Quantum Computing Education - Workforce Development

Be responsible for your own professional development journey.

2020 marked the launch of a decade of explosive growth for the enterprise quantum computing market. This disruptive technological innovation is projected to reach $9.1B in annual revenue by 2030 (vs. $260M in 2020). Although, global quantum technology research reports reveal near-term trends with the potential to disrupt global commerce, the quantum workforce is not yet established to meet the anticipated demand for this important industry of the future.

Our new flagship Quantum Computing Education - Workforce Development Program is designed to empower our community of lifelong learners with quantum technology industry knowledge for global impact. At IEEE, we are dedicated to advancing technology for the benefit of humanity through educational activities. We aim to serve professionals involved in all aspects of science and technology that underlie modern civilization.

2021 Program Portfolio

 

Upcoming Courses

Check back soon for more upcoming courses.

 

Courses On-Demand

Quantum Engineering: Photonics in Quantum Computing and Quantum Networking
Wednesday, 28 July 2021 at 12pm ET

View this course on-demand

Abstract
This masterclass will review how photonics play a central role in several of the leading candidate technologies for building quantum computers and quantum networks. We will discuss trapped ions, trapped neutral atoms, optically active defects and quantum dots in solid-state materials, and purely photonic approaches for realizing quantum processors. We will also review how superconducting circuits, which don’t natively involve optics, can be coupled to photonics. The emphasis will be on giving a broad survey of the various photonics-related quantum technologies and the current state-of-the-art in each.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Dr. Peter McMahon, Assistant Professor, Cornell University School of Applied and Engineering Physics (AEP)

Dr. Peter McMahonPeter McMahon is an assistant professor of Applied and Engineering Physics at Cornell University. His research lab investigates how to harness physical systems to perform computations more energy-efficiently or faster (or both) than conventional computers. He works on both classical and quantum computing with a variety of platforms, including photonics and superconducting circuits. Peter received his Ph.D. from Stanford University in Electrical Engineering and performed his postdoctoral work at Stanford in Applied Physics before moving to Cornell. His is a CIFAR Azrieli Global Scholar in Quantum Information Science and won a Google Quantum Research Award in 2019.

 

 

Automation and Synthesis of Quantum Circuits
Wednesday, 12 May 2021 at 12pm ET

View this course on-demand

Download presentation slides (PDF, 46 MB)

Abstract
This class will review current limitations of designing quantum circuits, typically done at the gate level or using specific functional building blocks; introduce automation and computer-aided design (CAD) technologies for quantum algorithm design; and demonstrate how these technologies unlock new frontiers of creativity in quantum algorithm development.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Amir Naveh, Co-Founder and Head of Algorithms at Classiq Technologies

Amir NavehAmir Naveh is the co-founder and Head of Algorithms at Classiq Technologies, an exceptional quantum startup that recently received significant venture funding. Classiq enables the development of quantum algorithms through automation and synthesis. Amir is a former leader of large R&D teams and projects in the Israeli Ministry of Defence and Intelligence community and a "Talpiot" alumnus.

 

 

A Hands-On Approach to Quantum Computing Learning - Comics and Coding with Q#
Wednesday, 5 May 2021 at 10am ET

View this course on-demand

Download presentation slides (PDF, 13 MB)

Abstract
This course offers a unique opportunity to learn about quantum computing through an intuitive series of comics. It will provide new learners a fun way to understand key concepts. Learners already familiar with the fundamentals will experience a new perspective on quantum computing algorithms. Q# will be used to construct Grover’s algorithm hands-on.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Dr. Kitty Y. M. Yeung, Microsoft Quantum, Sr. Program Manager

Dr. Kitty Y. M. YeungDr. Kitty Yeung is a physicist, artist, maker, fashion designer and musician based in Germany. She works at Microsoft Quantum Systems as a Senior Program Manager on quantum computing education. Kitty is the producer of MS Learn quantum modules and the Quantum Learning website with customized learning materials, creator of comic series Quantum Computing through Comics, lecturer at HackadayU and Microsoft Reactor on Quantum Computing, founder & designer of sustainable and STEAM fashion brand, Art by Physicist, and creative technologist & lead of the Fashion Hack at Microsoft.

