Smart Current
التيار الذكي
BLACKOUTS ARE NOT JUST AN ANNOYANCE
Middle East North Africa is the 2nd highest Geo-economic region in both frequency and duration of power outages
SDG affected
strongly
unaffected
medium
16.45
Average Blackouts
per Month
avg. 4.9 hours
“Characterized by unreliability and inefficiency” (De Gruyter)
>700Bد.إ
Yearly economic loss
Electrical grids
Quantum Topological Data Analysis
Regulate power-flows
Graph neural network
“Classical features”
Quantum enhanced
It has been shown that optimal power flows lead to
60% reduction in blackout risk.
Most blackouts are avoidable.
1
1
Electrical grids
Quantum Topological Data Analysis
Regulate power-flows
Graph neural network
“Classical features”
Quantum enhanced
We provide automated real-time powerflow corrections to avoid and mitigate blackouts.
However, optimization methods are too slow to respond in real time.
Overview
Electrical grids
Quantum Topological Data Analysis
Regulate power-flows
Graph neural network
“Classical features”
Quantum enhanced
Online blackout response dashboard
Open-source quantum circuit implementations
Blackout response and prevention machine learning models
smartcurrent.com
Smart Currents
Smart current – Our innovation, our mission
Electrical grids
Quantum Topological Data Analysis
Regulate power-flows
Graph neural network
“Classical features”
Quantum enhanced
Electrical grids
Quantum Topological Data Analysis
Regulate power-flows
Graph neural network
“Classical features”
Quantum enhanced
Enhanced ML approach
using power grid geometry
Train a graph neural network with emphasis on neighbor connectivity
Representing Graphs as Topological Surfaces
Quantum Speedup of the Genus/Betti number approximation
Classical-Complexity
Quantum-Complexity
Super-Polynomial
Polynomial
Electrical grids
Quantum Topological Data Analysis
Regulate power-flows
Graph neural network
“Classical features”
Quantum enhanced
Our end-to-end quantum-inspired models for power flows can be found on Github.
We’ve also created the first open-sourced implementations of two circuits.
Quantum TDA Boundary Operator
Quantum Orthogonal Neural Network
Hardware Verification
Noisy simulation
IBM Guadalupe
Candice, 30s
Business
Electrical grids
Quantum Topological Data Analysis
Regulate power-flows
Graph neural network
“Classical features”
Quantum enhanced
Market (MENA)
Yearly investments in power 80+Bد.إ
Value proposition
Efficiency improvements and risk minimization for grid operators
No direct competitors, indirect competitors can be partners
Roadmap
6 months
Training
Validation on DR POWER dataset (over 1TB)
Cost ~د.إ20,000
Infrastructure
AWS Sagemaker API
IBM Quantum Cost ~د.إ150,000
Business
Create partnership with one key MENA government
Personnel
Yearly Cost ~د.إ600,000
3+ years
Training
Addition of government-specific databases
Cost ~د.إ120,000
Infrastructure
Govt. On-premises, IBM Quantum, AWS
Cost ~د.إ1,500,000
Research
Investigate other topological properties
Sohum Thakkar,
United States
Henok Daniel, Eritrea
Yaffa Jaradat, Palestine
Elias Huber, Switzerland
Allen Baranov,
USA
Omar Al Khazali,
Jordan
Kannan Vijayadharan,
India
Candice Kang, China
Haya Fuad,
Syria
Stephen Lang, USA
Sohum Thakkar,
United States 🇺🇸
Henok Daniel, Eritrea 🇪🇷
Yaffa Jaradat, Palestine 🇵🇸
Elias Huber, Switzerland 🇨🇭
Allen Baranov,
USA 🇺🇸
Omar Al Khazali,
Jordan 🇯🇴
Kannan Vijayadharan,
India
Candice Kang, China 🇨🇳
Haya Fuad,
Syria 🇸🇾
Stephen Lang, USA 🇺🇸
🇮🇳
Overview slides
Value Proposition Breakdown
Problem – Blackouts
Solution – Business value in MENA
1
1
2
2
Bring the power of quantum to the manage energy grids
Adapt to the need of our partners
Collaboration across MENA for a better future
Market: Yearly investments in power 80+Bد.إ
Client proposition: Efficiency improvements and risk minimization for grid operators
No direct competitors, indirect competitors can be partners
3
Frost & Sullivan
3
6 months (training & partnership)
3+ years (Deployment & Research)
Phase 1
Cost~د.إ500,000
Phase 2
Cost~د.إ20,000,000
Business | How our innovative solution creates value
Competitors
1
Value now – Implement our classical graph neural network and further develop our quantum enhancements in cooperation with industry partners
2
Leverage the power of quantum computers as hardware matures
Bring the power of quantum to the manage energy grids
Collaboration across MENA for a better future
Our approach based on graph neural networks and close collaboration with grid-managers allow tailored use-cases beyond grid-management: Grid planning and maintenance, efficiency optimization, stability evaluation …
Adapt to the need of our partners
Collaborate across MENA with governments and non-government entities. Establish local quantum competences.
Competitor | Description |
Voltus™ |
|
SPINE LLC |
|
Grid-operators |
|
While potential competitors exist …
Different business focus and all of them could be business partners!
Improve and save the lives of ordinary people
How did we test?
We can run some part of the algorithm classically.
Classical runtime
TDA
Compound GNNs
Dim 1
Dim 2
Dim 3
Dim 4
Dim 5
Dim 6
…
Dim 1
Dim 2
Dim 3
Dim 4
Dim 5
Dim 6
…
Feasible zone ☺️
Need quantum 🙄