Kavli Affiliate: Irfan Siddiqi
| First 5 Authors: Neel R. Vora, Yilun Xu, Akel Hashim, Neelay Fruitwala, Ho Nam Nguyen
| Summary:
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
susceptible to errors 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% with only 500 ns readout. QubiCML has the potential to be the
standard real-time state discrimination method for the quantum community.
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