The rapid evolution of malware and their families pose additional challenges to the existing malware detection solutions. There is a need of fast and accurate detection of malware due to the advancement of AI-enabled malware. Since decade, Windows based malware detection is evolving due to the rapid inclusion of new and improved malware families. The manual inspection and detection of such malware is inappropriate solution due to their dynamically changing and self-replicating behavior over the network. To address such issues, machine learning techniques have been widely explored. However, such techniques are computationally infeasible for fast detection of malware due to their deep analysis on malware executable and accurate detection. Thus, quantum computing-enabled machine learning techniques can be further investigated for malware detection. This paper analyzes the performance of the quantum machine learning techniques in Windows based malware detection. For the experimental analysis, recent malware datasets such as Ember and BODMAS both based on the LIEF project are considered and evaluated the performance of the quantum machine learning techniques for malware detection.