The Workforce Of The Future

At A Glance:

Quantum machine learning is an exciting and rapidly growing field at the intersection of quantum computing and machine learning. With the advent of the Noisy Intermediate-Scale Quantum (NISQ) era, we are witnessing a major shift in computing power and the potential for new breakthroughs in areas that were once considered intractable for classical computers.

In particular, quantum machine learning is expected to have a significant impact on two broad areas: non-convex optimization and combinatorial optimization. These are both problems that are notoriously difficult for classical computers to solve due to the exponential growth in computational complexity as the problem size increases.

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By leveraging the powerful laws of quantum mechanics, such as superposition and entanglement, quantum machine learning has the potential to solve these problems significantly faster than classical algorithms. This is especially important as data sets continue to grow in size and complexity, making it increasingly difficult for classical computers to handle the computations involved.

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While quantum machine learning is still a relatively new field, its potential impact on our future cannot be overstated. As we continue to explore the capabilities of NISQ-era quantum computers and beyond, we can expect to see even more exciting developments in this area and potentially solve some of the most complex problems facing our world today.

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Here are some of the areas QML will disrupt:

• Understanding nanoparticles

• The creation of new materials through molecular and atomic maps

• Molecular modeling to discover new drugs and medical research

• Understanding the deeper makeup of the human body

• Enhanced pattern recognition and classification

• Furthering space exploration

• Creating complete connected security through merging with IoT and blockchain

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What We Do:

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At Qulabs we are building Quantum tools, APIs, AI tools, APIs for various applications. Our solutions provide easy to use interface for leveraging Classical-Quantum algorithms. This Classical-Quantum hybrid approach is required in advance use cases to obtain speed up in optimization workloads in various applications involving Combinatorial Optimization Problems.

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Quatum-Finance is a very promising application of Quantum Computing. Among the huge variety of problems currently, Quantum Computing is good for portfolio optimization, stock-option pricing, and fraud detection. Many problems in finance can be expressed as optimization problems. These are tasks that are particularly hard for classical computers, but find a natural formulation using quantum optimization methods. In recent years, this field has experienced tremendous growth, partly due to the commercial availability of quantum annealers and quantum computers.

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Our Solutions Are Quantum Agnostic:

As different problems are tailored for different platforms, we are building our algorithms to run on all possible quantum hardware platforms:

• Superconducting

• Photonic

• Ion Traps

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Our Solutions Are Quantum Inspired:

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Suitable for the current NISQ era, we are developing new algorithms that can be applied now using different strategies:

• Tensor Networks

• Digital Annealing

• Quantum Inspired Optimization

• Artificial Intelligence

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