inspired intelligence” and operates similar to a human brain. It learns autonomically by detecting byte-patterns at the moment of stimulation. The HSDS stores each unique byte pattern only once regardless of how many times it encounters that specific pattern. It registers and adjusts the semiotic generic zoloft cost value for each byte pattern each time it is stimulated – adjusting the size of the net automatically. It determines the semiotic value for each byte pattern with the following dimensions, each of which may have many values: time of stimulation, place of stimulation, syntax of surrounding byte patterns, and packet payload and addressing. Thus, the HSDS creates an n-dimensional representation of the semiotic value of every byte-pattern; thereby capturing every detail within the complexity of data.

Cognitive Network Management represents one of the most important emerging network infrastructure opportunities. Network operators have made great strides with existing Self-organizing Network (SON) technologies. However, Artificial Intelligence (AI), in combination with Software-Defined Networking (SDN) and advanced analytics, is poised to take autonomous, intelligent network operation and control to an entirely new level. While AI has not yet made extensive inroads into the realm of networks, cellular systems in particular will soon achieve early and extensive success through the combination of Machine Learning, SDN, and advanced data analytics technologies.

The potential economic and social benefits cannot be overstated as networks will achieve and entirely new level of self-awareness, self-configuration, self-optimization, self-healing, and self-protection. This will be a benefit for existing networks as well as evolved LTE, emerging Internet of Things (IoT) systems, and soon to be launched 5G networks. An interdisciplinary approach will be required for systems integration as many technologies are brought together including Machine Learning, SDN, Network Function Virtualization (NFV), Network Slicing, Quality of Service Management, and advanced security methodologies.

Intent Based Networks capture business intent, and in so doing, bridge the gap between business operations and information technology. The benefits of intent based networking are many and varied, but arguably all improvements stem from the ability for IBN to automate network management and orchestration in a proactive and intelligent manner. Accordingly, intent based networking represents a solution set that is at the confluence of a few key technologies including machine learning (and other forms of AI), SDN, and IoT technologies.

This research evaluates intent based networking including comparison of IBN with traditional networking in terms of architecture, capabilities, and benefits for carrier and enterprise networks. The report analyzes technologies, infrastructure, and the impact of implementing intent based networking in conjunction with other emerging technologies such as Multi-access Edge Computing (MEC), 5G, AI, and IoT. The report also evaluates leading vendors, strategies, and solutions.

.An adaptive network will be based on hybrid, programmable infrastructure comprising physical and virtual network resources across the WAN and provider data centers that are managed and orchestrated by the software control layer.
Traditional provider WANs are highly stable and complex networks with multiple layers, protocols and services. In particular, optical transport networks are typically configured statically and engineered using worst- case, full-fill, end-of-life conditions. However, rapid and unpredictable growth in capacity requirements and competitive concerns require using network assets as fully as possible with minimal stranded capacity. Customer demands, too, push the need for more flexible, on-demand connectivity services enabled by more dynamic transport network architectures. Providers need infrastructure that can self-configure and self-optimize to meet the demands of existing services (cloud services, high-quality video, mission-critical enterprise services) and rapidly adapt to make way to future services (5G, network slicing, IoT/M2M).

Use cases:

•

ADI enhances a SDI with the necessary sophisticated algorithms, machine learning and artificial intelligence – fueling the SDI with intelligence. An ADI allows a SDI to build and run self-learning respectively self-healing infrastructure environments. Thus, without human interaction AI-defined IT infrastructure environments are capable of:

deploying the necessary resources depending on the workload requirements as well as de-allocating the resources when they are not needed anymore.

constantly analyzing the ever-changing behavior and status of every single infrastructure component and thus understanding itself.

reacting or proactively acting based on the status of single infrastructure components by autonomously taking actions and thus leading the entire infrastructure into an error-free status.

