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The practical implementation of many quantum algorithms known today isbelieved to be limited by the coherence time of the executing quantum hardwareand quantum sampling noise. Here we present a machine learning algorithm,NISQRC, for qubit-based quantum systems that enables processing of temporaldata over durations unconstrained by the finite coherence times of constituentqubits. NISQRC strikes a balance between input encoding steps and mid-circuitmeasurements with reset to endow the quantum system with an appropriate-lengthpersistent temporal memory to capture the time-domain correlations in thestreaming data. This enables NISQRC to overcome not only limitations imposed byfinite coherence, but also information scrambling or thermalization inmonitored circuits. The latter is believed to prevent known parametric circuitlearning algorithms even in systems with perfect coherence from operatingbeyond a finite time period on streaming data. By extending the Volterra Seriesanalysis of dynamical systems theory to quantum systems, we identifymeasurement and reset conditions necessary to endow a monitored quantum circuitwith a finite memory time. To validate our approach, we consider the well-knownchannel equalization task to recover a test signal of $N_{ts}$ symbols that issubject to a noisy and distorting channel. Through experiments on a 7-qubitquantum processor and numerical simulations we demonstrate that $N_{ts}$ can bearbitrarily long not limited by the coherence time.

Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data