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The shortage of skilled labor is one of the quantum computing sector’s greatest challenges. The week-long tutorials program, with tutorials by leading experts, is aimed squarely at workforce development and training considerations. **The tutorials are ideally suited to develop quantum champions for industry, academia, government, and build expertise for emerging quantum ecosystems.** IEEE Quantum Week will cover a broad range of topics in quantum computing and engineering including a lineup of fantastic hands-on tutorials on programming and applications.

- Ed Leonard, Northrop Grumman — [email protected]
- Huiyang Zhou, NC State University — [email protected]

Each tutorial at IEEE Quantum Week 2022 is 3.0 hours long (i.e., two sessions on the same day of 90 mins).

Carmen G. Almudever: Technical University of Valencia, Spain

Matthias Möller: Delft University of Technology, The Netherlands

Evandro Chagas Ribeiro da Rosa, Quantuloop, Brazil

Cláudio Lima: Quantuloop, USA

Scott Pakin: Los Alamos National Laboratory (LANL), USA

Eleanor G. Rieffel: NASA Ames Research Center, USA

Xin-Chuan Wu, Intel Corporation, USA

Mohannad Ibrahim, NC State University, USA

Shavindra Premarante, Intel Corporation, USA

Albert Schmitz, Intel Corporation, USA

Daniel Justice: Carnegie Mellon University, USA

Hanrui Wang, MIT, USA

Jiaqi Gu, University of Texas at Austin, USA

Zirui Li, Shanghai Jiao Tong University, China

Zhiding Liang, University of Notre Dame, USA

Weiwen Jiang, George Mason University, USA

Yiyu Shi, George Mason University, USA

David Z. Pan, University of Texas at Austin, USA

Yongshan Ding, Yale University, USA

Frederic T. Chong, University of Chicago, USA

Song Han: MIT, USA

Daniel Volya, Prabhat Mishra: University of Florida, USA

Scott Pakin: Los Alamos National Laboratory (LANL), USA

Eleanor G. Rieffel: NASA Ames Research Center, USA

Mariana Bernagozzi, Tushar Mittal, Renier Morales: IBM Quantum, USA

Yonatan Cohen, Yoav Romach, Lior Ella: Quantum Machines, Israel

Jiabao Chen, Karim Essafi, Elena Yndurain: QunaSys, Japan

Vinod Mishra: US Army Research Lab, USA

Jonathan Wurtz, QuEra Computing Inc, USA

Alexei Bylinskii, QuEra Computing Inc, USA

Corbin McElhanney, QuEra Computing Inc, USA

Pedro Lopes, QuEra Computing Inc, USA

Loïc Henriet, Pasqal SAS, France

Louis-Paul Henry: Pasqal SAS, France

Pedro Rivero, Caleb Johnson, Agata Branczyk, Jim Garrison: IBM Quantum, USA

Martin Suchara, Michael Brett, Sebastian Hassinger, Juan Moreno, Jordan Sullivan, Tyler Takeshita: Amazon Web Services, USA

Michael Hush, Yuval Baum, Tom Stace, Russell Anderson, Andre Carvalho, Leo Andreta de Castro, Anurag Mishra, Michael Biercuk: Q-CTRL, Australia & USA

Sebastian Feld, Medina Bandic, Aritra Sarkar, Hans van Someren: Delft University of Technology, The Netherlands

Daniel Mills, Cristina Cirstoiu: Cambridge Quantum, UK

Sebastian Feld, Delft University of Technology, The Netherlands

Tariq Bontekoe, TNO, The Netherlands

Stan van der Linde: TNO, The Netherlands

Rajkumar Kettimuthu, Alexander Kolar, Joaquin Chung: Argonne National Lab (ANL) & University of Chicago, USA

William Cunningham, Santosh Radha: Agnostiq, Canada

Drew Vandeth, IBM Quantum - Essex, USA

Andrew Cross, IBM Quantum - Yorktown, USA

James Wootton: IBM Quantum - Zurich, Switzerland

Shaolun Ruan, Singapore Management University, Singapore

Zhepeng Wang, George Mason University, USA

Yong Wang, Singapore Management University, Singapore

Weiwen Jiang, George Mason University, USA

Qiang Guan: Kent State University, USA

Micheal Healy, IBM Quantum, USA

Thomas Alexander, IBM Quantum, Canada

Edward Chen: IBM Quantum, USA

Ravi K. Naik, Lawrence Berkeley National Laboratory (LBNL), USA

Akel Hashim, Lawrence Berkeley National Laboratory (LBNL), USA

Timothy Proctor, Sandia National Laboratory, USA

Samuele Ferracin: Keysight Technologies Inc, USA

Ed Younis, Wim Lavrijsen, Costin Iancu: Lawrence Berkeley National Lab (LBNL), USA

Scott Pakin: Los Alamos National Laboratory (LANL), USA

Eleanor G. Rieffel: NASA Ames Research Center, USA

In this tutorial, we introduce participants to the computational models that give quantum computing its immense computational power. We examine the thought processes that programmers need to map problems both to quantum annealers and gate-model quantum processors. And we discuss hardware and algorithmic challenges that must be overcome before quantum computing becomes a component of every software developer’s repertoire.

