Applying quantum physics to build quantum devices for computing has recently become reality with companies such as Google, IBM, and Intel making prototypes for algorithm experimentation. These devices demonstrate that binary computing states (0 vs. 1) can be manipulated using the rules of quantum mechanics to include superposition, entanglement, and wave interference as fundamentally new avenues for computing algorithms. While quantum algorithms have already shown in-principle speed-ups over classical computation for certain classes of problems such as factoring prime numbers, finding new algorithms for statistical computation such as machine learning is ongoing. The key differences between classical and quantum computing will be discussed in the context addressing genomics questions through simple quantum machine learning examples.