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.
A Scalable Microarchitecture for Efficient Instruction-Driven Signal Synthesis and Coherent Qubit Control
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abstract
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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NeuroQD: A Learning-Based Simulation Framework For Quantum Dot Devices
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.