ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements
arxiv(2024)
Abstract
Similar to reading the transistor state in classical computers, identifying
the quantum bit (qubit) state is a fundamental operation to translate quantum
information. However, identifying quantum state has been the slowest and most
error-prone operation on superconducting quantum processors. Most existing
state discrimination algorithms have only been implemented and optimized "after
the fact" - using offline data transferred from control circuits to host
computers. Real-time state discrimination is not possible because a
superconducting quantum state only survives for a few hundred us, which is much
shorter than the communication delay between the readout circuit and the host
computer (i.e., tens of ms). Mid-circuit measurement (MCM), where measurements
are conducted on qubits at intermediate stages within a quantum circuit rather
than solely at the end, represents an advanced technique for qubit reuse. For
MCM necessitating single-shot readout, it is imperative to employ an in-situ
technique for state discrimination with low latency and high accuracy. This
paper introduces QubiCML, a field-programmable gate array (FPGA) based system
for real-time state discrimination enabling MCM - the ability to measure the
state at the control circuit before/without transferring data to a host
computer. A multi-layer neural network has been designed and deployed on an
FPGA to ensure accurate in-situ state discrimination. For the first time,
ML-powered quantum state discrimination has been implemented on a radio
frequency system-on-chip FPGA platform. The deployed lightweight network on the
FPGA only takes 54 ns to complete each inference. We evaluated QubiCML's
performance on superconducting quantum processors and obtained an average
accuracy of 98.5
standard real-time state discrimination method for the quantum community.
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