An AI-defined infrastructure cannot be compared with classic automation software, which typically works with predefined scripts. An ADI utilizes a company’s existing knowledge executing it automatically and independently. However, like every new born organism an ADI needs to be trained but afterwards can work autonomously. Thus, based on the learned knowledge, disturbances can be solved – even proactively for not expected events by connecting appropriate incidents from the past. Therefore, an ADI monitors and analyzes all responding components in real-time to identify and solve a problem based on its existing knowledge. The more incidents are solved the bigger the infrastructure knowledge gets. The core of an ADI is a knowledge-based architecture that can analyze incidents and changes and autonomously develop strategies to solve an issue.

Furthermore, an AI-defined infrastructure embraces communities to:

consume the knowledge from external experts to become more intelligent.

connect with other ADI environments to link, combine and share their knowledge base.

constantly expand the knowledge pool.

optimize the knowledge.

All in all, an ADI is an intelligent system that – initially fueled with external knowledge – can learn and make decisions autonomously without human interaction.

ADI is only a single piece of the entire AI-defined enterprise stack

An ADI is an essential part of today’s IT operations building the foundation for the AI-enabled enterprise. However, first and foremost it enables IT departments changing the infrastructure behavior from a today’s semi-dynamic to a true real-time IT environment.

This autonomous way of planning, building, running and maintaining the entire infrastructure let IT operations and developers deploy IT resources like server, storage, network, databases and other ready services in the most efficient way – by using the knowledge of more than just one expert but the entire IT operations team. Furthermore, IT operations are being transformed from pure consumers of resources to orchestrators respectively managers of a completely automated and intelligent IT stack. The very foundation of an end-to-end AI-ready enterprise.

Python For Fintech

bhagvan kommadi — Sat, 03 Oct 2026 14:17:44 +0000

Please check out my latest book : Python For Fintech
https://www.amazon.in/Ultimate-Python-Fintech-Solutions-Applications/dp/8197256209

CLM Research ideas

bhagvan kommadi — Fri, 02 Oct 2026 05:50:12 +0000

Recent research in Contrastive Language Models (CLMs) and System 1 Decision Models (e.g., Stanford/NVIDIA’s CLM-8B, TypeSafe AI's Jev, laya, openjev) marks a departure from generative autoregressive LLMs for step-by-step decision-making.

Instead of generating text token-by-token (a slow "System 2" decoding loop), these non-generative models treat action execution as a state-action embedding matching problem. A unified encoder embeds the current environment state, and task-specific projection heads score candidate actions, tool selections, or discrete choices in a single forward pass.

Core Research Themes & Novel Paper Ideas

                    +--------------------------------+
                    | Current State & Environment Context |
                    +--------------------------------+
                                   |
                         [ Frozen Shared Encoder ]
                                   |
                    +--------------------------------+
                    |   Latent State Representation  |
                    +--------------------------------+
                        /          |          \
                       /           |           \
           +----------+      +-----------+      +----------+
           | Choice   |      | Noul      |      | Score    |
           | Head     |      | Head      |      | Head     |
           +----------+      +-----------+      +----------+
           | Pick 1-of-N     | Truth Prob|      | Ranked   |
           | Actions  |      | Scoring   |      | Rubrics  |
           +----------+      +-----------+      +----------+

1. Calibration & Uncertainty Estimation in Non-Generative Decision Heads

Background: Empirical evaluations of CLM-8B and open models show that softmax probabilities across candidate actions often suffer from miscalibration across distinct domains.
Research Idea: Conformal Prediction & Out-of-Distribution (OOD) Fallbacks for Single-Pass Decision Models. Develop a lightweight conformal inference layer on top of CLM prediction heads (e.g., Choice or Noul heads) to output valid prediction sets with formal statistical coverage guarantees. If the prediction set contains multiple competing choices (high ambiguity), the system routes control to an autoregressive model (System 2 LLM).

