QuEra Computing, a provider of neutral-atom quantum computers, has been awarded two grants by the Defense Advanced Research Projects Agency (DARPA) as part of the Imagining Practical Applications for a Quantum Tomorrow (IMPAQT) programme. The grants will advance quantum algorithms and application development including Quantum Machine Learning. QuEra’s technology is based on large-scale arrays of neutral atoms and offers up to 256 qubits on its Aquila-class machines. The company is also working on scaling up to higher numbers. Five of QuEra’s partners, including Moody’s, Harvard University, The University of Padova, BlueQubit, and Polaris Quantum Biotech, also received DARPA IMPAQT grants for projects on QuEra’s quantum computers.
QuEra Computing Receives DARPA IMPAQT Contracts for Quantum Computing Projects
QuEra Computing, a company specialising in neutral-atom quantum computers, has been awarded two grants from the Defense Advanced Research Projects Agency (DARPA) as part of the Imagining Practical Applications for a Quantum Tomorrow (IMPAQT) programme. The grants are aimed at advancing quantum algorithms and application development. The projects awarded to QuEra are titled “Quantum Reservoir Learning using Neutral Atoms and its Applications” and “Error-Corrected Quantum Architectures Based on Transversal Logical Gates.”
The DARPA IMPAQT programme funds innovative applications and algorithms that can utilise practical quantum platforms, which could be demonstrated in the next few years. The programme aims to connect quantum computing researchers with application domain experts working on classical platforms to address their target problems.
QuEra’s technology is built on large-scale arrays of neutral atoms. It currently offers users up to 256 qubits on its Aquila-class machines, available for access on a major public cloud platform. The company is actively working towards scaling up to much higher numbers. QuEra’s designs feature a unique combination of system size, coherence, and an innovative analog quantum processing mode that provides new ways to solve machine learning, optimisation, and simulation problems.
QuEra’s Quantum Reservoir Learning Project
The first QuEra project to receive a DARPA IMPAQT grant is focused on “Quantum Reservoir Learning using Neutral Atoms and its Applications”. This project is based on the team’s previous quantum machine learning proof-of-concept of the method for MNIST handwritten digit data sets.
In the 12-month project, QuEra will advance this proof-of-concept, turning it into a full-fledged demonstration at scale. The aim is to show how a classification problem of practical interests can be solved using QuEra’s neutral-atom quantum hardware. The performance of the method will be determined and compared to other classical approaches. QuEra will enhance the method’s applicability by determining further strategies to encode problems in the hardware, enabling the pursuit of further applications to real-life use cases.
Error-Corrected Quantum Architectures Project
The second project, “Error-Corrected Quantum Architectures Based on Transversal Logical Gates,” aims to enhance existing surface code quantum computation schemes with the use of transversal logical entangling gates. The implementation and performance of these gates will be analysed in detail on state-of-the-art neutral atom array quantum computers.
QuEra will also investigate the savings in the concrete setting of key algorithmic subroutines and small-scale quantum algorithms. This has the potential to reduce the space-time resources required per gate from O(d3) to O(d2), allowing an order-of-magnitude (10x) reduction in physical q*N resources to implement a logical circuit.
Partnerships and Collaborations
Five partners that were awarded DARPA IMPAQT grants are also running their projects on QuEra’s quantum computers. These partners are focusing their research in a number of areas, including:
- Moody’s: Predicting events impacting insurers.
- Harvard: Analog-digital quantum machine learning on programmable neutral-atom quantum simulators.
- BlueQubit: Areas where classical methods fall short, particularly in Gibbs sampling.
- Polaris Quantum Biotech: Quantum-aided drug discovery.
- The University of Padova: Numerical benchmarking of quantum simulations to be run on QuEra’s hardware.
“We are honored to be recipients of DARPA’s IMPAQT grants, which serve as a testament to the groundbreaking work we’re doing in the realm of neutral-atom quantum computing,” said Alex Keesling, CEO, QuEra. “These grants will push the boundaries of what’s possible in optimization and machine learning. As we continue to scale our neutral-atom machines, we’re not just advancing QuEra’s capabilities; we’re contributing to the broader quantum ecosystem.”
Quick Summary
Seven projects, including two led by QuEra Computing, have been awarded contracts by the Defense Advanced Research Projects Agency (DARPA) to advance quantum algorithms for neutral-atom quantum computers. The projects aim to enhance quantum machine learning and error correction, and will be conducted on QuEra’s neutral atom-based quantum computers, which offer up to 256 qubits and unique system features for solving complex problems.
- QuEra Computing, a leader in neutral-atom quantum computers, has been awarded two grants from the Defense Advanced Research Projects Agency (DARPA) as part of the Imagining Practical Applications for a Quantum Tomorrow (IMPAQT) program.
- The grants will fund projects to advance quantum algorithms and application development. The projects are titled “Quantum Reservoir Learning using Neutral Atoms and its Applications” and “Error-Corrected Quantum Architectures Based on Transversal Logical Gates”.
- Five of QuEra’s partners, including Moody’s, Harvard University, The University of Padova, BlueQubit, and Polaris Quantum Biotech, also received DARPA IMPAQT grants for projects conducted on QuEra’s neutral atom-based quantum computers.
- QuEra’s technology is built on large-scale arrays of neutral atoms, offering users up to 256 qubits on its Aquila-class machines. The company is working towards scaling up to higher numbers.
- The first project will focus on advancing a proof-of-concept for quantum machine learning, while the second project aims to enhance existing quantum computation schemes.
- The five partner projects focus on predicting events impacting insurers (Moody’s), quantum machine learning (Harvard), Gibbs sampling (BlueQubit), quantum-aided drug discovery (Polaris Quantum Biotech), and benchmarking of quantum simulations (The University of Padova).
