Understanding Quantum Control Processor Capabilities and Limitations through Circuit Characterization

Butko, Anastasiia; Michelogiannakis, George; Williams, Samuel; Iancu, Costin; Donofrio, David; Shalf, John; Carter, Jonathan; Siddiqi, Irfan

doi:10.1109/icrc2020.2020.00011

Understanding Quantum Control Processor Capabilities and Limitations through Circuit Characterization

Conference · Mon Jan 18 04:00:00 EST 2021 · 2020 International Conference on Rebooting Computing (ICRC)

DOI:https://doi.org/10.1109/icrc2020.2020.00011· OSTI ID:1721660

Butko, Anastasiia ^[1]; Michelogiannakis, George ^[1]; Williams, Samuel ^[1]; Iancu, Costin ^[1]; Donofrio, David ^[1]; Shalf, John ^[1]; Carter, Jonathan ^[1]; Siddiqi, Irfan ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Continuing the scaling of quantum computers hinges on building classical control hardware pipelines that are scalable, extensible, and provide real time response. The instruction set architecture (ISA) of the control processor provides functional abstractions that map high-level semantics of quantum programming languages to low-level pulse generation by hardware. Here, we provide a methodology to quantitatively assess the effectiveness of the ISA to encode quantum circuits for intermediate-scale quantum devices with O(10²) qubits. The characterization model that we define reflects performance, the ability to meet timing constraint implications, scalability for future quantum chips, and other important considerations making them useful guides for future designs. Using our methodology, we propose scalar (QUASAR) and vector (qV) quantum ISAs as extensions and compare them with other ISAs in metrics such as circuit encoding efficiency, the ability to meet real-time gate cycle requirements of quantum chips, and the ability to scale to more qubits.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

DOE Contract Number:: AC02-05CH11231

OSTI ID:: 1721660

Conference Information:: Journal Name: 2020 International Conference on Rebooting Computing (ICRC) Journal Volume: 2020

Country of Publication:: United States

Language:: English

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