2. Hierarchical Control Architectures (Hybrid System 1 / System 2)

Background: High-rate agents (e.g., computer use, autonomous tactical robotics, real-time market makers) require low-latency judgment (sub-10ms to 50ms).
Research Idea: Asynchronous Dual-Process Agent Control via Latent State Caching. Investigate a decoupled framework where a slow, generative LLM generates macro-level plans, contextual constraints, and candidate action spaces asynchronously, while a cached CLM encoder/head executes micro-decisions at high frequency (10–50 Hz). Research focuses on dynamic KV-cache alignment and real-time state invalidation techniques when environment conditions drift.

3. Contrastive Objective Design for High-Cardinality Action Spaces

Background: Standard contrastive loss functions perform well when ranking small candidate sets ($N less than 20$), but experience performance degradation when scaled to massive candidate sets (e.g., selecting across thousands of tools, APIs, or schema elements).
Research Idea: Hierarchical Contrastive Embeddings for Dynamic API & Tool Routing. Propose a multi-stage tree-structured contrastive loss that performs coarse-to-fine action scoring. Evaluating the scaling laws of contrastive loss versus hard-negative mining strategies when indexing thousands of tool schemas simultaneously.

4. Synthetic Trajectory Generation & Post-Training Alignment

Background: Training CLMs requires diverse, high-quality execution trajectories containing state-action pairs, synthetic hard negatives, and rubric scores.
Research Idea: Automated Hard-Negative Trajectory Synthesis for Robust System 1 Decisions. Formulate self-supervised methods to generate counterfactual trajectories ("near-misses" and subtle operational errors) from agent execution logs. Researching how contrastive fine-tuning on counterfactual state representations mitigates overconfidence in edge-case decision states.

Methodological Summary

Research Area	Primary Challenge	Proposed Solution / Direction	Evaluation Metrics
Model Calibration	Overconfidence in incorrect decision states.	Split conformal prediction & OOD routing to LLMs.	Expected Calibration Error (ECE), Selective AUROC, Fallback Precision.
High-Rate Control	Latency of autoregressive decoding loops.	Dual-process caching: Slow System 2 planner + Fast CLM executor.	Latency per step (ms), Trajectory completion rate, Compute footprint.
High-Cardinality Routing	Degradation when selection sets exceed 100+ choices.	Coarse-to-fine hierarchical heads with specialized hard-negative mining.	Top-$k$ recall, Latency scaling ($O(\log N)$ vs $O(N)$), Retrieval accuracy.
Domain Adaptation	Zero-shot degradation on specialized schemas.	Lightweight adapter fine-tuning on task-specific heads (75MB checkpoints).	DeepSWE / Benchmark success rate, Parameter efficiency.

LLM Research ideas

bhagvan kommadi — Fri, 25 Sep 2026 15:48:01 +0000

Here is a set of active LLM research directions drawn from recent papers (arXiv, Sept 2026):

Reasoning & reliability
——-
Detecting when chain-of-thought reasoning is unfaithful — e.g. models producing correct answers via unstated shortcuts, or explanations that do not reflect actual reasoning steps work on auditing reasoning trajectories with formal logic solvers, and on why chain-of-thought monitoring can't detect collusion among LLM agents in pricing scenarios

Formal/graph-based analysis of why LLMs fail at reasoning — one paper models systematic errors using multi-tape Turing machine analysis
Selective abstention: teaching models to recognize when they should say "I do not know" rather than hallucinate, via self-questioning frameworks

Agents
————-

Long-horizon agent security — defending LLM agents against long-horizon threats using "shadow memory" techniques
Memory architectures for agents — interpretable memory decision controllers that decouple confidence from consistency using three-signal complementarity
Cost-aware orchestration — deciding when to invoke an LLM at all vs. cheaper methods, framed as confidence-gated hybrid systems
Personalized agents combining knowledge-graph traversal with planning

