Quantum Machine Learning
By Bhagvan Kommadi (@bhaggu)
Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum physics and machine learning. It is often associated with machine learning methods applied to data generated from quantum experiments.
Quantum computers will provide the computational advantage to classify objects in nth dimensions. The areas that quantum machine learning is applied is in the areas of nano-particles, material discovery, chemical design, drug design, pattern recognition, and classification.
The applicable use cases are creating new materials that can be applied in space tech, wearable tech, renewable energy, nanotech, new drugs and chemical combinations, genetic science, biometrics, IoT devices, and universe discovery.
Quantum walks are a quantum analogue to random walks and have substantially reduced the time-consumption in Monte Carlo simulations for mixing of Markov chains as reported by Ashley Montanaro (2015). These quantum algorithms are applied for investment strategies in wealth management and trading.
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