Connect Power Grids to |

Open-source bridge connecting quantum algorithms to real-time energy infrastructure.

pip install quantumgridos

Supports Python 3.9, 3.10 and 3.11 only.View Documentation

Supports:

Solve for future power systems challenges

100 M+

devices

Exponential Scale

Millions of DERs create optimization problems classical computers cannot solve efficiently.

1,000+

grid states

Real-Time Demands

Sub-second decisions required across exponentially growing device combinations.

<90ms

windows

Legacy Systems

New solutions must integrate with existing SCADA, historians, and simulators.

The Missing Integration Layer

QuantumGrid OS bridges quantum processors and power system hardware.

Quantum

QuantumGrid OS

Your Systems

Quantum Ready

QAOA & VQE algorithms
IBM, Atom, IonQ, D-Wave support

Legacy Integration

RTDS, SCADA, PI Historian
DNP3, Modbus, IEC protocols

Intelligent Fallback

Auto quantum/classical triage
HPC when quantum hits limits

What Makes QuantumGrid OS Truly Unique

Four breakthrough innovations that no other quantum computing library offers

Physics-Preserving Quantum States

No other quantum library has this: maintain physical laws (Kirchhoff, power flow) within quantum superposition states.

Topology-Aware Eigenvalue Algorithm

Exploits unique grid connectivity patterns to accelerate quantum eigensolvers beyond generic quantum computing libraries.

Superposition Contingency Analysis

Exponential speedup for cascading failure scenarios by evaluating all N-k contingencies in quantum superposition simultaneously.

Uncertainty-Modeling via Decoherence

Turns hardware limitations into features: quantum decoherence naturally models grid uncertainty and forecast error.

Not just another quantum library.

Purpose-built for power grids with innovations that turn theoretical quantum advantage into practical reality.

Validated for Utility Operations

EV Coordination

Optimal charging station assignment during grid stress events.

Feeder Switching

Real-time reconfiguration for outage restoration and load balancing.

DER Dispatch

Coordinate millions of distributed resources for voltage and economics.

Cyber Response

Rapid pattern recognition and optimal recovery sequencing.

Tutorial Videos

Learn about quantum computing for power systems through our educational content

Full YouTube Playlist →

Dr. Chanda introduces Quantum Computing for Power Systems (2023)

Classical to Quantum in Four Steps

Ingest

Real-time data from your systems

Translate

Power state to quantum variables

Optimize

QAOA/VQE execution

Return

Classical control signals

Code Example

import quantumgridos as qgo

# Quick test
network = qgo.PowerNetwork.from_ieee_case(14)
optimizer = qgo.MaxCutOptimizer(network)
result = optimizer.solve()
print(f"Partition: {result['partition']}")

Open, Neutral, Production-Ready

Quantum

Qiskit, Cirq, PennyLane, provider-agnostic

Grid Systems

RTDS, GridLAB-D, Pandapower, OpenDSS

Infrastructure

Docker, Kubernetes, PI adapters

Start Your Quantum Journey

Research Access

Open-source codebase
Technical docs & community support

Full Partnership

Binary installation
Integration assistance & dedicated compute

We are looking to work with free pilot project with Electric and Gas utilities with an engineering commitment (1-2 people, 3-6 months).

Deploy in Minutes

docker pull quantumgrid/os:latest

Configure simulator

Define problem

Select backend

Run

Monitor

Cite QuantumGridOS

If you use QuantumGridOS in your research, please cite it using one of the formats below.

S. Chanda, "QuantumGridOS: A Python Library for Quantum Computing in Power Systems," Saral Systems, 2025. [Online]. Available: https://quantumgridos.com. [Accessed: Nov 29 2025].

QuantumGridOS is open-source software licensed under Apache 2.0

Questions from Engineers

No. Use cloud providers or simulators for development and testing.

Python familiarity required. Grid engineers adapt quickly with provided examples.

Standard protocols supported. Custom adapters available for partnerships.

Scales with problem size. Benefits clear beyond 1,000+ decision variables.

Local processing default. Cloud quantum calls use encrypted channels.

Standard Linux server. 16GB RAM, 8 cores recommended.

Protocol-based integration. Works with major SCADA/DMS/historian vendors.

Automatic classical optimization when quantum approaches fail or timeout.

Open source free. Full partnership requires compute and engineering commitment.

Available for production deployments. Contact for enterprise agreements.