Kitty worked as a research scientist, hardware engineer and user experience designer at Intel, and Manager of the Microsoft Garage program in Silicon Valley, California. She received her Ph.D. in Applied Physics from Harvard University (Thesis: Engineering Plasmonic Circuits in 2-Dimensional Electron Systems) and a M.Sci., B.A. and M.A. in Natural Sciences from University of Cambridge. Kitty's career has been focusing on physics while pursuing the integration of technology, science, design and art. Kitty frequently gives technical and career talks reflecting her passion and experience in quantum computing, wearables industry, digital transformation, and internal startups. See her work on www.artbyphysicistkittyyeung.com

 

 

A New Approach to Quantum Machine Learning
Wednesday, 28 April 2021 at 12pm ET

View this course on-demand

Abstract
In recent years, there has been an increasing interest in combining the disciplines of quantum information theory and machine learning. One of the approaches is to translate ML or DL to quantum equivalents expecting "QUANTUM" speed up in training and inference. The other approach is to explore a new direction of machine learning which utilizes the nature of quantum computing. Though we are seeing the early signs of quantum advantage (e.g analytical performance), it is still uncharted and needs to be explored. This class will discuss simple and effective approaches to construct QML algorithms and explain some of the basic and key building elements and overall algorithm architecture.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Dr. Jae-Eun Park, Program Director for Quantum Industry Solution at IBM

Dr. Jae-Eun ParkJae Eun Park is managing IBM GBS Quantum Algorithm Acceleration Teams. Currently he is working on problems in Quantum ML, AI & Machine Learning, Optimization and Simulation for multiple industry application by blending quantum and classical advanced analytics together.

He has 19 years of industry experience in business strategy, advanced analytics & modeling, and artificial intelligence. He has managed IBM Research teams in AI industry focusing on commerce sector including supply chain and marketing. Previously, he managed Research strategy and offering team for IBM Research.

He holds MBA from The New York University of Stern School of Business with Stern Scholar honor and Ph.D in Electrical Engineering and Computer Science from Arizona State University.

 

 

Quantum Engineering Bootcamp: Module 2 - Integrating Your Findings - Best Practices and Pitfalls for Setting Up a Quantum HW Test Program
Thursday, 22 April 2021 at 12pm ET

View this course on-demand

Abstract
When considering a test program comprised of multiple projects and research thrusts, it is important to take an integrated approach to ensure progress. Processes and procedures can serve as guidelines to help ensure said progress and avoid common pitfalls associated with emerging technologies such as quantum computers. This class will address the best practices and pitfalls of setting up an integrated test program.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Will Madsen, Quantum Systems Engineering and Architecture Manager, Rigetti Computing

Will MadsenWill leads systems engineering and integration efforts within the technical organization at Rigetti Computing and manages its portfolio of Department of Defense (DOD) programs. Before joining Rigetti, Will was a Developmental Engineer for the United States Air Force where he led engineering teams in flight testing and space launch operations. He holds a BS in Systems Engineering from the US Air Force Academy.

 

 

Quantum Engineering Bootcamp: Module 1 - Test and Evaluation for Quantum Devices
Tuesday, 20 April 2021 at 12pm ET

View this course on-demand

Abstract
Test and evaluation is at the heart of the push to advance the state of the art in quantum devices. Understanding and adapting test best practices from other industries is imperative to ensure efficiency and speed-up learning cycles. This class will address key considerations with test and evaluation of NISQ era hardware and will aim to educate attendees on how to best think through planning HW tests.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Will Madsen, Quantum Systems Engineering and Architecture Manager, Rigetti Computing

Will MadsenWill leads systems engineering and integration efforts within the technical organization at Rigetti Computing and manages its portfolio of Department of Defense (DOD) programs. Before joining Rigetti, Will was a Developmental Engineer for the United States Air Force where he led engineering teams in flight testing and space launch operations. He holds a BS in Systems Engineering from the US Air Force Academy.

 

 

Overview of Quantum Machine Learning Algorithms
Wednesday, 14 April 2021 at 10am ET

View this course on-demand

Download presentation slides (PDF, 2 MB)

Abstract
Quantum computers make it feasible to solve some problems that are computationally intractable on classical computers. Machine learning (ML) uses big data and statistical/mathematical modelling to solve problems and often the best ML algorithm is the one that scales best as the input grows in size. This makes quantum computers a natural fit for machine learning.

