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A Scalable Microarchitecture for Efficient Instruction-Driven Signal Synthesis and Coherent Qubit Control

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arxiv 2205.06851 v1 pith:STSNDEBT submitted 2022-05-13 quant-ph

classification quant-ph
keywords controlqubitquantumarchitectureexecutioninstructionlimitedscalable
verification ladder T0 review T1 audit T2 compute T3 formal
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Execution of quantum algorithms requires a quantum computer architecture with a dedicated quantum instruction set that is capable of supporting translation of workloads into actual quantum operations acting on the qubits. State-of-the-art qubit control setups typically utilize general purpose test instruments such as arbitrary waveform generators (AWGs) to generate a limited set of waveforms or pulses. These waveforms are precomputed and stored prior to execution, and then used to produce control pulses during execution. Besides their prohibitive cost and limited scalability, such instruments suffer from poor programmability due to the absence of an instruction set architecture (ISA). Limited memory for pulse storage ultimately determines the total number of supported quantum operations. In this work, we present a scalable qubit control system that enables efficient qubit control using a flexible ISA to drive a direct digital synthesis (DDS) pipeline producing nanosecond-accurate qubit control signals dynamically. The designed qubit controller provides a higher density of control channels, a scalable design, better programmability, and lower cost compared to state-of-the-art systems. In this work, we discuss the new qubit controller's capabilities, its architecture and instruction set, and present experimental results for coherent qubit control.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GPU-Accelerated Host-Aware Dead-Measurement Detection in Hybrid Quantum--Classical Programs: Full Version

    quant-ph 2026-07 conditional novelty 6.5 of 10

    Semantics-aware abstract interpretation of classical host code finds non-contributory measurements, enabling ~38% gate removal standalone and >30% after SOTA circuit optimizers, with GPU speedups via levelized SSA.

  2. NeuroQD: A Learning-Based Simulation Framework For Quantum Dot Devices

    cond-mat.mes-hall 2025-09 conditional novelty 6.0 of 10

    A U-Net trained on one small quantum dot device predicts electrostatic potentials for devices up to 99 dots, giving a 1000x faster simulator that reproduces real-device tuning features.

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