Quantum Technology: Computing MicroMasters

What you will learn:
- How to mathematically describe basic quantum phenomena relevant to quantum technologies.
- Engineering challenges of quantum computation.
- How to formulate and apply basic quantum algorithms.
- Quantum communication and sensing technologies.
- How to analyze various quantum computing architectures.

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Applied Quantum Computing III: Algorithm and Software (edX)

Mar 25th 2024
Applied Quantum Computing III: Algorithm and Software (edX)
Course Auditing
Categories
Effort
Languages
Learn domain-specific quantum algorithms and how to run them on present-day quantum hardware. This course is part III of the series of Quantum computing courses, which covers aspects from fundamentals to present-day hardware platforms to quantum software and programming. The goal of part III is to discuss some of [...]

Quantum Detectors (edX)

Feb 12th 2024
Quantum Detectors (edX)
Course Auditing
Categories
Effort
Languages
Learn about quantum sensors and devices that extract maximal information from the world around us. Classical detectors and sensors are ubiquitous around us from heat sensors in cars to light detectors in a camera cell phone. Leveraging advances in the theory of noise and measurement, an important paradigm of [...]

Applied Quantum Computing II: Hardware (edX)

Feb 12th 2024
Applied Quantum Computing II: Hardware (edX)
Course Auditing
Categories
Effort
Languages
Learn how present-day material platforms are built to perform quantum information processing tasks. This course is part 2 of the series of Quantum computing courses, which covers aspects from fundamentals to present-day hardware platforms to quantum software and programming. The goal of part 2 is to provide the essential [...]

Applied Quantum Computing I: Fundamentals (edX)

Jan 8th 2024
Applied Quantum Computing I: Fundamentals (edX)
Course Auditing
Categories
Effort
Languages
Learn the fundamental postulates of quantum mechanics and how they can be mapped onto present-day quantum information processing models, including computation, simulation, optimization, and machine learning.