While its recent ascent to prominence is notable, tools for QML are extensions of low-level quantum computing the roots of ML extend back to the early 1960s [2], reflecting packages that require exhaustive knowledge in quantum a path of varied progress and challenges. Besides some initial computing and ML and often require manipulations on the set-backs [3] the foundations of today's deep neural networks qubit level [14-18]. In this work, we aim to bridge the gap that drive a lot of the recent advancements have already for the success factor (c) and introduce sQUlearn, an easy-touse been established almost half a century ago [4, 5]. The reasons and NISQ-ready python library for QML which aims to for the accelerated breakthroughs of the past decade are essentially democratizing access to QML. threefold: (a) an increased computational resources, (b) the availability of large-scale data, and (c) the emergence Given the close relationship between the fields of ML and of development tools that abstract away low-level complexity. QML, we strive for high compatibility with already available While these factors have enabled remarkable breakthroughs, tools.