80% beginner

20% intermediate

0% advanced.

No prior knowledge of quantum computing or quantum mechanics is expected, but the final section of the tutorial goes

into some technical depth that requires that attendees have understood the preceding sections.

Carmen G. Almudever: Technical University of Valencia, Spain

Matthias Möller: Delft University of Technology, The Netherlands

Time: 10:00-13:30 Mountain Time (MDT) — UTC-6

In this tutorial, the attendees will learn how to implement quantum algorithms using LibKet (pronounced lib-ket), an open-source software library that facilitates the development of hardware-agnostic quantum kernels and the exploration of their behavior and performance on different QPUs and quantum simulators by means of a unified programming interface in C++.

The tutorial will start with an introduction to essential concepts of quantum computing like quantum bits and registers, superposition and entanglement, up to quantum gates, circuits and algorithms and a demonstration of how to apply them in practice at the hand of practical code examples in LibKet.

Afterwards, we will consider the more advanced quantum approximate optimization algorithms (QAOA), which belong to the class of variational quantum algorithms (VQA) that combine quantum computing with a classical optimization framework. We will discuss a unified implementation of different QAOA instances for the max-cut and dominating-set problem in LibKet which are part of our recently proposed QPack benchmark. We will discuss their performance on different simulators and real QPUs as an exemplified workflow of quantum-algorithm development.

No prior knowledge of quantum computing is required, as the first part if this tutorial will introduce the attendees to the basic concepts and we gradually build up knowledge

Vinod Mishra: US Army Research Lab, USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

In this tutorial, we will focus on Q-comm and explore the principles of physics behind its operation and also study its fundamental aspects like entanglement and quantum error correction. Examples of its physical realizations will be presented. Finally, we will also describe the current status of the attempts to go beyond point-to-point Q-comm and create a Quantum Internet.

Evandro Chagas Ribeiro da Rosa, Quantuloop, Brazil

Cláudio Lima: Quantuloop, USA

Time: 14:00-17:30 Mountain Time (MDT) — UTC-6

Xin-Chuan Wu, Intel Corporation, USA

Mohannad Ibrahim, NC State University, USA

Shavindra Premarante, Intel Corporation, USA

Albert Schmitz, Intel Corporation, USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

To use the Intel® Quantum SDK, you will need to create a few Intel accounts prompted by the following links.

##### Sign up for Intel® DevCloud

##### Wait for and complete the email confirmation step.

**Register at Intel® Communities**or use your Intel credentials to log in,##### Be sure to set a Display Name by visiting

**Intel Communities**.

##### Complete this

**SDK registration form**to gain access to the SDK.##### When your access is confirmed, you’ll receive an email notice and a message through Communities.

Jonathan Wurtz, QuEra Computing Inc, USA

Alexei Bylinskii, QuEra Computing Inc, USA

Corbin McElhanney, QuEra Computing Inc, USA

Pedro Lopes, QuEra Computing Inc, USA

Loïc Henriet, Pasqal SAS, France

Louis-Paul Henry: Pasqal SAS, France

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

- Elementary notions of quantum physics, e.g., what is a Hamiltonian.
- Notions of Hilbert spaces and the linear algebra involved in their manipulation, including state evolution and eigenspectra.
- Basic concepts in combinatorial optimization and graph theory.

Daniel Justice: Carnegie Mellon University, USA

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

Pedro Rivero, Caleb Johnson, Agata Branczyk, Jim Garrison: IBM Quantum, USA

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

Attendees will need:

- Basic Python knowledge
- Basic linear algebra
- Basic knowledge of quantum circuit concepts (e.g. state preparation, gates, measurement)

- Basic Qiskit API (i.e. quantum circuits and visualization tools).
- To download and install quantum prototypes.
- To use and develop new software tools using Qiskit and quantum prototypes.
- To contribute to the quantum prototypes stack.

Mariana Bernagozzi, Tushar Mittal, Renier Morales: IBM Quantum, USA

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

Hanrui Wang, MIT, USA

Jiaqi Gu, University of Texas at Austin, USA

Zirui Li, Shanghai Jiao Tong University, China

Zhiding Liang, University of Notre Dame, USA

Weiwen Jiang, George Mason University, USA

Yiyu Shi, George Mason University, USA

David Z. Pan, University of Texas at Austin, USA

Yongshan Ding, Yale University, USA

Frederic T. Chong, University of Chicago, USA

Song Han: MIT, USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

For each section, we will provide hands-on experience in implementing PQC and running on real quantum machines. We will also discuss the existing difficulties, and show our perspective of PQC, especially QML in the NISQ era. All attendees will leave with code examples that they can leverage as the backbone implementation of their own research.

