"Quantum Thursdays - Architectures for Multinode Superconducting Quantum Computers"

Presented by Michael DeMarco, Brookhaven National Lab

Thursday, May 18, 2023, 12:00 pm — Videoconference / Virtual Event (see link below)

Many proposals to scale quantum technology rely on modular or distributed designs where individual quantum processors, called nodes, are linked together to form one large multinode quantum computer (MNQC). One scalable method to construct an MNQC is using superconducting quantum systems with optical interconnects. However, a key limiting factor of these machines will be internode gates, which may be two to three orders of magnitude noisier and slower than local operations. Surmounting the limitations of internode gates will require a range of techniques, including improvements in entanglement generation, the use of entanglement distillation, and optimized software and compilers, and it remains unclear how improvements to these components interact to affect overall system performance, what performance from each is required, or even how to quantify the performance of a component. We employ a `co-design' inspired approach to quantify overall MNQC performance in terms of hardware models of internode links, entanglement distillation, and local architecture. In the particular case of superconducting MNQCs with microwave-to-optical interconnects, we uncover a tradeoff between entanglement generation and distillation that threatens to degrade MNQC performance. We show how to navigate this tradeoff in the context of algorithm performance, layout how compilers and software should optimize the balance between local gates and internode gates, and discuss when noisy quantum internode links have an advantage over purely classical links. Using these results, we introduce a research roadmap for the realization of early MNQCs, which illustrates potential improvements to the hardware and software of MNQCs and outlines criteria for evaluating the improvement landscape, from progress in entanglement generation to the use of quantum memory in entanglement distillation and dedicated algorithms such as distributed quantum phase estimation. While we focus on superconducting devices with optical interconnects, our approach is general across MNQC implementations.

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