PROJECTAGNI
From Visakhapatnam, Andhra Pradesh, India
Quantum AI Labs
A research-first quantum optimization laboratory, designing hybrid algorithms that work on classical simulators today and seamlessly integrate with quantum processors as hardware matures.
Our Mission
To pioneer quantum-accelerated methods for planning, optimization, and coordination by harnessing superposition, entanglement, and interference—while keeping solutions practical, benchmarked, and reproducible throughout the quantum computing evolution.
Research Philosophy
Simulate First. Benchmark Rigorously. Publish Responsibly.
Classical Baselines First
Strong benchmarks before any quantum comparisons
Hybrid by Default
Best tool for each computational task
Instance-Dependent Results
No blanket quantum advantage claims
Automatic Fallbacks
Classical solutions when quantum doesn't deliver
Vendor-Neutral Architecture
Open interfaces, no lock-in
Reproducible Science
Versioned configs, deterministic seeds, audit trails
Research Areas
Operations Optimization
Orchestrate routes, schedules, and resource assignments under hard, real-world constraints with full traceability.
Combinatorial Optimization at Scale
- Vehicle Routing Problems with capacity, time windows, and service levels
- Job shop scheduling with precedence constraints
- Dynamic re-planning under disruptions
- Multi-objective trade-offs: distance, time, energy, cost
Quantum-Classical Hybrid Approaches
- QUBO formulations for discrete optimization
- QAOA variants for structured problems
- Quantum annealing for constraint satisfaction
- Ensemble selection with automatic fallback
Use Cases
Finance & Portfolio Modeling
Construct rule-aware, transparent portfolios with explicit risk/return trade-offs and governed rebalancing protocols.
Discrete Portfolio Optimization
- Cardinality constraints: select exactly N assets from M candidates
- Budget and capital allocation under liquidity constraints
- Sector caps, issuer limits, and regulatory compliance
- Turnover minimization and transaction cost modeling
Risk-Return Trade-offs
- Mean-variance optimization with Markowitz framework
- Conditional Value-at-Risk measures
- Robust optimization under parameter uncertainty
- Multi-period rebalancing with regime detection
Use Cases
Quantum AI Advancements
Explore methods at the intersection of quantum algorithms and machine intelligence that improve search, sampling, and policy learning.
Quantum-Enhanced Exploration
- Superposition-style branching for long-horizon planning
- Quantum-accelerated Monte Carlo tree search
- Variational quantum reinforcement learning
- Entanglement-aided coordination signals
Quantum Machine Learning
- Quantum Neural Networks with parametrized circuits
- Quantum kernels for feature space expansion
- Variational quantum classifiers
- Quantum feature extraction for classical ML pipelines
Use Cases
NVQLink Inspiration
Forward-Compatible Hybrid Architecture
"NVQLink is the Rosetta Stone connecting quantum and classical supercomputers—uniting them into a single, coherent system that marks the onset of the quantum-GPU computing era."
Quantum Error Correction at Scale
Qubits require real-time classical supervision for calibration, syndrome decoding, and dynamic error correction.
- Microsecond-latency feedback loops
- High-bandwidth throughput for data exchange
- Real-time orchestration between quantum gates and classical control
Fault-Tolerant Quantum Computing
GPU-accelerated classical control becomes essential as quantum systems scale toward millions of logical qubits.
- Real-time QEC decoding algorithms
- Dynamic calibration and gate optimization
- Hybrid quantum-classical execution
- Seamless CPU-GPU-QPU workflow integration
Our Forward-Compatible Design
We engineer abstractions to be NVQLink-ready if and when such access becomes available:
No partnership implied.
Methods & Tooling
Open-Source SDKs We Use
CUDA-Q
NVIDIA's platform for hybrid quantum-classical programming
Apache 2.0Qiskit
IBM's quantum computing framework
Apache 2.0Cirq
Google's quantum circuit library
Apache 2.0PennyLane
Xanadu's differentiable quantum programming
Apache 2.0TensorFlow Quantum
Google's quantum machine learning library
Apache 2.0Strawberry Fields
Xanadu's photonic quantum computing framework
Apache 2.0GPU Simulators
NVIDIA cuQuantum — Accelerates statevector and tensor network simulations.
Used under NVIDIA EULA; Python components may be open source.
India's Quantum Mission: The Quantum Leap is Our Vision
India's passion for quantum computing is fueling a national transformation, with the National Quantum Mission (NQM) charting a bold roadmap toward self-reliance and global leadership. As part of Viksit Bharat @2047—our nation's vision to become a developed economy by 2047—we at Quantum Gandiva AI Labs are deeply committed to this vision.