Martin Suchara, Michael Brett, Sebastian Hassinger, Juan Moreno, Jordan Sullivan, Tyler Takeshita: Amazon Web Services, USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

Michael Hush, Yuval Baum, Tom Stace, Russell Anderson, Andre Carvalho, Leo Andreta de Castro, Anurag Mishra, Michael Biercuk: Q-CTRL, Australia & USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

or professional engineers looking to improve the performance of their quantum devices. No prior knowledge of quantum control is required. An understanding of the basics of quantum mechanics is needed for the introductory materials. The latter quantum control exercises are completed in the programming language python, and require a basic understanding of python. The attendees will learn: the basic concepts of quantum control through interactive exercises in Black Opal, how to solve quantum control problems in python using Boulder Opal and how to deploy quantum control solutions on real quantum hardware. The software packages Black Opal and Boulder Opal, both from Q-CTRL, will be made available to the attendees of the tutorials. Both platforms have free options available that will ensure the attendees can all continue to complete content from the tutorial after the conference.

Yonatan Cohen, Yoav Romach, Lior Ella: Quantum Machines, Israel

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

background is required, even if we assume a basic understanding of the vision of the quantum computing field and its

current limitation.

Sebastian Feld, Medina Bandic, Aritra Sarkar, Hans van Someren: Delft University of Technology, The Netherlands

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

A quantum computer should not be seen as a black box, but rather as a realization of a special computer architecture including several layers. At the lowest level, n-qubit registers are developed using different integration technologies. Those are driven by means of analog control and read-out electronics. Based on these two hardware layers, several software layers follow. With strong reference to the quantum technology incorporated, quantum execution and error correction are carried out, followed by a generalization using quantum instruction set architecture. The compiler and runtime layer pursue multiple tasks such as decomposing, optimizing, mapping, and scheduling given circuits. Finally, there is the top layer of quantum algorithms, in which relevant applications for solving complex problems are addressed.

In this tutorial, we will give an overview of current challenges and goals of machine learning-based activities related to the different layers of a full-stack quantum computer. Examples of topics covered are: quantum circuit characterization and clustering; quantum circuit transformation and optimization; quantum error correction; quantum circuit mapping and scheduling; hybrid and distributed quantum computation.

The tutorial will have both a strong theoretical and practical part. These will be interwoven as follows: first, the corresponding investigated layer of the quantum computer stack will be explained, whereas challenges are interactively motivated by means of smaller examples (i.e., a kind of mini-lecture with exercises). Afterwards, current scientific work in this particular domain is presented and explained (i.e., in a review article manner). However, the aim here is not to elaborate on the smallest details, but rather to convey state-of-the-art open questions and solution approaches. The tutorial will end with an embedding of the individual insights in an overall context that helps to describe future work in the field of machine learning for quantum computation.

Shaolun Ruan, Singapore Management University, Singapore

Zhepeng Wang, George Mason University, USA

Yong Wang, Singapore Management University, Singapore

Weiwen Jiang, George Mason University, USA

Qiang Guan: Kent State University, USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

Jiabao Chen, Karim Essafi, Elena Yndurain: QunaSys, Japan

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

Daniel Mills, Cristina Cirstoiu: Cambridge Quantum, UK

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

in python. We recommend that attendees install pytket and Qermit before the tutorial, and that you are able to make

use of jupyter notebooks. Instructions on installing pytket can be found here https://cqcl.github.io/pytket/build/html/install.

html, and for Qermit here https://cqcl.github.io/qermit/manual/

manual intro.html.

Micheal Healy, IBM Quantum, USA

Thomas Alexander, IBM Quantum, Canada

Edward Chen: IBM Quantum, USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

Tutorial attendees should register with IBM Quantum platform, install Qiskit (locally or using the IBM Quantum Lab) and complete the Getting Started with Qiskit tutorial to prepare for the session. The tutorial content will consist of material of 20% beginner, 70% intermediate, and 10% advanced experience levels.

1) Quantum programming language designers and enthusiasts who wish to learn about the latest developments in OpenQASM

and quantum error correction.

2) Quantum application and algorithm designers who are interested in the latest developments in real-time compute and

quantum error correction using IBM Quantum hardware.

3) Software engineers with an interest in low-level programming models and compilers for quantum computers.

