Quantum machine learning is an emerging interdisciplinary research area intersecting quantum physics & machine learning. It is often associated with machine learning methods applied to data from quantum experiments.

Quantum computers provide the computational advantage to classify objects in nth dimensions. Quantum machine learning is applied in the areas of nano-particles, material discovery, chemical design, drug design, pattern recognition & classification.

Use cases include: creating new materials for space tech, wearable tech, renewable energy, nanotech, new drugs & chemical combos, genetic science, biometrics, IoT devices & universe discovery.

Quantum walks are a quantum analogue to random walks and have substantially reduced time-consumption in Monte Carlo simulations for mixing of Markov chains (Ashley Montanaro, 2015).

These quantum algorithms are applied in wealth management & trading.

Key TakeAways

-Discover classic machine learning & quantum machine learning methods & applications in various areas.

-Learn how quantum algorithms are applied in wealth management and trading.

-A demo on quantum algorithms

Bio: Bhagvan Kommadi is the Founder of Architect Corner & has around 18 years’ experience in the industry, ranging from large scale enterprise development to helping incubate software product startups. He has a Masters in Industrial Systems Engineering at Georgia Institute of Technology (1997) & Bachelors in Aerospace Engineering from Indian Institute of Technology, Madras (1993).

He is a member of IFX forum, Oracle JCP and participant in Java Community Process. He founded Quantica Computacao, the first quantum computing startup in India. Bhagvan has engineered & developed simulators & tools in the area of quantum technology using IBM Q, Microsoft Q# & Google QScript. The company's focus is on developing quantum cryptographic tools, which will provide quantum-proof data security that will help banking institutions protect their transactions.

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Artificial Intelligence
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