  In this work, we present a Quantum Hopfield Associative Memory (QHAM) and
demonstrate its capabilities in simulation and hardware using IBM Quantum
Experience. The QHAM is based on a quantum neuron design which can be utilized
for many different machine learning applications and can be implemented on real
quantum hardware without requiring mid-circuit measurement or reset operations.
We analyze the accuracy of the neuron and the full QHAM considering hardware
errors via simulation with hardware noise models as well as with implementation
on the 15-qubit ibmq_16_melbourne device. The quantum neuron and the QHAM are
shown to be resilient to noise and require low qubit overhead and gate
complexity. We benchmark the QHAM by testing its effective memory capacity and
demonstrate its capabilities in the NISQ-era of quantum hardware. This
demonstration of the first functional QHAM to be implemented in NISQ-era
quantum hardware is a significant step in machine learning at the leading edge
of quantum computing.