During this talk, we will give an overview of some of the algorithms that exist within quantum machine learning discussing requirements, speedup and possible limitations. This includes HLL and several applications of HLL. Our goal is to give a flavor of how quantum machine learning works and why it is a promising application for quantum computers.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructors
Dr. Troels Steenstrup Jensen, Head of KPMG Global Quantum and Head of Machine Learning at KPMG Denmark
Marco Ugo Gambetta, NewTech Consultant at KPMG Denmark

Dr. Troels Steenstrup JensenTroels is Head of Machine Learning and Quantum Technologies at KPMG Denmark and head of KPMG's Global Quantum Hub. He has a PhD in theoretical quantum mathematics and works at the intersection of mathematics, statistics, physics, computer science and business. He has been working with Machine Learning for more than 10 years and with Quantum Technologies for more than 3 years. Troels has a deep passion for technology and for bringing theory to practice - seeing technology solutions come to life at clients is his main driver. He combines a strong theoretical foundation with business understanding and pragmatic solution design in order to create value for clients.

 

 

Quantum Computing 101: Introduction to Quantum Computing for Non-Technical Learners
Wednesday, 31 March 2021 at 12pm ET

Abstract
Quantum computers could create new industries because of their unique ability to generate extraordinary power that speeds up certain types of complex calculations of great importance in a way that is simply not possible with today’s ordinary computers. They are a more powerful type of computer because they are designed to drastically improve information processing power by taking advantage of special properties of quantum mechanics. This class is designed to introduce quantum computing to non-technical learners who want to have massive quantum fun while learning about this important technology. Register today if you are planning for a career in quantum computing or if you are simply curious about quantum computing because it could shape our future.

Instructor
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Maeva GhondaMaëva Ghonda is a scientist with the unique ability to explain complex information in a manner that is easy to understand. Maëva fell for her true love -- Quantum -- while working as Quantum Scholar for the Joint Quantum Institute (JQI) for a National Institute of Standards and Technology (NIST) Fellow. She began to discover what is possible with quantum -- i.e. Quantum Teleportation and Quantum Money -- while reading intricate details of novel quantum-enabled inventions hidden in global patent documents to uncover valuable quantum technology innovations. Before this fantastic quantum meet-cute, Maëva was an engineer in aerospace where she worked on the production of the 3D printed parts for the autonomous CST-100 Starliner for NASA’s Commercial Crew Program. Moreover, she has also held cybersecurity risk management roles in healthcare and financial services. In addition to her passion for Quantum Computing and Quantum Cryptography, Maëva Ghonda is also quite obsessed with Quantum Teleportation and, of course, Quantum Money.

 

 

Spin Qubit System Integration with Advanced Semiconductor Manufacturing
Wednesday, 24 March 2021 at 12pm ET

View this course on-demand

Download presentation slides (PDF, 6 MB)

Abstract
As the field of quantum computing burgeons, many technology platforms are now in contention for realizing the dream of a useful quantum computer, which can help tackle problems conventional computers cannot. At Intel, we are developing spin qubit systems using our advanced semiconductor manufacturing facilities on 300mm wafers with recent results showing long qubit coherence times. With each wafer that reaches our measurement labs, we have more than 10,000 quantum dot test structures that can be measured. This could represent a large measurement bottleneck if it were not for first-of-a-kind tools such as our 300mm cryoprober, which is able to measure what would have normally been weeks-worth of data within a single day. The statistical data measured by this cryoprober enables critical feedback to integration and manufacturing for improvement of wafer uniformity, device performance, and process stability. By measuring at ~1.6 Kelvin, we can provide this feedback close to the final operating temperature of the spin qubit devices. Additionally, our manufacturing facilities allows us to develop a custom cryogenic CMOS control system, our Horse Ridge control chip. All these devices and systems represent a large cross section of the advanced semiconductor manufacturing process. An overview of spin qubits and this manufacturing and testing infrastructure will be given to demonstrate the role advanced semiconductor manufacturing can serve towards a useful quantum computer.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Dr. Lester Lampert, Quantum Computing Engineer at Intel Corporation

Dr. Lester LampertLester joined the Quantum Computing program at Intel in 2017 and currently works in the Quantum Computing Measurement Lab with a focus on spin qubit control and system integration. Before joining Intel, Lester spent many years studying two-dimensional materials systems, specializing in long-distance spin transport and wafer scale manufacture of graphene. He holds a Ph.D. in Applied Physics from Portland State University and a B.S. in Engineering Physics from University of Wisconsin-Platteville.