Knowledge graphs + LLMs(relevant to your Enterprise Context Engine work)
———

Agent-driven compilation of scientific knowledge where LLMs interpret, embeddings organize, and graphs emerge — essentially using multi-agent LLM pipelines to build structured KGs from unstructured corpora
Consensus reasoning knowledge graphs to make chain-of-thought synthesis more robust against wrong intermediate steps

Architecture / efficiency
———-

2026 architecture work is moving beyond "just scale transformers bigger" — more emphasis on diffusion language models, agent harnesses, tool use, long-context handling, and practical serving infrastructure

Meta-research: can LLMs do research?
——-

A growing subfield studies whether LLMs themselves can generate novel, useful research ideas. The seminal study ran a large-scale head-to-head comparison between 100+ expert NLP researchers and an LLM ideation agent, finding real gaps in self-evaluation and generation diversity. Follow-on work studies the ideation-execution gap (do "good" LLM ideas actually pan out when executed?) and builds literature-grounded novelty/falsifiability benchmarks to judge idea quality more rigorously.

AI Coder- Chartreuse

bhagvan kommadi — Mon, 14 Sep 2026 16:32:54 +0000

Please check out new branch added - Chartreuse
https://github.com/bhagvank/AI-Coder/blob/Chartreuse/README.md

How It Works

Neural Sketch Learning

Code generation is driven by a syntax and semantics specification supplied as input. The framework is trained by being shown applications that are already configured within it (a supervised learning approach). Training input consists of a set of applications configured in terms of their views, entities, and actions; metadata from these configurations teaches the framework the application's semantics — its data types, methods, control flow, exceptions, and entity types.

The neural sketch framework combines neural learning and combinatorial search to identify patterns and render applications, while enforcing the constraints defined in the configuration file. Internally, the source code can build tree-based syntactic models ("sketches") of a program.

A sketch corresponds to a class, entity, or method — including its arguments, return values, and control flow.

Application Framework

The framework follows a metadata-driven architecture, with metadata stored and modeled using the object repository pattern. Metadata is read to render applications assuming a microservices-based architecture with the following layers:

Service layer
Services client (mobile)
Persistence layer
Data access layer
Database layer Each layer is implemented using language SDK– and platform-specific patterns and practices, with metadata attributes configured per layer. Applications built with the framework are configurable and extensible through events, delegates, templates, services, and classes, and support customization without code changes (with custom modules loadable when code changes are needed).

This approach targets native, hybrid, and web-based mobile applications, and can be used across iOS, Android, and Windows, along with their respective programming language SDKs. Because the approach is not tied to a specific technology stack, it can be upgraded by changing the framework's implementation in the relevant layer.

Application Architecture

The application architecture is designed to be extended with commercial packages and software, using only the functionality required, and includes:

Security & licensing enforcement
Auditing of inserts, edits, and deletions
Multi-tiered, entity-context sensitive security for data and actions across user roles and groups
Admin-level metadata management through screens, plus developer-level configuration of metadata and programmatic update

AI Coder

bhagvan kommadi — Wed, 09 Sep 2026 05:25:14 +0000

Please check out the neural sketch related code :

https://github.com/bhagvank/AI-Coder/

AI Coder is an AI bot that generates code in PHP, Ruby on Rails, Django, Java, and Python — including Python/Django and other frameworks. Rather than requiring developers to write code by hand, AI Coder is given basic information about the entities involved in the application, and it produces the corresponding code.

AI Coder is built on neural sketch learning: an artificial neural network trained to recognize code-level patterns across different software by "reading" large volumes of human-written code and repository content. From this training it learns to generate code based on an application's components.

Key capabilities include:

Generating application code from entity, rule, tool, and protocol definitions
Reading from a code repository and generating code based on existing components
Creating sample code and unit tests for software frameworks
Generating test data
Extensibility to support additional software APIs for code generation
How It Works
Neural Sketch Learning
Code generation is driven by a syntax and semantics specification supplied as input. The framework is trained by being shown applications that are already configured within it (a supervised learning approach). Training input consists of a set of applications configured in terms of their views, entities, and actions; metadata from these configurations teaches the framework the application's semantics — its data types, methods, control flow, exceptions, and entity types.

