Tutorials Program

Tutorials Scope and Goals

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.

Tutorials Chair and Contact

Tutorials Program

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


QCE22 Tutorials — Overview

Sunday, Sep 18

TUT02 — LibKet: A Cross-Platform Library for Running Quantum Algorithms on NISQ Processors (virtual only)

Carmen G. Almudever: Technical University of Valencia, Spain
Matthias Möller: Delft University of Technology, The Netherlands

TUT05 — Introduction to Quantum Programming with Ket (virtual only)

Evandro Chagas Ribeiro da Rosa, Quantuloop, Brazil
Cláudio Lima: Quantuloop, USA

Monday, Sep 19

TUT01 — Introduction to Quantum Computing Part1

Scott Pakin: Los Alamos National Laboratory (LANL), USA
Eleanor G. Rieffel: NASA Ames Research Center, USA

TUT06 — Intel Quantum SDK: A Platform for Efficient Execution of Variational Algorithms

Xin-Chuan Wu, Intel Corporation, USA
Mohannad Ibrahim, NC State University, USA
Shavindra Premarante, Intel Corporation, USA
Albert Schmitz, Intel Corporation, USA

TUT11 — TorchQuantum: A Fast Library for Parameterized Quantum Circuits

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

TUT20 — Quantum Noise Characterization and Mitigation for NISQ Systems

Daniel Volya, Prabhat Mishra: University of Florida, USA

Tuesday, Sep 20

TUT01 — Introduction to Quantum Computing Part2

Scott Pakin: Los Alamos National Laboratory (LANL), USA
Eleanor G. Rieffel: NASA Ames Research Center, USA

Wednesday, Sep 21

TUT07 — Quantum Computing with Neutral Atoms: Introduction and Practice

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

TUT09 — Leveraging Software Prototypes of Advanced Quantum Computing Techniques

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

TUT12 — Hybrid Quantum-Classical Algorithms on Amazon Braket

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

TUT13 — Q-CTRL: Unlock the Potential of Your Quantum Technology

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

TUT15 — Machine Learning for Full-Stack Quantum Computation

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

Thursday, Sep 22

TUT21 — A Gym for Training and Benchmarking RL-Based Quantum Compilation

Sebastian Feld, Delft University of Technology, The Netherlands
Tariq Bontekoe, TNO, The Netherlands
Stan van der Linde: TNO, The Netherlands

TUT22 — SeQUeNCe: A Customizable Discrete Event Simulator of Quantum Networks

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

TUT26 — Qiskit Quantum Error Correction Software Framework

Drew Vandeth, IBM Quantum - Essex, USA
Andrew Cross, IBM Quantum - Yorktown, USA
James Wootton: IBM Quantum - Zurich, Switzerland

Friday, Sep 23

TUT16 — QuantumFlow+VACSEN: A Visualization System for Quantum Neural Networks on Noisy Quantum Devices

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

TUT19 — Running Quantum Error Correction with IBM Quantum Services

Micheal Healy, IBM Quantum, USA
Thomas Alexander, IBM Quantum, Canada
Edward Chen: IBM Quantum, USA

TUT23 — Understanding Quantum Computing Benchmarks for Algorithmic Performance and Error Correction

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

TUT24 — Generating Resource Efficient Programs Using Synthesis

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


TUT01 — Introduction to Quantum Computing Parts 1 & 2

Scott Pakin: Los Alamos National Laboratory (LANL), USA
Eleanor G. Rieffel: NASA Ames Research Center, USA

Date: Mon, Sep 19, 2022 — Part 1
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Date: Tues, Sep 20, 2022 — Part 2
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Quantum computing offers the potential to revolutionize high-performance computing by providing a means to solve certain computational problems asymptotically faster than any classical computer. Relatively recently, quantum computing has advanced from merely a theoretical possibility to engineered reality, including commercial entities offering early prototype quantum processors, both special-purpose quantum annealers and general-purpose gate-model processors. The media have been showcasing each new development and implicitly conveying the message that quantum-computing ubiquity is nigh. Here, we will respond to this hype and provide an overview of the exciting but still early state of the field.
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.
Keywords: Circuit-model, gate model, quantum annealing, quantum algorithms, QAOA 
Contents Level: We expect the content level to be distributed as follows:
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.
Target Audience: The tutorial targets a broad audience: essentially anyone who is curious about quantum computing and wants to learn how it works and what it can and cannot do. Those who know little to nothing about quantum computing will benefit more from the tutorial than those who have existing expertise in quantum mechanics or who already have had non-trivial exposure to quantum computing, but even professionals who have worked in the field will likely gain at least some knowledge.

TUT02 — LibKet: A Cross-Platform Library for Running Quantum Algorithms on NISQ Processors (virtual only)

Carmen G. Almudever: Technical University of Valencia, Spain
Matthias Möller: Delft University of Technology, The Netherlands

Date: Sun, Sep 18
Time: 10:00-13:30 Mountain Time (MDT) — UTC-6
Abstract: Quantum Computing is an emerging compute technology that has the potential to change the way we will be solving computational problems in the future. However, quantum processing units (QPUs) require a fundamental redesign of algorithms to exploit the full potential of quantum mechanical phenomena such as superposition of states and qubit entanglement which enable exponential speed-ups over classical algorithms for certain types of applications. Today’s noisy and intermediate-scale quantum (NISQ) devices are far from showing their true potential, but nonetheless they enable researchers to gain first-hand experience with this new and exciting computing paradigm.
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.
Keywords:  Quantum algorithms, NISQ devices, Quantum-programming library, Quantum approximate optimization algorithms, Quantum linear solver 
Contents Level: The content level is distributed as follows: 20% beginner, 40% intermediate, 40% advanced.
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
Target Audience: The tutorial targets a broad audience that ranges from researchers and students with engineering, math, computer science or physics background without any knowledge of quantum computing and want to get introduced to this emerging field of research and want to know about its (future) potential, to experts from academia or industry interested in understanding what kind of applications can be accelerated with quantum computing and explore them by running quantum algorithms on different quantum hardware/simulation platforms using LibKet. They will all learn how to express quantum algorithms in their quantum circuit form, how to program and execute them and how to interpret the results.

