Quantum Technology: Computing and Sensing

Overview

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Classical detectors and sensors are ubiquitous – examples include heat sensors in cars and light detectors in camera cell phones. Leveraging advances in the theory of noise and measurement, an important paradigm of quantum metrology has emerged. Here, ultra-precision measurement devices collect maximal information from the world around us at the quantum limit. This enables a new frontier of perception that promises to impact machine learning, sensing platforms, autonomous navigation, surveillance strategies, information processing, and communication systems.

In this MicroMasters, students will learn the fundamentals of state-of-the-art quantum detectors and sensors.

In addition, they will also learn the important foundational principles of quantum computing. They will be introduced to state-of -the-art quantum computing architectures and their working principles and will also gain hands-on experience programming real quantum machines.

Courses in this program are designed to introduce both physics and engineering aspects of quantum technologies, in particular quantum detectors/sensors/networking/computing, with minimal prerequisite courses.

Syllabus

Courses under this program:
Course 1: Quantum Detectors and Sensors

Learn how to analyze and design quantum sensors and devices that extract maximal information from the world around us.

Course 2: Applied Quantum Computing I: Fundamentals

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.

Course 3: Applied Quantum Computing II: Hardware

Learn how present-day material platforms are built to perform quantum information processing tasks.

Course 4: Applied Quantum Computing III: Algorithm and Software

Learn domain-specific quantum algorithms and how to run them on present-day quantum hardware.

Courses

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17 weeks, 6-9 hours a week, 6-9 hours a week
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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 quantum metrology has emerged. Here, ultra-precision measurement devices collect maximal information from the world around us at the quantum limit. This enables a new frontier of perception that promises to impact machine learning, autonomous navigation, surveillance strategies, information processing, and communication systems.

Students in this in-depth course will learn the fundamentals about state-of-the-art quantum detectors and sensors. They will also learn about quantum noise and how it limits quantum devices. The primary goal of the course is to empower students with a critical and deep understanding of emerging applications at the quantum-classical boundary. This will allow them to adopt quantum detectors and sensors for their own endeavors.
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5 weeks, 7-8 hours a week, 7-8 hours a week
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This fundamentals course is part 1 of the series of quantum computing courses and covers aspects from fundamentals to present-day hardware platforms to quantum software and programming. This course provides the essential foundations required to understand computing models built from the principles of quantum mechanics.

This course requires a minimal set of engineering and science prerequisites but will allow students to develop a physical and intuitive understanding of the topics.

Attention:

Quantum Computing 1: Fundamentals is an essential prerequisite to Quantum Computing 2: Hardware and Quantum Computing 3: Algorithm and Software. Learners should plan to complete Fundamentals before enrolling in the Hardware or the Algorithm and Software courses.
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5 weeks, 7-8 hours a week, 7-8 hours a week
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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 understanding of how the fundamental quantum phenomena discussed in part 1 can be realized in various material platforms and the underlying challenges faced by each platform. To this end, we will focus on how quantum bits (qubits, the building block of quantum information processing) can be defined in each platform, how such qubits are manipulated and interconnected to form larger systems, and the sources of errors in each platform.

With an emphasis on present-day leading candidates, we will discuss following specific quantum material platforms:
- Superconductor-based
- Atom/ion traps-based
- Spin-based
The material will appeal to engineering students, natural sciences students, and professionals whose interests are in using and developing quantum information processing technologies.

Attention:

Quantum Computing 1: Fundamentals is an essential prerequisite to Quantum Computing 2: Hardware and Quantum Computing 3: Algorithm and Software. Learners should plan to complete Fundamentals (1) before enrolling in the Hardware (2) or the Algorithm and Software (3) courses.

Alternatively, learners can enroll in courses 2 or 3 if they have solid experience with or knowledge of quantum computing fundamentals, including the following: 1) postulates of quantum mechanics; 2) gate-based quantum computing; 3) quantum errors and error correction; 3) adiabatic quantum computing; and 5) quantum applications and NISQ-era.
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5 weeks, 7-8 hours a week, 7-8 hours a week
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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 the key domain-specific algorithms that are developed by exploiting the fundamental quantum phenomena (e.g. entanglement)and computing models discussed in part I. We will begin by discussing classic examples of quantum Fourier transform and search algorithms, along with its application for factorization (the famous Shor’s algorithm). Next, we will focus on the more recently developed algorithms focusing on applications to optimization, quantum simulation, quantum chemistry, machine learning, and data science.

A particularly exciting recent development has been the emergence of near-intermediate scale quantum (NISQ) computers. We will also discuss how these machines are driving new algorithmic development. A key aspect of the course is to provide hands-on training for running (few qubit instances of) the quantum algorithms on present-day quantum hardware. For this purpose, we will take advantage of the availability of cloud-based access to quantum computers and quantum software.

The material will appeal to engineering students, natural sciences students, and professionals whose interests are in using as well as developing quantum technologies.

Attention:

Quantum Computing 1: Fundamentals is an essential prerequisite to Quantum Computing 2: Hardware and Quantum Computing 3: Algorithm and Software. Learners should plan to complete Fundamentals (1) before enrolling in the Hardware (2) or the Algorithm and Software (3) courses.

Alternatively, learners can enroll in courses 2 or 3 if they have solid experience with or knowledge of quantum computing fundamentals, including the following: 1) postulates of quantum mechanics; 2) gate-based quantum computing; 3) quantum errors and error correction; 3) adiabatic quantum computing; and 5) quantum applications and NISQ-era.