Amazon Braket is a fully managed quantum computing service that helps researchers and developers get started with the technology to accelerate research and discovery. Amazon Braket provides a development environment for you to explore and build quantum algorithms, test them on quantum circuit simulators, and run them on different quantum hardware technologies.
Building useful quantum applications requires new skills and potentially radically different approaches to problem solving. Building this expertise will take time and requires access to quantum technologies and programming tools. Amazon Braket helps organizations begin exploring the potential of quantum computers today, and prepare for the future.
Although many approximation methods have been introduced, simulating quantum systems in physics and chemistry still represents a significant challenge for classical computers. However, the unique features of quantum computers and the ability to natively manipulate quantum mechanical states creates the potential to efficiently solve important problems such as describing the electronic structure of molecules. Simulation of quantum systems will have broad applications including the design of new materials and catalysts, drug discovery, and the exploration of high-temperature superconductors.
Optimization problems are ubiquitous across many industries including telecommunications, supply chain logistics, and financial services. Finding the optimal approach from a set of alternatives can overwhelm classical computers as the number of possible combinations drives up complexity. Quantum computing can be used to address a wide range of these problems, for instance in the field of combinatorial optimization by accelerating linear programming algorithms and Monte Carlo methods. While future error-corrected quantum computers have provable speed-ups, near-term devices can be used to run heuristic algorithms which might already offer an advantage.
Quantum machine learning
Machine learning provides methods for systems to learn from data, identify patterns, and make decisions in an autonomous way. Scalable error-corrected quantum computers can improve many widely used tools in machine learning including recommendation systems and support-vector machines for classification. Quantum computing also has the potential of offering a richer class of learning models.
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