# Overcoming the coherence time barrier in quantum machine learning on temporal data

## TL;DR

This paper introduces NISQRC, a quantum machine learning algorithm that enables processing of long temporal data without being limited by quantum hardware's coherence time.

## Contribution

NISQRC uses mid-circuit measurements and resets to overcome coherence time and sampling noise limitations in quantum machine learning.

## Key findings

- NISQRC can process arbitrarily long temporal data, confirmed through simulations and experiments on a 7-qubit quantum processor.
- The algorithm's effectiveness is validated using a channel equalization task to recover distorted test signals.
- Volterra Series analysis confirms the algorithm's ability to maintain persistent temporal memory in the quantum system.

## Abstract

The practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset operations to reduce circuit executions, while still maintaining an appropriate length persistent temporal memory in the quantum system, confirmed through the proposed Volterra Series analysis. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling in monitored circuits and sampling noise, problems that persist even in hypothetical fault-tolerant quantum computers that have yet to be realized. To validate our approach, we consider the channel equalization task to recover test signal symbols that are subject to a distorting channel. Through simulations and experiments on a 7-qubit quantum processor we demonstrate that NISQRC can recover arbitrarily long test signals, not limited by coherence time.

Inherent limitations on continuously measured quantum systems calls into question whether they could even in principle be used for online learning. Here, the authors experimentally demonstrate a quantum machine learning framework for inference on streaming data of arbitrary length, and provide a theory with criteria for the utility of their algorithm for inference on streaming data.