Date: Wed, Sep 21
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: The idea of applying Quantum Mechanics for secure communications is quite old but recently it has become real and even commercial as well. The most widespread application of quantum key distribution (QKD) has been commercialized and is being used in many application domains like banking, defense, etc. Quantum communication (Q-comm) along with quantum computing will revolutionize the future technology base of the society.
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.
Keywords:  Quantum mechanics, Quantum communication, QKD, Quantum Internet, Entanglement
Contents Level: 40% beginner, 40 % intermediate, 20% advanced
Target Audience: The target audience for this course is someone with good background in physics and mathematics at senior undergraduate level. It will also be of benefit to students and professionals particularly in the disciplines of wireless, optical, and satellite communication.

TUT05 — Introduction to Quantum Programming with Ket (virtual only)

Evandro Chagas Ribeiro da Rosa, Quantuloop, Brazil
Cláudio Lima: Quantuloop, USA

Date: Sun, Sep 18
Time: 14:00-17:30 Mountain Time (MDT) — UTC-6
Abstract: Using superposition and entanglement, one can develop an application accelerated by a quantum computer to solve some problem faster than a high-performance classical computer ever could. Although quantum computers capable of outperforming classical computers in solving real-world problems are not a reality yet, we expect them to be ready soon. Until then, we can get ready for this future, developing and testing quantum-accelerated solutions now. In this tutorial, we cover from the description of what is a quantum bit to the implementation and execution of quantum algorithms, presenting the basics of quantum computing and how to express them in a quantum program. The objective is to offer a gentle introduction to quantum computing, presenting a new quantum programming language, paving the way for those getting started in the field. We will be hands-on in the quantum programming language Ket, looking into the superposition to better understand the computation process without deepening into the math of quantum mechanics. We will introduce the concepts of quantum superposition, entanglement, quantum gates, and measurement, following a step-by-step evolution of the quantum state while applying quantum operations. In the end, we will present, implement, and execute two well-known quantum algorithms: Grover’s search algorithm and Shor’s factoring algorithm.
Keywords: Quantum Computing, Quantum Programming, Quantum Simulation, Quantum Algorithm, Grover’s Algorithm, Shor’s Algorithm, Ket
Contents Level: The content level of this tutorial is approximate 80% beginner, 20% intermediate, 0% advanced. No prior knowledge of quantum mechanics or quantum computing is required. The basics of linear algebra will help understand some of the content, but it is not required. We expect the attendees to have a basic understanding of Python and, optionally, Jupyter Notebook.
Target Audience: The target audience of this tutorial is students, researchers, and enthusiasts that are getting started with quantum computing and quantum programming. We will present the tutorial focusing on peculiarities, limitations, and features of the quantum computation model and not on the physical realization of quantum computers, a more appealing perspective to engineers, developers, and computer scientists. The attendees will learn how to program and execute quantum algorithms and applications in Ket, preparing them for future research and development of solutions powered by quantum computers.

TUT06 — Intel Quantum SDK: A Platform for Efficient Execution of Variational Algorithms

Xin-Chuan Wu, Intel Corporation, USA
Mohannad Ibrahim, NC State University, USA
Shavindra Premarante, Intel Corporation, USA
Albert Schmitz, Intel Corporation, USA

Date: Mon, Sep 19
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Variational quantum algorithms are a most promising class of quantum computing workloads that demonstrate quantum advantages. In this tutorial, we will introduce the basic concept of quantum computation and the design of variational algorithms. Next, we will present Intel Quantum SDK to perform efficient execution of variational algorithms, and we will explain in detail the full-stack platform design. In addition, we will show how users can program their own quantum variational algorithms through Intel Quantum SDK and submit their jobs through Intel DevCloud. This tutorial will be highly interactive. Participants will get access to Intel Quantum SDK and have hands-on experience in running the example applications. We will also demonstrate how a hybrid quantum-classical program is written, compiled, and executed on the platform.
To use the Intel® Quantum SDK, you will need to create a few Intel accounts prompted by the following links.
  1. Sign up for Intel® DevCloud
    • Wait for and complete the email confirmation step.
  2. Register at Intel® Communities or use your Intel credentials to log in,
  3. 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.
Keywords:  Intel Quantum SDK, Quantum Computing, LLVM-Based Quantum Compiler, Variational Quantum Algorithms, Quantum Runtime,NISQ,VQE 
Contents Level: No particular background knowledge of quantum computing is required. In the first session the fundamental concepts of quantum computing are introduced to the participants and the following session of the tutorial explains how to use the tool to write your own program. However, a basic understanding of programming languages in C++ is of advantage. The content will be distributed approximately 60% beginner and 40% intermediate. 
Target Audience: This tutorial is targeted for general quantum computing engineers/researchers/students who are interested in quantumclassical applications. We will have hands-on exercises to demonstrate variational algorithms on the platform step-bystep. Since the platform introduced in this tutorial leverages C++ to describe quantum circuits, participants are expected to be familiar with standard C++ programming.

