SK
Sunil Kumar Mishra
Co-Founder & Chief Executive Officer
Quantum many-body dynamics specialist from IIT BHU, leading company strategy and translational deeptech execution.
ConnectFirst quantum-classical SDK for tactical edge & space AI
Scaling intelligencebeyondthe compute wall
QubitPsi AI builds a modality-agnostic intelligence engine with ModAqua, mapping text, imagery and RF signals into tensor-network representations that run across CPU, GPU, and QPU backends from one codebase.
- 70%+
- compute saved (SWaP-C)
- 100×
- lower inference draw
- O(n²·d) → O(nlog(χ)·d)
- tensor attention efficiency
Capabilities
The ModAqua stack for mission-grade AI
A quantum-classical SDK built for defense and space workloads where power, latency, and reliability matter more than datacenter assumptions.
Unified Modality Engine
One abstraction layer for text, imagery, RF, and telemetry so edge systems can run true multimodal inference locally.
Tensor Network Mapping
ModAqua maps tokens into MPS and MERA representations to preserve signal structure under strict SWaP-C constraints.
Automatic Compilation
Write models once and compile dynamically to CPU, GPU, or QPU execution paths without rebuilding your stack.
Sovereign Security
Air-gapped and mission-ready deployment options designed for defense and critical infrastructure requirements.
Tensor Attention Advantage
Optimized attention pathways reduce bottlenecks from O(n²·d) to O(nlog(χ)·d) for faster edge-ready execution.
Quantum Reservoir Computing
Dual-unitary brickwall dynamics with Krylov stabilization accelerate training for noisy tactical signal environments.
ModAqua Technology
From raw signals to edge decisions
The stack is designed for tactical and orbital constraints, minimizing inference draw while keeping multimodal intelligence close to the sensor.
- 01
Abstract
ModAqua ingests text, imagery, RF signals, and telemetry into a unified modality-agnostic tensor representation.
- 02
Compile
The compiler maps classical model definitions into optimized execution graphs for CPU, GPU, or QPU targets.
- 03
Stabilize
Dual-unitary brickwall reservoirs with Krylov basis stabilization sustain memory while reducing training instability.
- 04
Operate
Mission teams deploy low-power, on-board inference with zero dependence on continuous cloud downlink connectivity.
Modality-agnosticCPU/GPU/QPU runtimeTensIR-ready toolchain
The Company
Deeptech execution for defense and space AI
QubitPsi AI Private Limited is a quantum-AI company developing ModAqua, a modality-agnostic compiler layer that helps constrained systems process mission data on board instead of sending massive raw streams to remote clouds.
Standard protocol mission
We are building the default protocol for hybrid multimodal AI training across GPU and QPU backends.
Edge-first execution
Our focus is tactical edge and space systems where low-latency, low-power autonomy is essential for reliability.
Open-core to enterprise scale
An Apache 2.0 developer engine drives adoption while enterprise modules support sovereign and regulated deployments.
70%+
Compute saved (SWaP-C)
100×
Lower inference draw
$60B+
Initial segment TAM
$8T+
SpaceTech TAM by 2032
Leadership
Meet the founders
A founding team spanning quantum information science, edge systems, and applied AI — aligned around building sovereign-ready multimodal intelligence.
CEO
AK
Amit Kumar Jaiswal
Co-Founder & Chief Technology Officer
NLP and quantum-inspired modelling expert leading Jay Chaudhry Software Innovation Centre @ IIT-BHU, ex-CTO @ Endless Protocol, driving Quantum engineering.
ConnectRG
Ruchir Gupta
Co-Founder & Chief Financial Officer
Edge computing and 5G/6G systems expert from IIT BHU, driving operations, partnerships, and commercialization pathways.
ConnectInsights
Discuss roadmap fitStrategic execution path
Get in touch
Let’s build the quantum advantage together
Whether you are evaluating mission pilots, sovereign deployments, or multimodal edge workloads, our team is ready to collaborate.
contact@qubitpsiai.com
QubitPsi AI Private Limited · Registered office, India