Anuj Dalvi@anuj_dalvi276
1
Events attended
1
Submissions made
India
2+ years of experience
About me
I am Anuj, a disciplined, impact‑driven AI/ML engineer in my final year of B.Tech AIML at CSMU, already working like a full‑time builder of systems and products. I design and ship end‑to‑end AI solutions for real problems, such as medical diagnostics pipelines, NASA turbofan remaining‑life prediction, an audio language model for DRDO, and a face recognition attendance system. Alongside this, I build and deploy production‑ready web experiences and digital infrastructure for clients like Ceejay Engineering and Nanhe Dhaage, handling APIs, deployments, performance, UI/UX, and SEO. Through roles with GDG, the AWS Student Ambassadors technical team, and hackathons like Smart India Hackathon and IBM’s GramCred, I keep sharpening my leadership, rapid‑prototyping, and community‑building skills. Over the past few months, I have also been stepping seriously into quantum computing and quantum information, following material from IBM Quantum, Google DeepMind, and foundational books to connect quantum mechanics and information theory with my existing AI work. In the long term, I am shaping myself into someone who can understand complex, high‑stakes problems in domains such as healthcare, aerospace, and education, and architect robust AI systems—classical and eventually quantum‑enhanced—that genuinely solve them.
🤓 Latest Submissions
QUBOB: Quantum-Inspired Microservice Optimizer
### Problem Statement Modern cloud-native applications frequently consist of dozens to hundreds of microservices deployed across distributed Kubernetes clusters and multiple availability zones. Placing these services optimally across nodes is an NP-hard combinatorial optimization problem with an S^N search space (yielding over 10^20 possible configurations for a modest 20-service, 10-node cluster). Standard Kubernetes schedulers evaluate only static CPU and memory limits, remaining blind to real-time inter-service network traffic. Consequently, high-throughput service pairs are routinely scattered across different availability zones. Each cross-zone hop introduces an extra 5 to 10 ms of round-trip time (RTT), causing cascading latency bottlenecks, SLA breaches, and thousands of dollars in unmonitored cloud egress bandwidth fees ($0.01–$0.09/GB). Classical greedy algorithms get trapped in local minima by greedily filling individual nodes, while physical quantum processing units (QPUs) suffer from queue latency, high operational costs, and rapid depletion of compute budgets. ### The Solution: QUBOB QUBOB (Quantum-Inspired Unconstrained Binary Optimizer for IBM Bob 2.0) resolves this dilemma by running quantum-inspired optimization natively on standard classical CPUs. 1. **QUBO Hamiltonian Formulation:** QUBOB decodes workload manifests (Kubernetes Deployments, StatefulSets, and Docker Compose) into a weighted communication graph and constructs a multi-objective Hamiltonian: $$\mathcal{H} = \mathcal{H}_{\text{latency}} + \mathcal{H}_{\text{resource}} + \mathcal{H}_{\text{affinity}} + \mathcal{H}_{\text{penalty}}$$ This formula balances latency minimization against CPU/RAM capacity bounds and PCI-DSS compliance constraints. 2. **Quantum-Inspired Solver (QIEA):** Decision states are modeled as quantum Q-bits in superposition. Using Han & Kim dynamic rotation gates and Pauli-X catastrophe tunneling operators, QIEA explores billions of states and avoids local optima without requiring actual quantum hardware. 3. **Actionable DevOps Output:** QUBOB automatically generates unified git diffs injecting production-ready Kubernetes `nodeAffinity` and `topologySpreadConstraints` directly into deployment YAMLs. ### Unique Innovation & Measured Impact In an enterprise benchmark of a 20-microservice PCI-DSS digital banking platform, QUBOB analyzed 48 dependency edges and resolved a 3,500 req/s critical bottleneck between payment and fraud services. Across availability zones, this single edge wasted 28,000 ms of cumulative latency every second. QUBOB solved this 60-variable QUBO in just 0.242 seconds, co-locating the critical pair with zero constraint violations and achieving a verified 99.6% latency reduction. QUBOB operates with a peak RAM footprint of just 0.39 MiB (<1% of the hackathon Bobcoin budget) and is verified by 228 passing tests with 100% core coverage.
27 Sep 2026
