CAREER: SQALE: Scalable Quantum Architectures with LDPC Error-correction

Qubits, or quantum bits, are the fundamental units of quantum computers and they can be built from a variety of technologies, e.g., neutral atoms, ions, superconducting circuits, photons. Irrespective of the technology used to build them, they are fragile and can retain their coherence (to preserve the stored information) only for a limited time, which typically ranges from microseconds to seconds. Besides, when the qubits are manipulated through external forces to perform useful computation, the application of these forces can also be faulty, leading to many errors in the computational process. The field of quantum error correction and fault tolerance tries to protect information from qubit decoherence and faulty processes by introducing a controlled amount of redundancy in the way the information is stored. A classical analogy would be to repeat, or clone, each bit of information so that if less than half of the repetitions are corrupted, then the majority vote will still recover the original bit. Since quantum mechanics forbids the cloning of qubits, the research community has developed very sophisticated codes and decoders to store and manipulate information in the presence of noise and faults. These codes work by checking parities of information stored in the qubits, which necessitates interactions between qubits that are far apart. But in many hardware technologies, engineering such long-range interactions is very challenging, so it is imperative to develop scalable methods to address this challenge. This project will develop such a scalable solution by networking multiple small quantum processors and using network-provided entanglement to circumvent long-range interactions. These networked quantum computers can then solve outstanding challenges in digital security, drug discovery, materials design, and other applications with a major positive impact on society. The research will advance knowledge by investigating both fundamental and translational aspects of a networked architecture for optimal quantum low-density parity-check (QLDPC) codes. The project will create new synergies between quantum computing and networking, e.g., through error correction-based entanglement purification protocols. The expected intellectual contributions of this project include: (1) network geometries needed to implement these codes; (2) a fundamental understanding of the challenges when performing encoded computation and error correction in a networked setting; (3) protocols to purify (potentially encoded) entangled states that enable these networked operations on the QLDPC codes under investigation; (4) detailed code-level and network-level simulations using tools such as Stim to evaluate the architecture; and (5) a detailed comparison of the proposed approach with the standard monolithic architecture for fault tolerant quantum computing. The project’s Education Plan will provide early exposure to linear algebra and its application in quantum research for STEM education. Undergraduate students will be mentored by Ph.D. students for a mutually beneficial experience. These activities integrate well with the investigator’s work for the NSF Engineering Research Center for Quantum Networks and the Arizona Quantum Initiative, whose mechanisms can help scale the proposed education activities. The early exposure to linear algebra is also broadly beneficial for students interested in other fields such as artificial intelligence and data science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2540171 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Narayanan Rengaswamy | Institution: University of Arizona, TUCSON, AZ | Award Amount: $477,555 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2540171 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2540171.html