A sketch corresponds to a class, entity, or method — including its arguments, return values, and control flow.

Application Framework
The framework follows a metadata-driven architecture, with metadata stored and modeled using the object repository pattern. Metadata is read to render applications assuming a microservices-based architecture with the following layers:

Service layer
Services client (mobile)
Persistence layer
Data access layer
Database layer
Each layer is implemented using language SDK– and platform-specific patterns and practices, with metadata attributes configured per layer. Applications built with the framework are configurable and extensible through events, delegates, templates, services, and classes, and support customization without code changes (with custom modules loadable when code changes are needed).

Application Architecture
The application architecture is designed to be extended with commercial packages and software, using only the functionality required, and includes:

Security & licensing enforcement
Auditing of inserts, edits, and deletions
Multi-tiered, entity-context sensitive security for data and actions across user roles and groups
Admin-level metadata management through screens, plus developer-level configuration of metadata and programmatic updates

Python For Fintech Solutions

bhagvan kommadi — Tue, 21 Jan 2025 05:21:14 +0000

Please check out my latest book : Python For Fintech
https://www.amazon.in/Ultimate-Python-Fintech-Solutions-Applications/dp/8197256209

Go Lang in Depth

bhagvan kommadi — Mon, 20 Jan 2025 08:54:51 +0000

Please check out:

My latest book is on Amazon: Go Lang in Depth
lnkd.in/ge8d9Px7

4G -> 5G -> 6G

bhagvan kommadi — Sun, 20 Nov 2022 09:27:31 +0000

Applications which existed in 4g will become 10 times faster in 5g and 100 faster in 6g in terms of handling uploads and downloads. The key reason is the communication infrastructure which is going to change in the internet backbone. The devices will be upgraded to 5g and 6g soon. In some of the countries it is happening now. This will change how the locational intelligence and real time streaming applications perform and how they are being used. Roadside traffic and wealth management applications will have new use cases as features. The other AI applications related to NLP, NLU, Clustering, Supervised and Unsupervised learning ML algorithms will work in real time by using the historic data available on the cloud. Scalability and reliability of the AI applications and solutions will improve with 5g and 6g implementations. Conversational AI solutions which helped to automate customer service will work more efficiently and substituting human for monotonous and frequently asked queries.

CCW presentation - AI Adaptive network for smart cities - 5g

bhagvan kommadi — Mon, 27 Jun 2022 18:41:55 +0000

please check out my presentation on AI adaptive network for smart cities - 5g

https://bit.ly/3u7ktaW

Agile Sprint improvements

bhagvan kommadi — Sun, 19 Jun 2022 17:54:53 +0000

I found this good sprint improvements/achievements which can be used as goals for improving productivity in agile process.

Decrease number of High priority Bugs in production from 2 to 0 KPI
Increase Automation test coverage from 80% to 90%
Reduce the average number of new bug reports from 72 per month to 60 per month
Increase the crash-free ratio from 96% to 99% for the new release
Reduce average QA rejections per-feature from 1.7 to 1.0
Increase code coverage from 35% to 50%
Increase Sprint velocity from 42 to 60 points
Reduce the average number of bugs per feature from 1.7 to 1
Reduce average code review time by 30%
Reduce "design-blocked" flag to <2% of planned features
Set up and implement automatic CI and CD with CloudBees
100% of logged errors reported to Sentry
Reduce Travis build times <5m for all projects
Reduce "design-blocked" flag to <2% of planned features
Create the refactoring plan
80% of components ported from AngularJS to React
100% of reporting routes (/api) migrated to new paths
Reduce the response time for old APIs from 890 ms to 300 ms (on average)
Proactively find >20 bugs
Implement the new QA automation tool and run new process on 50% of code base
Reduce critical bugs reported from 5 to 2