TUT07 — Quantum Computing with Neutral Atoms: Introduction and Practice

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

Date: Wed, Sep 21
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: As new platforms for quantum computing advance, new computational paradigms and algorithmic strategies are introduced. In this tutorial, we will explore the peculiarities and hardware-efficient algorithms of the upcoming neutral atom quantum computing platform. We will explore the fundamental concepts by which these platforms work and focus on the problems most efficiently solved with them: quantum simulation and optimization. Participants will learn strategies to extract quantum Hamiltonian ground states adiabatically and variationally, how to leverage the laws of quantum mechanics to sample the Hilbert space for machine learning applications, and explore the advantages of smooth evolution in quantum simulators versus digital Trotterization. This tutorial is co-developed by experts in theory, computer science, hardware, and education from two of the world’s leading quantum computing companies focused on neutral-atom architectures.
Keywords: Neutral Atoms, Rydberg, Optimization, Quantum machine learning, Quantum simulation 
Contents Level: Beginner to intermediate. 
Target Audience: Topics will be introduced from the scratch, but participants will appreciate the presentation the most if their previous background includes:
  • 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.
An audience of practitioners both from industry and academia is what we have in mind for this tutorial. These practitioners will learn we the basic functionalities of neutral-atom platforms and to criticize some of their strengths and weaknesses.

Date: Mon, Sep 19
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: As quantum computers with a larger number of qubits become available, it is becoming increasingly difficult to create, maintain, debug and optimize quantum software for these larger machines. The process of manually specifying the connection between qubits and gates, or cobbling together pre-written quantum blocks, worked well for 5 or 15 qubits, but won’t be effective for 100, 1000 qubits, or more. Just like classical programming has evolved from assembly language to C, from raw HTML to higher-level tools, a similar progression is required for quantum programming and algorithm design. High-level functional models offer a new approach. Software engineers and quantum algorithm designers specify the desired functionality of the circuit as well as the desired constraints. The software then explores a huge design space to look for valid solutions that match the functionality and meets the constraints and generates an optimized, hardware-aware circuit that can run on any gate-based universal quantum computer. Participants in this tutorial will learn and practice this new approach: the synthesis of optimized and hardware-aware quantum circuits from high-level functional models. We will cover key functional and algorithmic blocks, learn how to combine them into powerful software, learn how to express hardware constraints, and how to optimize circuits to the desired characteristics such as depth, number of qubits, accuracy, and more. We will create both pure quantum as well as hybrid classical/quantum algorithms.
Keywords: Quantum computing, Quantum algorithms, Circuit synthesis, Circuit optimization, Hardware-aware programming, Scalable algorithms, Functional programming, Quantum algorithm design 
Target Audience: The expected background and prerequisites of the tutorial attendees — practitioners and researchers; describe what the target audience; what will they learn; The attendees are expected to be familiar with basic gate-level design tools (such as Qiskit) and with the operating concept of the quantum computing algorithms (e.g. Grover, VQE, QAOA). Both practitioners and researchers will learn: – How to express quantum circuits and algorithms using functional level models – How to synthesize hardware-aware circuits from these models – How to analyze and revise these circuits – How to execute them on a variety of hardware platforms – How to create custom building blocks that can be added to the synthesis engine

TUT09 — Leveraging Software Prototypes of Advanced Quantum Computing Techniques

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

Date: Wed, Sep 21
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: It can take a long time for new quantum computing techniques to make their way from original research to applicable software integrated into a stack. This is why we are building software prototypes – a collaboration between researchers and developers at IBM Quantum to convert cutting-edge research into reusable software packages for applications like chemistry and machine learning. To help users get access to the software faster, we’re making these packages available earlier in the development cycle and gathering feedback to help inform the longer-term design and development. In this tutorial, we will introduce attendees to the first two launched prototypes, entanglement forging for physical and chemical simulation with reduced qubit resources and quantum kernel training for machine learning applications, as well as preview new prototypes. We will show how to install each prototype, how to get the most out of the documentation, and how to set up and solve example problems in areas like chemistry and machine learning. We will also share opportunities to engage with the developers and other users throughout the prototype development cycle. Attendees should leave with a basic understanding of the core research concepts behind each prototype as well as how to get started applying the new techniques to their own work, and how to contribute to the codebase.
Keywords:  Quantum machine learning, Variational quantum eigensolver, Qiskit, Chemistry, Quantum computing, Quantum software 
Contents Level: 20% beginner, 60% intermediate, 20% advanced. 
Target Audience: 
Attendees will need:
  • Basic Python knowledge
  • Basic linear algebra
  • Basic knowledge of quantum circuit concepts (e.g. state preparation, gates, measurement)
Attendees will learn:
  • 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.