4) Experimentalists with an interest in quantum control systems and quantum error correction

Attendees should have a basic understanding of quantum computing, the circuit model, and approaches to programming

these devices. Prior experience with OpenQASM, Qiskit, IBM Quantum Services, and Python will be helpful. Attendees will

learn about the latest developments in OpenQASM and IBM Quantum’s path towards enabling real-time compute (classical

compute and control-flow within the lifetime of the qubits) and error correction within its hardware.

Daniel Volya, Prabhat Mishra: University of Florida, USA

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

Sebastian Feld, Delft University of Technology, The Netherlands

Tariq Bontekoe, TNO, The Netherlands

Stan van der Linde: TNO, The Netherlands

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

The general idea behind RL is simple: an agent is able to perform an action inside an environment, whereupon it receives a reward. The agent aims to maximize the cumulative reward and by using this approach, it is able to learn policies for actions that are otherwise hard to specify.

The goal of this tutorial is to present a gym in which researchers can train and benchmark their RL agents and algorithms for OpenQL. Here, OpenQL stand as a representative for potentially any quantum compilation framework. The motivation behind this is to allow research on RL-based solutions to problems like circuit decomposition (learn rules that satisfy diverse fitness functions based on, e.g., number of gates, circuit depth, and fidelity), qubit mapping and routing (learn strategies that try to anticipate future properties of quantum circuits), and scheduling (learn policies that considerate possible error channels like, e.g., crosstalk or memory effects). The main advantage for a researcher is that this gym abstracts away parts that are not necessarily relevant to them. The main advantage for a compilation framework is that trained models work against one API, thus enabling an easy incorporation of smart algorithms and agents into the compiler.

The tutorial consists of a theory part and a practical hands-on part. The theory part covers background on topics such as: full-stack quantum computing architecture; quantum compilation and passes; reinforcement learning and gyms. The practical hands-on part demonstrates the following: general structure and interfaces of a gym; setting up a quantum compiler gym; implementing simple environments together with default rewarders; customizing environments and rewarders; training simple reinforcement learning agents.

Rajkumar Kettimuthu, Alexander Kolar, Joaquin Chung: Argonne National Lab (ANL) & University of Chicago, USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

Ravi K. Naik, Lawrence Berkeley National Laboratory (LBNL), USA

Akel Hashim, Lawrence Berkeley National Laboratory (LBNL), USA

Timothy Proctor, Sandia National Laboratory, USA

Samuele Ferracin: Keysight Technologies Inc, USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

Ed Younis, Wim Lavrijsen, Costin Iancu: Lawrence Berkeley National Lab (LBNL), USA

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

- Direct synthesis for “small’ (<10 qubit) circuits
- Synthesis of “large” (~100s qubits) circuits using partitioning and topology selection
- Generation of circuit approximations for error mitigation (for 100s qubits)
- Hardware design exploration – generation for heterogeneous gate sets and portability
- Algorithm discovery – ansatz instantiation and exploration

circuit optimization, or for tools for automated circuit generation from high level algorithmic descriptions. The circuit optimization functionality is of general interest. The error mitigation approach using circuit approximations is valuable for NISQ devices. The hardware design exploration enabled by BQSKit is of interest to architects, as well as the device characterization community together with the budding performance evaluation community. Some of the techniques covered are of interest to software engineers who want to extend synthesis tools or integrate synthesis into a more “traditional” quantum compiler.

William Cunningham, Santosh Radha: Agnostiq, Canada

Time: 10:00-14:30 Mountain Time (MDT) — UTC-6

machine learning. Participants are expected to have basic working knowledge of Python (functions, decorators, etc), scientific computing (distributed/parallel programming), and preferably having worked in HPC environments. Participants are also expected to know

the basics of quantum computing and to be familiar with at least one quantum SDK, such as Qiskit or PennyLane. In

this tutorial, participants will learn methods and practices for distributed quantum and high performance computing both

in a general sense as well as applied to specific machine learning problems. Participants should leave knowing how

to use Covalent to orchestrate distributed quantum and HPC workflow

Drew Vandeth, IBM Quantum - Essex, USA

Andrew Cross, IBM Quantum - Yorktown, USA

James Wootton: IBM Quantum - Zurich, Switzerland

Time: 13:00-16:45 Mountain Time (MDT) — UTC-6

For some time robust and specific libraries for conducting QEC research and experimentation did not exist. This left researchers having to continually redevelop software individually and did not enable quick testing and exploration of ideas that require computations. The Qiskit QEC framework is being developed to alleviate this burden and to allow researchers to both quickly test and verify new ideas and to allow those applications to scale beyond simple small examples.

In this tutorial the Qiskit QEC software framework will be presented in terms of programming for general QEC purposes but also specifically related to the coding of decoders and the simulation of QEC codes.