 

 

Implications of Quantum Technologies for Cybersecurity
Wednesday, 17 March 2021 at 10am ET

View this course on-demand

Download presentation slides (PDF, 3 MB)

Abstract
Quantum computers pose a threat to cyber security as the core algorithms of current public-key infrastructure are easily broken by a sufficiently large quantum computer. Further escalating the issue, information that needs to stay secure for some time in the future is at risk of "harvest now and decrypt later" attacks. Fortunately, quantum technology also bring means to overcome this challenge with three key solutions to restore security: Quantum Key Distribution, Post Quantum Cryptography and Quantum Random Number Generation. In this class, we will introduce the cyber security challenge and the above-mentioned key means to restore security, including how companies are approaching the situation.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructors
Dr. Troels Steenstrup Jensen, Head of KPMG Global Quantum and Head of Machine Learning at KPMG Denmark
Marco Ugo Gambetta, NewTech Consultant at KPMG Denmark

Dr. Troels Steenstrup JensenTroels is Head of Machine Learning and Quantum Technologies at KPMG Denmark and head of KPMG's Global Quantum Hub. He has a PhD in theoretical quantum mathematics and works at the intersection of mathematics, statistics, physics, computer science and business. He has been working with Machine Learning for more than 10 years and with Quantum Technologies for more than 3 years. Troels has a deep passion for technology and for bringing theory to practice - seeing technology solutions come to life at clients is his main driver. He combines a strong theoretical foundation with business understanding and pragmatic solution design in order to create value for clients.

 

 

Bringing a Quantum Computer to Life with RF Pulses: Fundamental Aspects of Pulse Level Control
Wednesday, 10 March 2021 at 9am ET

View this course on-demand

Download presentation slides (PDF, 2 MB)

Abstract
The qubits in a quantum computer, once they are assembled in place and ready to operate, are inert and in their ground state. To execute a quantum circuit, we must send an intricate and complex sequence of pulses and perform measurements that will determine the state of the qubits. All of this is accomplished using the quantum hardware controller. In this class, we will see how the hardware controller fits into the quantum stack, provide examples on how various quantum gates are translated to pulses, and discuss the evolution of the quantum hardware controller from its roots using lab test equipment into the sophisticated machines that are being built and used today. In particular, we will focus on the OPX, the unique controller offered by quantum machines. Then, we will discuss: (1) the various design and architecture challenges that go into building quantum hardware controllers and (2) why it is essential (a) to build an entirely new classical processing architecture from the ground up in order to maximize the potential of the quantum hardware controller and (b) to create specialized programming languages for pulse level control. We will also demonstrate an example with a particular pulse language named: QUA.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Dr. Lior Ella, Research and Product Team Leader, Quantum Machines

Dr. Lior EllaLior is a physicist with extensive experience in the development of quantum devices and techniques. He holds advanced degrees in both electrical engineering and in physics. He is currently a research and product team leader at Quantum Machines, working on system, product and architecture engineering of the next generation of quantum hardware controllers.

 

 

Fundamental Concepts in Quantum Error Correction
Wednesday, 24 February 2021 at 10am ET

View this course on-demand

Access course on the IEEE Learning Network
Earn 1 PDH / 0.1 CEUs

Download presentation slides (PDF, 4 MB)

Abstract
The long-term vision of quantum computing relies on building systems that implement Quantum Error Correction (QEC), which enable computations to be robust to physical qubit errors. In this class, I will break down QEC into basic concepts that will help you grasp the ongoing research and development in the field. I will discuss why error correction is a necessity for scalable systems, what stabilizers are and how they detect and protect quantum states from error, the basics of error correcting codes such as the Surface Code, and how measurements are used to decode and remove quantum errors. I will not cover the basics of qubits, quantum circuits, or linear algebra, which you will need to get the most out of this course. On a personal level, I find QEC to be engaging for its mix of quantum circuit manipulation, computer science concepts, and fundamental quantum mechanics with deep implications on the future of quantum computing. Hopefully you will find QEC as fun as I do!