Date: Tue, Sep 20
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: As larger quantum systems become available, the need to be able to execute more complex quantum workloads has become more pressing. The need to run algorithms that combine classical and quantum computation in the same execution cycle is driving new requirements for both what is needed to program quantum systems and the context in which results are interpreted. In this tutorial, we will introduce users to Qiskit Runtime primitives, a new programming model, powered by a set of foundational functions, that enable a user to more seamlessly define and tailor quantum-classical workloads across the respective resources. We will show users how to build and optimize algorithms in this programming model in order to optimize their execution for speed and solution quality on today’s quantum systems using IBM’s Qiskit Runtime Service. Additionally, we will also show users how to experiment with trading off speed and accuracy using convenient inputs in this programming model that allows you to seamlessly leverage the latest error mitigation techniques on IBM systems.
Keywords: Qiskit runtime, Quantum Development Kit, quantum algorithms, Applications quantum-classical programming 
Contents Level: 60% beginner and 40% intermediate. 
Target Audience: This tutorial targets developers and researchers who want to leverage quantum computing in their respective fields and are interested in learning how to execute quantum algorithms using Qiskit Runtime. We expect the typical attendee to have had exposure to programming in Python and some basic knowledge about Quantum Computing. Some familiarity with Qiskit would also help. Prior to attending the tutorial session, participants are expected to have both Python and Qiskit installed.

TUT11 — TorchQuantum: A Fast Library for Parameterized Quantum Circuits

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

Date: Mon, Sep 19
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Parameterized Quantum Circuits (PQC) have attracted much research attention because of their potential to achieve quantum advantages on the current Noisy-Intermediate Scale Quantum (NISQ) computers. Quantum Neural Networks (QNN) for Quantum Machine Learning (QML) and Variational Quantum Eigensolver (PQC) for molecular dynamics are popular PQC examples. However, there are three challenges for current PQC research. Firstly, the library infrastructure for exploring PQC research is not fast and convenient enough, hindering the development of new techniques for PQC. Secondly, the quantum noise can severely degrade the PQC reliability. Thirdly, the design space for PQC circuit architecture (ansatz) is huge, and it is difficult to find a good one efficiently. The TorchQuantum library and the QuantumNAS framework, published in the 28th IEEE International Symposium on High-Performance Computer Architecture (HPCA-28) this year, provide solutions to the three challenges. In this tutorial, we will first introduce the usage of the TorchQuantum library, a general framework for PQC search, construction, training and deployment. All the operations for PQC circuit simulation are implemented with the PyTorch native operators to leverage the supports for GPU acceleration and automatic gradient computations to achieve fast PQC parameter training. We will introduce basic examples on how to construct and train QNN to perform image classification tasks and VQE circuit to estimate the ground state energy of molecules. Secondly, we will introduce the QuantumNAS framework to search for the most noise-robust circuit architecture efficiently. The search is performed with real hardware feedback in the loop to find the circuit most resilient to noise. Finally, for highly scalable training of PQC, we will introduce how to train PQC on real quantum devices with the parameter shift rule.
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.
Keywords:  Parameterized Quantum Circuits, Quantum Machine Learning, Neural Networks, Quantum Circuits, Co-Design Optimization, Hands-On Programming 
Contents Level: The content of this tutorial is aimed at 40% beginner and 60% intermediate level attendees, but the materials should be interesting and useful even for advanced level attendees. We expect the typical attendee to be a beginner in quantum computing or machine learning and be familiar with Python.
Target Audience: This tutorial aims to get researchers familiar with the TorchQuantum library for quantum circuit research and the QuantumNAS technique for quantum noise mitigation. It is appropriate for both the computer scientists or hobbyists with interests in applying their machine learning models to quantum computing and the quantum computing hobbyists with the interests in quantum machine learning and molecular dynamics.

TUT12 — Hybrid Quantum-Classical Algorithms on Amazon Braket

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

Date: Wed, Sep 21
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Many of the most promising quantum algorithms are hybrid quantum-classical solutions that combine the use of a quantum processor and a classical processor to arrive at a solution. Quantum circuits are parameterized, initialized with some values, and then run on the quantum processor. These parameters are then adjusted by performing a classical calculation that depends on the result of the quantum computation. Examples of such hybrid algorithms include the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE), algorithms that have important uses in performing quantum chemistry calculations and solving optimization problems on quantum computers. Hybrid algorithms rely on rapid iterative computations of the quantum and classical processors, typically requiring sharing data between the quantum and classical computer hundreds or thousands of times. This tutorial will teach the audience how to use Amazon’s Braket Hybrid Jobs capability to automate this task and reduce the overall execution time. Amazon Braket Hybrid Jobs simplifies the process of setting up, monitoring, and efficiently executing hybrid quantum-classical algorithms. In the hands-on part of the tutorial, participants will set up their Braket Notebooks, run quantum circuits on quantum devices, and learn how to use the hybrid jobs feature. All attendees will leave with code examples that they can use as a foundation for their own projects.
Keywords:  Quantum Computing, Hybrid Quantum Algorithms, Quantum-Classical Algorithms, Amazon Braket, Hands-On Programming 
Contents Level: 30% beginner, 50% intermediate, 20% advanced. 
Target Audience: This tutorial is structured to appeal to diverse audiences that include researchers in quantum computing, attendees from industry, as well as students and general audience. The minimum requirements for attendees are an elementary knowledge of a modern programming language (such as Python), and familiarity with the basic concepts of quantum computing (quantum gates, quantum circuits, qubit measurement). The audience will learn how to run quantum circuits on quantum computers and simulators in the cloud using the Amazon Braket service, understand the uses of hybrid quantum-classical algorithms, and experiment with the Hybrid Jobs feature to efficiently execute hybrid quantum-classical algorithms.

