Zapata AI, in collaboration with DARPA, has developed BenchQ, a tool for estimating quantum computing resources. The tool was created as part of the Quantum Benchmarking program, which aims to identify high-value use cases for quantum computers and the resources required to unlock their potential. BenchQ was developed with partners including Aalto University, IonQ, the University of Technology Sydney, and the University of Texas at Dallas. The tool has been used to estimate the resources required for solving complex problems in chemistry, a field where quantum computers are expected to make significant impact.
Introduction to BenchQ: A Quantum Resource Estimation Tool
BenchQ, a quantum resource estimation tool, was recently developed in collaboration with the Defense Advanced Research Projects Agency (DARPA). The tool is a result of the second phase of DARPA’s Quantum Benchmarking program, which aims to identify high-utility problems that future quantum computers might solve and the quantum computing resources required to unlock this utility.
The Role of Zapata AI and Orquestra® Platform in the Quantum Benchmarking Program
Zapata AI, a company with a long history of developing algorithms for quantum computers, was a natural fit for the program. The company’s Orquestra® platform was used to run Industrial Generative AI applications on different hardware backends. The platform’s functionality made it an ideal environment for predicting the performance of quantum algorithms for various hardware modalities. Orquestra can also scale up compute on the cloud for large benchmarking calculations, and its collaboration tools are well suited for sharing data with the large group of collaborators involved in the DARPA program.
The Two Technical Areas of the Quantum Benchmarking Program
The Quantum Benchmarking program was divided into two technical areas. In Technical Area 1 (TA1), the goal was to identify high-value use cases and create performance benchmarks for each of these use cases. This would be used to compare the performance of different quantum hardware devices. Zapata AI was a subcontractor of L3Harris with support from BBVA, bp, Copernic Catalysts, Mitsubishi Chemical, and academic partners at the University of Toronto.
In Technical Area 2 (TA2), the goal was to create software tools to make hardware-specific resource estimates for quantum computers. Zapata AI worked on this alongside partners at Aalto University, IonQ, the University of Technology Sydney, and the University of Texas at Dallas. The end result was BenchQ — an open-source tool for resource estimation and benchmarking for quantum computing applications.
The Potential of Quantum Computers in Chemistry
Chemistry is one of the most promising fields where quantum computers are expected to make an impact. Quantum computers, based on the same quantum physics that governs the behavior of atoms and molecules, can theoretically simulate the uncertainty of quantum physics more accurately than classical computers. This unique property means that quantum computers could one day support the development of new battery materials, new drugs, new industrial catalysts, and much more.
The Role of BenchQ in Ground State Energy Estimation (GSEE)
One of the key problems that quantum computers could help solve is ground state energy estimation (GSEE). For most valuable applications of quantum computing in chemistry, you would need to estimate the ground state energy of the molecules in question. Once you have an estimate of the ground state energy, you can extrapolate a lot of other properties of a molecule or chemical reaction you may be interested in. As a result, GSEE has been the focus of a lot of research in the quantum computing community. That made it an ideal problem for demonstrating resource estimation on BenchQ.
The Future of BenchQ and Quantum Computing
The work with BenchQ is just getting started. The plan now is to generate resource estimates for more application instances — in other words, industrially relevant problems that are hard to solve classically. As the work in TA1 continues to generate new application instances, BenchQ will be used to estimate the resources required for these applications.
The compilation tools in BenchQ will also be improved to make more detailed resource estimates and accommodate larger problems. These more detailed estimates will help developers across the quantum stack improve their tools and better explore the design space of the quantum problem to find a more efficient way to solve it.
BenchQ is expected to be a valuable resource for the quantum computing community. It is hoped that BenchQ will help the community identify bottlenecks and gaps that can be worked to overcome and bring the world closer to the age of practical quantum advantage.
Acknowledgements and How to Use BenchQ
The development of BenchQ would not have been possible without the contributions of team members at Aalto University, IonQ, the University of Technology Sydney, and the University of Texas at Dallas. To use BenchQ, you can run pip install benchq in your terminal or check out the Github repo.
“Chemistry is one of the most promising fields where quantum computers are expected to make an impact. Since quantum computers are based on the same quantum physics that governs the behavior of atoms and molecules, they can also theoretically simulate the uncertainty of quantum physics more accurately than classical computers. This unique property means that quantum computers could one day support the development of new battery materials, new drugs, new industrial catalysts, and much more.”
Zapata AI
“We’re excited to share BenchQ and hope it will be a valuable resource for the quantum computing community. We invite hardware developers to use BenchQ as a tool to optimize the design of their architectures for different applications and invite software developers to use it to optimize their algorithms for different quantum devices. Our hope is that BenchQ will help the community identify bottlenecks and gaps that we can work to overcome and bring the world closer to the age of practical quantum advantage.”
Zapata AI
“Finally, we’d like to thank our awesome team members at Aalto University, IonQ, the University of Technology Sydney, and the University of Texas at Dallas. Without their contributions, none of this important work would have been possible. We’re looking forward to building on our work in Phase I as we improve BenchQ and broaden community adoption in Phase II.”
Zapata AI
Quick Summary
The Quantum Benchmarking program, in collaboration with various partners, has developed BenchQ, an open-source tool for resource estimation and benchmarking for quantum computing applications. BenchQ has been used to estimate the resources required for quantum computers to solve industrially valuable problems in chemistry, such as ground state energy estimation, which could support the development of new battery materials, drugs, and industrial catalysts.
- Zapata AI, in collaboration with the Defense Advanced Research Projects Agency (DARPA), has developed BenchQ, a quantum resource estimation tool.
- BenchQ was created as part of DARPA’s Quantum Benchmarking program, which aims to identify high-utility problems that future quantum computers might solve and the resources required to unlock this utility.
- The tool was developed with partners including Aalto University, IonQ, the University of Technology Sydney, and the University of Texas at Dallas.
- BenchQ is designed to predict the performance of quantum algorithms for various hardware modalities and can scale up computing on the cloud for large benchmarking calculations.
- The tool was used to estimate the resources required for an industrially valuable problem in chemistry, specifically ground state energy estimation (GSEE).
- Quantum computers could potentially support the development of new battery materials, drugs, and industrial catalysts by accurately simulating the uncertainty of quantum physics.
- However, current quantum devices would need millions of physical qubits to perform a valuable GSEE calculation far beyond their current capabilities.
- BenchQ is expected to help reduce these resource requirements by identifying inefficiencies in current algorithms or hardware.
- Zapata AI plans to generate resource estimates for more application instances and improve the compilation tools in BenchQ to accommodate larger problems.
