Quantum for Energy and Resource Exploration
The Promise of Quantum in Energy and Resource Exploration
From oil and gas fields to renewable energy farms, modern energy systems rely on complex, data-intensive decision-making. These decisions—such as where to drill, how to balance supply and demand, or how to maintain grid reliability—involve millions of variables and interdependencies.
Classical supercomputers struggle with such challenges because the number of possible configurations grows exponentially. Quantum technologies open the door to faster, more accurate solutions, enabling:
- Optimized energy forecasting that accounts for seasonal patterns, weather anomalies, and demand fluctuations.
- Better exploration strategies for locating and assessing new resources.
- Smarter grid management to integrate renewables and prevent outages.
Infleqtion’s approach harnesses cold-atom quantum computing, high-performance sensors, and quantum-inspired algorithms to address problems that were previously intractable.
Tackling the Unit Commitment Problem
In the power industry, the Unit Commitment problem determines which power plants should operate at any given time to meet demand at the lowest possible cost—while respecting operational constraints like ramp-up times, fuel limits, and emissions targets.
Today, solving this problem for large grids is computationally expensive and often requires simplifying assumptions, which can lead to inefficiencies and higher costs.
Infleqtion’s quantum algorithms can:
- Evaluate more possibilities in less time, delivering closer-to-optimal solutions.
- Adapt rapidly to changing inputs, such as shifts in renewable energy availability.
- Reduce operational costs and emissions through better resource allocation.
By encoding the Unit Commitment problem directly into quantum circuits, we aim to help utilities and independent system operators improve both economic efficiency and grid reliability.
Quantum for Grid Contingency Analysis
Electrical grids must be resilient to contingencies—unexpected events like equipment failures, line outages, or sudden demand spikes. Contingency analysis simulates these scenarios to identify vulnerabilities and prepare mitigation strategies.
As renewable penetration grows and grid complexity increases, the number of scenarios to test can be enormous. Infleqtion’s quantum computing solutions can:
- Run larger, more detailed simulations without oversimplifying.
- Identify high-risk contingencies faster, improving grid security.
- Support real-time decision-making in control rooms during emergencies.
This capability is especially critical for integrating intermittent energy sources while maintaining a stable, reliable power supply.
Applied Research and Real-World Impact
Our work in quantum optimization for power systems builds on collaborations with leading institutions and government agencies, including ARPA-E’s ENCODE program, which targets billions of dollars in annual savings through more efficient energy delivery.
Published research and pilot projects have demonstrated quantum’s potential to:
- Improve energy demand forecasting using quantum-inspired contextual machine learning.
- Optimize Phasor Measurement Unit (PMU) placement for better grid observability.
- Enhance nuclear energy modeling at the subatomic level.
Why Infleqtion
With a world-class team, global R&D presence, and breakthroughs in cold atom quantum computing, Infleqtion is uniquely positioned to bring quantum energy solutions from the lab to the field. We combine:
High-performance quantum hardware
Specialized algorithms for energy optimization
Industry partnerships that ensure solutions are tailored for real-world constraints
The result: Practical, scalable tools that help energy producers, utilities, and governments make smarter, faster, and more sustainable decisions.
Additional Resources
Q2B24 Silicon Valley | Jeremy Renshaw, Senior Technical Executive – AI, Quantum, & Innovation, EPRI
Quantum for Power and Utilities Panel at Quantum World Congress 2023
On the Computational Viability of Quantum Optimization for PMU Placement
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Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment
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Quantum Computing for Enhancing Grid Security
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Optimization of Simultaneous Measurement for Variational Quantum Eigensolver Applications
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Power Flow Contingency Analysis with NISQ-era Hybrid Quantum Algorithms
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Computational Fluid Dynamics on Quantum Computers
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