TUT13 — Q-CTRL: Unlock the Potential of Your Quantum Technology

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

Date: Wed, Sep 21
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: 
Quantum technologies promise to change our world, boosting our ability to solve optimization and search problems through quantum computing, or improve our ability to navigate and map the world around us through quantum sensing. Quantum control will enable these technologies. Through this tutorial you will learn the foundations of optimal, robust and learning control. Then learn how to apply these control techniques to real problems through coding examples. Optimal control allows you to determine the best parameter values to operate your experiment. Robust control allows you to develop control pulses that mitigate the effects of noise and imperfections in your device. Learning control automatically determines the best control to use through machine-learning agents that learn directly from the experiment itself. The concepts that form the foundation of these controls will be taught through interactive visualizations and tasks delivered through the Black Opal learning platform. After building the foundations, we will work through coding challenges to implement these control techniques using the python package Boulder Opal. Demonstrations of these control techniques completed by Q-CTRL will be presented, Including: Improving the performance of cold-atom interferometry by 100x using robust control pulses. Boosting the performance of superconducting quantum computers by 9000 x using Fire Opal — a full stack quantum control solution. After completing this tutorial you will be able to: Identify the appropriate quantum control technique to use. Rapidly find quantum control solutions using Boulder Opal. Deploy these controls on real experiments, with automated calibration techniques that will perform.
Keywords: Quantum, control, computing, sensing, robust, optimal, learning 
Contents Level: Approximate 30% beginner, 50% intermediate, 20% advanced.
Target Audience: The target audience is PhD candidates and post doctoral fellows who are doing research into new quantum technologies
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.

Date: Tues, Sep 20
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: While quantum computers have the potential to solve important problems beyond the reach of any classical technology, error rates pose a great challenge towards the realization of a practical machine. In this workshop, we focus on the classical control stack, the enabler for mitigating these error sources in building a practical quantum computer. We demonstrate how it is implemented on QM’s Quantum Orchestration Platform (QOP), where real-time control sequences are intuitively programmed in software, to dramatically improve productivity and unlock the potential of current and future quantum devices. We will discuss the race for quantum advantage in the NISQ era, as well as how to easily program complicated quantum error correction (QEC) protocols from software. We will expand on how we can leverage classical control systems for error mitigation, with sophisticated feedback, and to simplify correction and calibration procedures, with real-time processing. We will discuss novel adaptive error syndrome measurements, repeat-until-success protocols, and we will see how both classical and quantum feedbacks are used with neutral atoms for error correction and mitigation.
Keywords:  Quantum computing, quantum control, hybrid quantum-classical, quantum software 
Contents Level: 20% Beginner, 50% Intermediate, 30% Advanced. 
Target Audience: We expect researchers and experimentalists specifically to appreciate the workshop. No specific
background is required, even if we assume a basic understanding of the vision of the quantum computing field and its
current limitation.

TUT15 — Machine Learning for Full-Stack Quantum Computation

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

Date: Wed, Sep 21
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: There are two different ways to combine the respective strengths of quantum computing and machine learning. On the one hand, the unique capabilities of quantum computation can be used to enhance existing and develop novel machine learning techniques (QC4ML). On the other hand, one can utilize state-of-the-art machine learning approaches to support the development of quantum computing hardware and software (ML4QC). This tutorial focuses on the latter.
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.
Keywords: Quantum Machine Learning, Machine Learning for Quantum, Full-Stack Quantum Computation
Contents Level: The tutorial aims to be as self-contained as possible. For this reason, no particular background knowledge of quantum computation or machine learning is required. Each of the addressed topics will be motivated by small, interactive examples, followed by an explanation of the necessary basic concepts. Based on this knowledge, the respective current scientific work is presented and explained to the participants. However, a basic understanding of computer science is of advantage. The content will be distributed roughly as follows: 30% beginner, 50% intermediate, 20% advanced. 
Target Audience: The self-contained tutorial is intended to serve a broad audience. We basically address everyone who is interested in improving the development of a full-stack quantum computer and who would like to experience a well-prepared introduction to this topic. This includes researchers and students from the fields of electrical and computer engineering, computer science, applied physics and other domains, with or without prior experience in quantum computation and machine learning. We would also like to bring interested practitioners from the industry closer to this emerging field of research. Any kind of improvement of quantum computing (system) software can help increasing the potential usability of a quantum computer in the company concerned.

TUT16 — QuantumFlow+VACSEN: A Visualization System for Quantum Neural Networks on Noisy Quantum Devices

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

Date: Fri, Sep 23
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: As one of the most popular machine learning algorithms, neural networks have been applied in a wide variety of applications, such as autonomous vehicles, simultaneous translation, and diagnostic medical imaging. With the increasing requirement on analyzing the large-scale data (e.g., 10^8 pixels for one 3D-CT medical image), neural networks encounter both memory-wall and compute-bound on classical computers. With the extremely high parallelism in representing and processing information, Quantum Computing is promising to address these limitations. But, how to make full use of the powerful quantum computers to accelerate neural networks is still unclear. QuantumFlow, published at Nature Communications this year, is an end-to-end framework to optimize neural networks onto a given quantum processor. Importantly, following the co-design philosophy, the developed quantum neurons in QuantumFlow demonstrate the quantum advantage. Meanwhile, VACSEN is an online visualization system which provides the “easy to understand” visualization of the noise status on all available quantum computing nodes, recommends the most robust transpilation of circuit on the selected quantum computing node, and allows the real-time execution for a given quantum algorithm with noise awareness. In this tutorial, we will introduce how to conduct the co-design of neural networks and quantum circuits with QuantumFlow and VACSEN. We will have hands-on experience in implementing the neural network on the quantum circuit. Finally, targeting the near-term quantum computers, we will present the current solution in QuantumFlow, discuss the existing challenges, and show our perspective of quantum machine learning in the NISQ-Era. All attendees will leave with code examples that they can use as the backbone implementation to their own projects.
Keywords: s Quantum Machine Learning, Neural Network, Quantum Circuits, Co-Design Optimization, Quantum Accelerator, Quantum Advantage, Hands-On Programming 
Contents Level: The content of this tutorial is aimed at 60% beginner and 40% intermediate level attendees, but materials should be interesting and useful even for advanced level attendees. We expect the typical attendee to be a beginner in quantum computing or machine learning, and be familiar with Python.
Target Audience: This tutorial aims to bridge the gap between neural network design and quantum circuit design, and therefore, it is appropriate for both the computer scientists or hobbyists with interests in applying their machine learning model to quantum computing and the quantum computing hobbyists with the interests in quantum machine learning. The first session of the tutorial will cover background on both machine learning and quantum computing to synchronize all attendees on the same page. Additionally, we will present the co-design approach applied in QuantumFlow to enable the quantum neuron achieving quantum advantage. In the second session, attendees will have the opportunity to implement the quantum neurons on IBM Qiskit using Google Colab. The third session will introduce the developed mapping optimizer in QuantumFlow, the challenge and perspective of quantum neural network in the near-term quantum computers through VACSEN.

