Quantum computers promise substantial speedups over conventional machines for many practical applications. While considered "dreams of the future" for a long time, first quantum computers are available now which can be utilized by anyone. A leading force within this development is IBM Research which launched the IBM Q Experience - the first industrial initiative to build universal quantum computers and make them accessible to a broad audience through cloud access. Along this initiative, the tool Qiskit has been launched which enables researchers, teachers, developers, and general enthusiasts to write corresponding code and to run experiments on those machines. At the same time, this provides an ideal playground for the design automation community which - through Qiskit - can deploy improved solutions e.g. on designing and realizing quantum applications. This special session summary aims to provide an introduction into Qiskit and is showcasing selected success stories on how to work with and develop for it. In addition to that, it provides corresponding references to further readings in terms of tutorials and scientific papers as well as links to publicly available implementations for Qiskit extensions.

Quantum computing is emerging at a meteoric pace from a pure academic field to a fully industrial framework. Rapid advances are happening both in the physical realisations of quantum chips, and in their potential software applications. In contrast, we are not seeing that rapid growth in the design and verification methodologies for scaled-up quantum machines. In this work we describe the field of verification of quantum computers. We discuss the underlying concepts of this field, its theoretical and practical challenges, and state-of-the-art approaches to addressing those challenges. The goal of this paper is to help facilitate early efforts to adapt and create verification methodologies for quantum computers and systems. Without such early efforts, a debilitating gap may form between the state-of-the-art of low level physical technologies for quantum computers, and our ability to build medium, large, and very large scale integrated quantum circuits (M/L/VLSIQ).

Quantum computation is currently moving from an academic idea to a practical reality. The recent past has seen tremendous progress in the physical implementation of corresponding quantum computers - also involving big players such as IBM, Google, Intel, Rigetti, Microsoft, and Alibaba. These devices promise substantial speedups over conventional computers for applications like quantum chemistry, optimization, machine learning, cryptography, quantum simulation, and systems of linear equations. The Computer-Aided Design and Verification (jointly referred as CAD) community needs to be ready for this revolutionizing new technology. While research on automatic design methods for quantum computers is currently underway, there is still far too little coordination between the CAD community and the quantum computation community. Consequently, many CAD approaches proposed in the past have either addressed the wrong problems or failed to reach the end users. In this summary paper, we provide a glimpse into both sides. To this end, we review and discuss selected accomplishments from the CAD domain as well as open challenges within the quantum domain. These examples showcase the recent state-of-the-art but also outline the remaining work left to be done in both communities.

We present our experience of using machine learning techniques over data originating from advanced meter infrastructure (AMI) systems for water consumption in a medium-size city. We focus on two new use cases that are of special importance to city authorities. One use case is the automatic identification of malfunctioning meters, with a focus on distinguishing them from legitimate non-consumption such as during periods when the household residents are on vacation. The other use case is the identification of leaks or theft in the unmetered common areas of apartment buildings. These two use cases are highly important to city authorities both because of the lost revenue they imply and because of the hassle to the residents in cases of delayed identification. Both cases are inherently complex to analyze and require advanced data mining techniques in order to achieve high levels of correct identification. Our results provide for faster and more accurate detection of malfunctioning meters as well as leaks in the common areas. This results in significant tangible value to the authorities in terms of increase in technician efficiency and a decrease in the amount of wasted, non-revenue, water.

When designing a complex system, adhering to a design methodology is essential to ensure design quality and to shorten the design phase. Until recently, enforcing this could be done only partially or manually. This paper demonstrates how constraint programming technology can enable automation of the design methodology support when the design artifacts reside in a central repository. At any phase of the design, the proposed constraint programming application can indicate whether the design process data complies with the methodology and point out any violations that may exist. Moreover, the application can provide recommendations regarding the design process. The application was successfully used to check the methodology conformance of an industrial example and produced the desired outputs within reasonable times.

The initial state of a design under verification has a major impact on the ability of stimuli generators to successfully generate the requested stimuli. For complexity reasons, most stimuli generators use sequential solutions without planning ahead. Therefore, in many cases, they fail to produce a consistent stimuli due to an inadequate selection of the initial state. We propose a new method, based on machine learning techniques, to improve generation success by learning the relationship between the initial state vector and generation success. We applied the proposed method in two different settings, with the objective of improving generation success and coverage in processor and system level generation. In both settings, the proposed method significantly reduced generation failures and enabled faster coverage

Functional verification of systems is aimed at validating the integration of previously verified components. It deals with complex designs, and invariably suffers from scarce resources. We present a set of methods, collectively known as testing knowledge, aimed at increasing the quality of automatically generated system-level test-cases. Testing knowledge reduces the time and effort required to achieve high coverage of the verified design.