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Dr. Julian Kelly, Research Scientist, Google AI Quantum

Dr. Julian KellyDr. Julian Kelly is a Research Scientist at Google AI Quantum. He is the lead for the System Control Team which is responsible for building the hardware and software to operate and manipulate Quantum Computers. He began his career in Quantum Computing in 2008 where he joined John Martinis' physics research group at UCSB as an undergraduate and researched qubit control and benchmarking techniques. Julian stayed at UCSB and completed his PhD in 2015 in experimental quantum computing. His thesis focused on the development of highly controllable, coherent, and scalable "Xmon" transmon systems that demonstrated record fidelity entangling gate and measurement operations, culminating in a demonstration of experimental quantum error correction. Since joining Google, Julian worked to improve, scale, and integrate quantum processors and was the lead designer for the 72 qubit Bristlecone processor. Julian also developed the automated calibration framework "optimus" which is a software backbone of operating quantum processors at Google. The above technologies were critical in the team's 2019 demonstration: "Quantum supremacy using a programmable superconducting processor.”

 

 

Quantum Systems Engineering for Scientists
Wednesday, 10 February 2021 at 4pm ET

View this course on-demand

Abstract
Dr. Martinis would like to invite you to a talk on Quantum systems engineering for scientists. As the field of quantum Computing has advanced building complex machines it seems like a good time to talk about some of the organizational principles that one might use for such a large effort. System engineering concepts have been well developed for other technologies, so here he has focused on quantum computers. This special emphasis comes from the need for engineering discipline for the many physicists and scientists on the project who typically don't have an engineering background, so his talk will cover some basic principles. He will also discuss some of the unusual constraints that are found for quantum computers such as the inability to copy information and the large amount of information that is needed to control qubits. Here's an example of an interesting principle that scientists should know. Although the scientific method is the foundation of all technology, it is well-known that strictly following the scientific method for project management will cause failure so you will want to know why. This is an important subject for the field of Quantum Computing so please come and bring a lot of questions since Dr. Martinis would like to learn from you through active engagement.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Dr. John Martinis, Physics Professor, UCSB

Dr. John MartinisJohn Martinis did pioneering experiments in superconducting qubits in the mid 1980’s for his PhD thesis. He has worked on a variety of low temperature device physics during his career, focusing on quantum computation since the late 1990s. He was awarded the London Prize in Low temperature physics in 2014 for his work in this field. From 2014 to 2020 he worked at Google to build a useful quantum computer, culminating in a quantum supremacy experiment in 2019.

 

 

Systems Engineering Approaches and Challenges in Quantum Computing
Wednesday, 20 January 2021 at 10am ET

View this course on-demand

Access course on the IEEE Learning Network
Earn 1 PDH / 0.1 CEUs

Abstract
NISQ era quantum computers can perform useful applications today. But, realizing the full potential of these systems will require both advances and close collaboration from a broad swath of science and engineering disciplines. Traditional systems engineering models, typically adapted from aerospace and defense industries, are often too prescriptive in defining requirements and use-cases. Furthermore, enterprise systems engineering methods and tools are often focused on how to best prepare an enterprise for change, not how to vector the development of specific systems that will disrupt enterprises. Systems engineering professionals need to consider augmenting best-practices while building lower TRL emerging technologies, such as quantum computing, that require more flexible planning. This course will address possible approaches and challenges, especially for systems with many potential use-cases of strategic interest.

Host
Maëva Ghonda, IEEE Chair, Quantum Computing Education for Workforce Development Program

Instructor
Will Madsen, Quantum Systems Engineering and Architecture Manager, Rigetti Computing

Will MadsenWill leads systems engineering and integration efforts within the technical organization at Rigetti Computing and manages its portfolio of Department of Defense (DOD) programs. Before joining Rigetti, Will was a Developmental Engineer for the United States Air Force where he led engineering teams in flight testing and space launch operations. He holds a BS in Systems Engineering from the US Air Force Academy.

 

 

The Quantum Computing Education - Workforce Development program is brought to you by:

IEEE Quantum IEEE Standards Association