Date: Tues, Sep 20
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: Chemistry is one of the more promising applications of near-term quantum computing, because it can improve our understanding of quantum systems, such as molecular structure and chemical reactions. Energy companies can leverage quantum computing to improve operational efficiencies and design new lower carbon products. On this tutorial we teach quantum computing key concepts, its application to the energy industry and experiment quantum algorithms development tools. We learn in detail how quantum algorithms to solve chemistry problems work, the variational quantum eigensolver (VQE) and use QamuyTM, a cloud-based quantum chemistry framework to experiment. First, we recap the basics of quantum computing and review the technology state-of-the-art. Each concept will be followed with hands-on simulations tests. Next, we learn about VQE and conduct experimental tests. After this tutorial, the participants will be able to explore the field of quantum computing by themselves and run quantum circuit simulations using industry recognized tools.
Keywords: Quantum computing, quantum algorithms, NISQ, quantum chemistry, VQE, energy industry 
Contents Level: The tutorial level is a mix of approximately 20% beginner, 60% intermediate, 20% advanced, each will take away different learnings from the quantum chemistry foundational concepts, code quantum circuits using classical simulators, and test in detail the VQE algorithms. Students should have some general chemistry and energy industry knowledge. 
Target Audience: This tutorial is mainly for engineers/scientists/researchers in computational chemistry interested in learning the applicability for the energy industry. Therefore, the audience is expected to have basic knowledge of quantum chemistry and have an experience of software programming to implement the calculation.

Date: Thurs, Sep 22
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: In this tutorial, we will introduce tools and techniques for developing experiments and algorithms which use real quantum devices. The tutorial will start with designing quantum circuits, and integrating them in hybrid workflows with classical software. We’ll show how to use compilers to optimise and target circuits to devices and simulators, including strategies for combining compiler passes for specific applications. Worked examples from CQ’s TKET development kit will be used, as well as quantum software tools that integrate with TKET (e.g. Qiskit). Moving to executing circuits, we will first cover various kinds of simulation (statevector, stabilizer, unitary, symbolic, shot-based) to iterate and test the algorithm. Then we will move to best practices for running on real devices, including relative merits of devices, and designing the experiment to be portable across multiple device types (e.g. superconducting, ion-trap, cold atom). Taking a broad approach to minimising noise when executing on NISQ devices, the tutorial will also cover quantum error mitigation and the relative merits of state of the art error mitigation techniques, with worked examples from CQ’s Qermit error mitigation package. Combining the parts, we will move on to working through a real-world experiment.
Keywords: Quantum software, Error mitigation, Compilation of quantum circuit, Quantum circuit construction 
Contents Level: 40% beginner, 40% intermediate, 20% advanced. 
Target Audience: We will assume very basic familiarity with quantum circuits and algorithms, and some basic experience with programming
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.

TUT19 — Running Quantum Error Correction with IBM Quantum Services

Micheal Healy, IBM Quantum, USA
Thomas Alexander, IBM Quantum, Canada
Edward Chen: IBM Quantum, USA

Date: Fri, Sep 23
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: All approaches to constructing and controlling a quantum computer are susceptible to errors that modify the target quantum state. On existing hardware, this limits the output fidelity of most quantum algorithms and prevents successfully executing most quantum algorithms. Quantum error correction codes, like their classical counterparts, enable the operation of high fidelity ‘logical‘ qubits encoded within collections of noisy and imperfect ‘physical‘ qubits. However, real-time error correction requires dynamic control of program flow during execution. That is to say, future operations on qubits must be conditioned on the results of measurements of specially designated qubits (such as flag or ancilla qubits) in most error correction algorithms. These measurement results can then be used to conditionally correct the state of the logical qubit. These decisions must occur within the lifetime of the computer’s qubits so that they may impact the quantum system before it decoheres. In this tutorial, we will introduce attendees to the basics of quantum error correction alongside OpenQASM3, a programming language that provides the features necessary to achieve quantum advantage using near-term quantum computers. We will then provide a high-level overview of the hardware and software requirements needed to implement dynamic control flow and quantum error correction. Finally, attendees will be taught how to write and run programs containing dynamic control flow and implement quantum error correction using real quantum systems provided through IBM Quantum Services and Qiskit.
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.
Keywords: Quantum error correction, real time control, dynamic circuits, quantum control systems 
Contents Level: 20% beginner, 70% intermediate, 10% advanced. 
Target Audience: This tutorial is appropriate for a variety of attendees:

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.