We align our research with India's aspirations to build proprietary quantum algorithms, quantum software for hardware, and quantum accelerators for 2047, contributing to superfast computing that powers innovation in every sector.
Amaravati Quantum Valley, Andhra Pradesh
Public reports cite January 1, 2026 launch target
Plans include IBM Quantum System Two installation
National Quantum Mission backing with substantial government allocation
Our Approach
- Sovereign-hardware-ready interfaces
- Prepared to connect if and when access becomes available
- No current access or partnership implied
This positions us to leverage India's quantum infrastructure while maintaining vendor neutrality globally.
Multi-Decade Roadmap
Simulation & Theory
Develop, validate, and benchmark quantum algorithms on classical simulators
Deliverables
- Quantum-inspired classical algorithms in production
- Strong baseline implementations
- Reproducibility harness with versioned configs
- Open-source circuit libraries
Success Criteria
- Parity or improvements versus classical on target problems
- Full reproducibility with deterministic seeds
- One published research brief per focus area
NISQ Implementation
Test on Noisy Intermediate-Scale Quantum devices via cloud access
Deliverables
- Production hybrid pipelines combining classical and quantum
- Head-to-head comparisons versus classical baselines
- Wall-clock time and cost-per-run analysis
- Error mitigation strategy validation
Success Criteria
- Quantum shows advantage on multiple problem instances
- Documented cases where classical remains superior
- Cost-effective hybrid execution demonstrated
Hybrid Production Systems
Deploy enterprise-grade hybrid pipelines in production environments
Deliverables
- Classical controllers with selective quantum subroutine calls
- Real-time decision systems using quantum acceleration
- Integration with enterprise workflow tools
- Customer deployments with validated ROI
Success Criteria
- Production deployments at multiple enterprise customers
- Measurable business impact
- Automated quantum vs classical routing
- Sub-second latency for time-critical applications
Fault-Tolerant Era
Scale to logical qubits and fault-tolerant quantum computers
Deliverables
- Algorithms upgraded for error-corrected quantum systems
- Million-qubit problem instances
- Industry-wide standards for hybrid computing
- Sovereign quantum infrastructure integration
Success Criteria
- Quantum dominates classical for target optimization problems
- Routine use of quantum acceleration in production
- Seamless upgrade path without system rewrites
- Quantum-AI integration in AGI systems
Validation Plan
Apples-to-Apples Comparisons
Strong Classical Baselines
- Integer Linear Programming
- Constraint Programming
- Mixed Integer Quadratic Programming
- Meta-heuristics: SA, GA, tabu search
Comprehensive Metrics
- Objective value & optimality gap
- Wall-clock time & cost per run
- Energy consumption
- Robustness to noise
- Constraint satisfaction rates
Ablation Studies
- Ansatz depth variations
- Noise model comparisons
- Error mitigation effectiveness
- Hyperparameter sensitivity
- Hidden hold-out test sets
Reproducibility Package
Every experiment includes scripts, configuration files, input datasets, random seeds, software versions, hardware specifications, and hidden hold-out test sets to prevent benchmark overfitting.
Current Achievements
Live in Production
- Simulator-backed QAOA and VQE pipelines
- Quantum-inspired heuristics on CPUs/GPUs
- Hybrid controllers with clean provider interfaces
- Internal benchmarking infrastructure
- Reproducibility harness with audit trails
In Development
- Cloud quantum processor integration: IBM, AWS, Azure
- Real-time re-planning engines for operations
- Portfolio backtesting framework
- Quantum RL experiments for long-horizon planning
Next Phase
- Targeted hardware runs via public/cloud quantum programs
- Published head-to-head comparison studies
- Design partner pilots in operations and finance
- Integration with AGI Labs long-horizon planning
Commitment to Excellence
Simulator-First Development
Rigorous error mitigation, strict performance baselines, and efficient batch scheduling ensure practical, scalable quantum solutions.
Evidence-Driven Research
We publish results honestly—including cases where quantum doesn't win—building trust through transparency.
Open Science
Contributions to open-source quantum computing ecosystems advance the entire field, not just our lab.
Vendor Neutrality
No hardware lock-in; we evaluate all platforms objectively and route workloads to optimal providers.
How to Engage
Partner with us on the quantum journey
For Researchers & Institutions
- Joint research projects with academic institutions
- Co-authored publications in quantum computing
- Access to our benchmarking infrastructure
- Share datasets and benchmark problems
- Collaborate on open-source quantum software
- Co-supervise graduate research projects
For Enterprises
- Pilot programs for operations optimization
- Portfolio modeling evaluations for financial institutions
- Quantum AI experiments for planning
- Evaluate quantum-hybrid solutions on your data
- Time-boxed pilots with clear success metrics
- Transition from research to production when validated