Quantum computers can now interface with electrical grid equipment

NREL and Atom Computing Debut Open Source Application for Quantum Studies in the Loop


A quantum computing solution stack.
Atom Computing’s quantum computing solution stack was interfaced with energy research equipment at the National Renewable Energy Laboratory. The interface is open source and vendor-neutral, allowing other researchers to continue their studies supported by quanta. Photo from Atom Computing

With its mind-boggling size and connections, the power system is so complex that even supercomputers struggle to solve certain optimization problems efficiently. But quantum computers could fare better, and now researchers can explore that prospect thanks to a software interface between quantum computers and networking equipment.

The quantum-scream link obtained by a team at the National Renewable Energy Laboratory (NREL) with funding from the Department of Energy’s Office of Energy Efficiency and Renewable Energy and in collaboration with RTDS Technologies Inc. and Atom Computing allows researchers to perform quantum experiments. And this capability goes beyond simple energy devices: With NREL’s Advanced Integrated Energy Systems Research (ARIES), researchers can perform quantum in-the-loop within highly realistic power systems. Quantum in-the-loop could be an important next step for using quantum computing to optimize power grid operations with the interconnection of increasingly complex distributed energy resources.

The research team successfully debuted its open source interface near Boulder, Colorado, using an RTDS real-time grid simulator stack and Atom Computings solution stack, leveraging its atomic array quantum computing technology. Their demonstration marked a historic moment for both quantum computing and power systems: for the first time, a how much information technology is integrated into a dynamic power grid research platformbreaking new ground in grid and hardware validation.

To evaluate the security of next-generation communication protocols and validate current and future quantum algorithms, it is critical to establish a real-world emulation environment with actual hardware and high-speed communication. This is exactly what we developed at ARIES with quantum in-the-loop, said Rob Hovsapian, a research consultant at ARIES.

What can Quantum Grill offer?

The research team’s quantum-in-the-loop framework is motivated by past findings that quantum algorithms are well suited for power system complexity, especially the large optimization problems that overwhelm classical computers. Such complex problems are increasingly common as distributed energy resources proliferate and energy flow becomes bi-directional.

With the sheer amount of ways power can now be generated and supplied, it’s very important to handle so many inputs and outputs, but classical computer-based optimizers are not designed to handle the exponential increase in input parameters that industry is expected to see over the next two decades, said Sayonsom Chanda, power system engineer at NREL. We are talking about millions of inputs and outputs; it is then that classical computers begin to show their limitations and quantum computers their advantages.

When modeling the network, every electric vehicle, appliance, or sensor is a variable potential. Their data interacts and coevolves in such convoluted ways that even a query on the available power of networks becomes computationally difficult. The new interface simplifies the process of translating optimization problems into quantum variables and facilitates the communication from quantum computers to power system simulations. As interest in quantum computing grows, the interface will help scientists classify the types of problems that can be solved by quantum computers and evaluate them in live experiments.

Imagine, for example, that a city needs to evacuate due to an oncoming hurricane, Hovsapian said. Suddenly you have to make decisions about efficient evacuation, which depends on the charge of the electric vehicles, their route outside the city, the availability of charging stations, etc. Quantum computing may hold the key to this kind of multi-objective optimization, and we now have the tools to find out.

Quantum physics, characterized by the concepts of probability and entanglement, offers a functionally different and in some cases faster form of calculation. For example, Grovers’ quantum algorithm can theoretically solve search problems more efficiently than any known classical algorithm. With recent technical achievements from companies like Atom Computing, quantum algorithms are being tested on real applications and power systems rank among the most enticing areas.

Consider some of the most challenging problems in today’s energy systems: making decisions based on large sensor networks; optimize system recovery during failure conditions; secure communications between network devices. There are critical applications where quantum computers can excel, so we’re accelerating their adoption in power systems with this interface, Hovsapian said.

Test configuration and technology

NREL and other research facilities regularly validate new energy technologies with in-the-loop hardware, but quantum in-the-loop has never existed until now. The demonstration relied on several unique capabilities: NRELs provided nine real-time digital simulators, which communicated over the ESnet network with Atom Computings’ quantum emulator and finally with Phoenix, its prototype system. Connecting the two sites was the newly developed interface, software for interpreting, converting and transmitting data from each end in real time.

Quantum computers perform digital simulations in real time.

Real-time digital simulators can simulate power systems for research and planning. NREL and Atom Computing have created an application programming interface between real-time simulators and quantum computers, allowing the simulations to communicate with quantum solvers for complex optimization problems. Photo by NREL

In an article written by the NREL team titled “Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids,” Chanda and Hovsapian rigorously explain interface design and provide an illustrative example: EV charging coordination.

Researchers can use the quantum computer to develop and implement quantum approximation optimization algorithm or quantum variational autoresolver algorithms, the paper states. These high-performance code snippets will help researchers bridge the computational gaps between classical and quantum computers and will primarily be based on familiar open source frameworks such as QISKIT, QMuTPy and Queso.

In addition to being technology agnostic for quantum computing platforms, the interface is compatible with all real-time digital simulation platforms. Users can interact with the software via a web browser and an intuitive interface to set desired optimization values, tune quantum algorithms, and retrieve qubit measurements. The authors plan to publicly release the code on GitHub.

What’s next?

Quantum computing is still in its infancy and its value for power systems remains unproven, but that is precisely why this interface is so useful: all theorizings and expectations related to quantum computing can now be evaluated experimentally.

It’s critical for utilities to field test and adopt next-generation technologies, and quantum computing is no exception, Chanda said. This interface is an enabler for future research into emerging network problems.

Aerial view of computer housing at the NREL facility.

ARIES capabilities at NREL provide realistic power system environments for advanced research. The quantum-in-the-loop interface allows researchers to perform experiments on ARIES resources using quantum computers. Photo by NREL

Quantum in-the-loop is especially enabling with ARIES, where it merges other in-the-loop technologies, such as commercial renewable energy resources, system controllers, supercomputer-powered emulations, sensors, and substation equipment. ARIES is as close to reality as it gets for power system experiments, and it continues to expand: Within the next year, ARIES will have the capacity to control 10,000 energy devices, among other investments. This capability offers a unique and crucial realism for evaluating quantum algorithms and driving advances in power systems.

The first optimization problem we want to address is how best to draw energy from different sources, Chanda said. You have some assets that are closer to certain loads and others that make more economic sense to deploy. Perhaps quantum computing can determine how to rapidly switch energy sources for resilience and efficiency.

We are still in the early days of quantum computing, but the US Department of Energy and NREL have removed a major barrier to bringing this promising technology into the energy systems arena.

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Image Source : www.nrel.gov

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