TUT20 — Quantum Noise Characterization and Mitigation for NISQ Systems

Daniel Volya, Prabhat Mishra: University of Florida, USA

Date: Mon, Sep 19
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: Quantum computers are subject to the cruel reality of noise and errors. Recent quantum advantage results have revealed not only the ability to control many-qubit systems, but also the severity of noise. In order to realize any meaningful computation on a quantum processor, the handling of noise is an integral puzzle piece. Due to the required qubit counts, quantum error correcting codes, although powerful, are impractical to implement on modern and near-term quantum computers. As a result, alternative strategies are necessary. In this tutorial, we will introduce quantum computing and briefly describe the major sources of quantum noise. We will outline methods to model and characterize noise in quantum systems. Next, we will describe state-of-the-art error mitigation strategies, either driven by hybrid classical-quantum loops or by inherent exploitation of quantum dynamics, that are applicable to present as well as near-term quantum computers. Finally, we will present a hands-on case study using open-source quantum toolchains that will walk through noise mitigation and error correction opportunities during compilation of quantum algorithms, quantum control generation, and measurement of final results.
Keywords: Quantum Computing, Quantum Noise, Noise Characterization, Noise Mitigation, Classical Post-processing, Quantum Control, Hands-On Case Study 
Contents Level: 20% beginner, 60% intermediate, 20% advanced. 
Target Audience: This tutorial is primarily targeted towards developers and researchers interested in reliable quantum computing. This tutorial will also attract designers, tool developers, managers, and researchers that seek to leverage quantum computers to solve useful problems. Attendees will get a deeper understanding of the sources of quantum noise and learn strategies to characterize and mitigate quantum noise to improve the quality of quantum computation.

TUT21 — A Gym for Training and Benchmarking RL-Based Quantum Compilation

Sebastian Feld, Delft University of Technology, The Netherlands
Tariq Bontekoe, TNO, The Netherlands
Stan van der Linde: TNO, The Netherlands

Date: Thurs, Sep 22
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: OpenQL is a framework for high-level quantum programming in C++ and Python and it provides tools for compiling and optimizing quantum code. Its focus lies on retargetability and compilation all the way down to assembly code for various control (micro-)architectures. The general process is that the framework gets as input a complete quantum circuit and a platform description, and outputs an equivalent circuit suited for the platform described. Current activities examine the possibilities to use reinforcement learning (RL) to improve compilation passes like mapping, decomposition, and scheduling.
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.
Keywords: Quantum Compilation, Reinforcement Learning, OpenAI Gym 
Contents Level: The tutorial aims to be as self-contained as possible. For this reason, no particular background knowledge of quantum computing or reinforcement learning is required. In the first session all the necessary basic concepts are explained to the participants and the following part of the tutorial builds up on this knowledge. However, a basic understanding of programming languages in general and Python in particular is of advantage. The content will be distributed roughly as follows: 30% beginner, 50% intermediate, 20% advanced. 
Target Audience: The self-contained tutorial is intended to serve a broad audience. We basically address everyone who is interested in programming RL-based system software for a quantum computer and who would like to experience a well-prepared introduction to this topic. This includes researchers and students from the fields of electrical and computer engineering, computer science, applied physics, and other domains, with or without prior experience in quantum computing and machine learning. We would also like to bring interested practitioners from the industry closer to this emerging field of research. An easyto- use framework for enhancing processes inside quantum compilation may help increasing the potential usability of a quantum computer in the company concerned.

TUT22 — SeQUeNCe: A Customizable Discrete Event Simulator of Quantum Networks

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

Date: Thurs, Sep 22
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Recent advances in quantum information science enabled the development of quantum communication network prototypes and created an opportunity to study full-stack quantum network architectures. The same way classical network simulators helped build the classical Internet, quantum network simulations are expected to play an increasingly important role in designing future quantum networks that scale to long distances and large number of hosts, and meet the latency, reliability and security needs of emerging applications. This talk introduces SeQUeNCe, a comprehensive, customizable quantum network simulator. Our modularized framework is suitable for simulation of quantum network prototypes that capture the breadth of current and future hardware technologies and protocols. We have implemented a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a quantum key distribution network, a teleportation network, and a metropolitan quantum network in the Chicago area consisting of nine routers equipped with quantum memories. SeQUeNCe is available as open source on GitHub. In this tutorial, we will present the design and architecture of SeQUeNCe followed by a live demonstrated and hands-on exercises.
Keywords: Simulating Quantum communication, Quantum network simulator, Discrete event simulation
Contents Level: 75% beginner, 25% intermediate. 
Target Audience: Researchers and scientists who work on quantum networking devices, quantum communication protocols, distributed quantum computing. Infrastructure providers interested in building quantum networks.

