More and more smartphones are being injected with malware that steals personal information or money from them. So, this project is designed to combat this problem.

What it does

This project helps differentiate between safe and malicious applications on mobile phones with quantum machine learning.

How we built it

We built this project through the Jupyter notebook with hours and hours of coding and hard work.

Challenges we ran into

The time restraint pressured us to work faster get it completed as soon as possible. This is also a new quantum algorithm so we also needed to spend the time learning and perfecting it.

Accomplishments that we're proud of

We were able to successfully code this project, giving us the results that we expected that could help identify malicious applications.

What we learned

We learned about how quantum computing can revolutionize cybersecurity. Quantum computing can solve problems that classical computing is unable to address since it has the capability to solve the world’s most complex problems. Additionally, it taught us how useful quantum machine learning is to business practices.

What's next for Malware Detection- A Quantum Machine Learning Approach

We can further find ways to provide protection against other types of cyberattacks, be used to defend against newer cybersecurity attacks, and be used to identify and block attempts to steal information. Also, we could design and develop new types of encryption using quantum computing, such as the aforementioned quantum key distribution method. Design and development of quantum-safe procedures and measures are needed to secure the data and devices.

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