Quantum state classifier using reservoir computation

Abstract

A quantum state classifier comprises: a reservoir computing circuit for post-processing a quantum bit to obtain a readout signal; and a readout circuit, coupled to the reservoir computing circuit, for discriminating a quantum state of a qubit from the readout signal from among a plurality of possible quantum states. The readout circuitry is trained in a calibration process activated by a particular quantum state of each of the plurality of quantum states, respectively, such that the weights within the linear readout circuitry are updated by a small batch learning of each of the plurality of measurement sequences for the calibration process. The readout circuit generates a binary output after the plurality of measurement sequences during a post-calibration classification process for the test qubits. The quantum state classifier also includes a controller coupled to the readout circuitry, selectively actuatable to output a control pulse in response to a quantum state of the test qubit indicated by the binary output.

Description

Background technique [0001] The present invention relates generally to quantum computing, and more particularly to quantum state classifiers using reservoir computing. Deploying machine learning algorithms requires expensive digital electronics (e.g., FPGAs) and tuning of hyperparameters for each qubit, which is not feasible for large-scale quantum computers. Furthermore, short time measurements require fast analog-to-digital converters (ADCs), which are power consuming and cannot be integrated into cryogenic electronics due to cooling power limitations. Therefore, there is a need for quantum state classifiers that can overcome the aforementioned limitations. Contents of the invention [0002] According to an aspect of the present invention, a quantum state classifier is provided. The quantum state sorter includes reservoir computation circuitry for post-processing the qubits to obtain readout signals. The quantum state sorter also includes linear readout circuitry operat...

AI Technical Summary

Problems solved by technology

Deploying machine learning algorithms requires expensive digital electronics (e.g., FPGAs) and tuning of hyperparameters for each qubit, which is not feasible for large-scale quantum computers

Furthermore, short measurements require fast analog-to-digital converters (ADCs), which are power consuming and cannot be integrated into cryogenic electronics due to cooling power limitations

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