TUT23 — Understanding Quantum Computing Benchmarks for Algorithmic Performance and Error Correction

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

Date: Fri, Sep 23
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: We aim to give attendees a first-principles understanding of contemporary methods of benchmarking quantum gates, what these benchmarks can and cannot tell us about algorithm performance, and what we can learn about how close we are to fault-tolerance thresholds. We will start by giving a broad overview of quantum noise and error processes, as well as different ways to represent them in gate-based quantum computing. We will then discuss contemporary methods of benchmarking quantum gates, and the trade-off between methods which are scalable but provide less information versus methods which provide more information but are less scalable. We will then discuss to what extent benchmarked error rates can inform us about quantum algorithm performance, and how they can help us enhancing this performance through error mitigation. Finally, we will cover quantum benchmarks as they relate to fault-tolerance thresholds. All material will be taught with visual aids to facilitate the learning process in the short time span of tutorial session. h
Keywords: Quantum Computing, Benchmarking, Quantum Algorithms, Error Mitigation, Quantum Error Correction, Noise on Quantum Computers 
Contents Level: The content level will be 30% beginner, 50% intermediate, and 20% advanced. We expect attendees to have basic abstract knowledge of quantum bits and operations. The content will appeal to scientists and engineers with a broad variety of experience levels, including undergraduate and graduate students familiar with quantum mechanics, as well as more senior scientists across quantum-related fields. 
Target Audience: This tutorial is aimed primarily at potential users and evaluators of quantum technologies, so that they can interpret benchmarking metrics available on existing and future quantum information processors. Additionally, developers of those technologies who want to understand and improve the performance of their processors for a variety of applications can benefit from this review of benchmarking techniques.

TUT24 — Generating Resource Efficient Programs Using Synthesis

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

Date: Fri, Sep 23
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Current and near-future quantum hardware is likely resource constrained: programs have to use few qubits and carefully limit their gate count. In this tutorial, we will show how to use synthesis, as implemented in the BQSKit framework, to assist in the algorithm development workflow:
  1. Direct synthesis for “small’ (<10 qubit) circuits
  2. Synthesis of “large” (~100s qubits) circuits using partitioning and topology selection
  3. Generation of circuit approximations for error mitigation (for 100s qubits)
  4. Hardware design exploration – generation for heterogeneous gate sets and portability
  5. Algorithm discovery – ansatz instantiation and exploration
Synthesis is a powerful circuit compilation technique whose dynamics, trade-offs and workflow are different fro other existing quantum optimizing compilers such as Qiskit, Tket etc. Currently, BQSKit can produce circuits of better quality than most existing tools. However, since our tools are Open Source, we can take a true deep dive into their nuts and bolts, showing how they can be reconfigured, customized, and extended to arrive at better problem specifications and improved workflow construction. Understanding their design principles, necessary trade-offs, and effect on overall performance through-out the workflow, will equip attendees with the knowledge and ability to better implement algorithms on NISQ devices using our (or similar, non-Open Source) tools.
Keywords: Synthesis, circuit optimization, circuit approximation, circuit instantiation 
Contents Level: 70% intermediate, 30% beginner. We introduce BQSKit and show it can be run in a “black-box” mode as a circuit optimizer, which is useful for all levels, including for beginners. We delve progressively deeper, explaining the ideas and rationale behind the different synthesis approaches, and how to use that background information to reconfigure, customize, and extend the development workflow for any particular program. We expect any audience member that has tried to improve the fidelity of any circuit executing on NISQ hardware to be interested in our techniques, hence the emphasis on the intermediate expertise level. As minimal requirements, attendees are expected to be familiar with the circuit model of quantum computing, basic linear algebra terms, the Python programming language, and how to run Jupyter [6] Notebooks.
Target Audience: This tutorial tutorial is intended for general users of quantum computers who have an interest (or need) for aggressive
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.

Date: Thurs, Sep 22
Time: 10:00-14:30 Mountain Time (MDT) — UTC-6
Abstract: Covalent is a new open-source Python tool for orchestrating and managing quantum and HPC workflows. In the first session of this tutorial, we discuss some of the challenges around building and maintaining scientific software, and how these are addressed with Covalent. Using extensive code samples and a hands-on tutorial, we demonstrate how to use this tool to deploy scalable tasks onto a variety of compute backends, including traditional HPC clusters and cloud environments. In the second session, we introduce the recently proposed quantum machine learning (QML) algorithm for generalized similarity learning using gate-based quantum computers. We practically walk through how one can learn similarity features between fundamentally different spaces by leveraging quantum embeddings. With numerical experimentation that involves a variety of steps and multiple runs to probe various features, we demonstrate how Covalent is used to manage and run distributed workflows in an applied setting.
Keywords: Workflow orchestration, quantum machine learning, open-source software, distributed computing, cloud computing. 
Contents Level: The content level is 50% for beginners, 40% at intermediate difficulty, and 10% for advanced users. 
Target Audience: The target audience are graduate/PhD students, postdocs, and early-career professionals in both quantum and classical
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

TUT26 — Qiskit Quantum Error Correction Software Framework

Drew Vandeth, IBM Quantum - Essex, USA
Andrew Cross, IBM Quantum - Yorktown, USA
James Wootton: IBM Quantum - Zurich, Switzerland

Date: Thurs, Sep 22
Time: 13:00-16:45 Mountain Time (MDT) — UTC-6
Abstract: Quantum Error Correction (QEC) will be a vital component of any successful quantum computer especially as the number of qubits within the quantum computer increases. The development of hardware and software that can correct these errors is therefore a grand challenge within the Quantum computing community.
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.
Keywords:  Quantum Error Correction, Qiskit, Quantum Computation 
Contents Level: 20% beginners, 60% intermediate, 20% advanced. 
Target Audience: This tutorial is aimed at individuals who would like to conduct experiments or calculate results related to Quantum Error Correction. It is assumed that participants have a basic knowledge of Python and an intermediate level of knowledge of Quantum Error Correction or are experienced with errors produced in quantum computers and some of the approaches used to mitigate or correct them