Quantum computation is a novel computational paradigm that is provably different from classical computers. I will briefly introduce the ideas behind their operation and the main types of quantum computers today, and then as a specific example I will discuss in somewhat more detail the quantum computers
based on superconducting Transmon qubits in use at IBM, Google, Rigetti, and many other commercial and academic quantum computing organizations.
We live in the era of NISQ : noisy intermediate-scale quantum computers. These computers are not like the quantum computers you read about in Nielsen and Chuang. They have many limitations and pose many obstacles to the quantum programmer. I’ll discuss some of these difficulties and how a compiler — specifically tket from Cambridge Quantum Computing — can help overcome these problems.
Quantum computing is now in the so-called NISQ (Noisy Intermediate-Scale Quantum) era, in which quantum processors with a limited number of qubits and imperfect behaviour are capable to execute relatively small quantum algorithms. In order to leverage the computational power of such resource-constrained and error-prone devices, not only efficient compilation techniques are required but also optimal full-stacks need to be developed. In this talk, I will provide an overview on the compilation of quantum algorithms on NISQ devices. I will also discuss how the use of structured design space exploration methodologies could help towards the development of a cross-layer co-design framework for full-stack quantum systems that will allow for a top-bottom, bottom-up optimizations across layers and the benchmarking of quantum computers.
In order to develop proper (quantum) algorithms, dedicated design flows and tools are required. In the conventional realm, corresponding solutions are standard in the meantime. In the quantum realm, however, developments are at the beginning (although great accomplishments have been made in the recent years). In this talk, we review how methods for design automation can help in this regard. More precisely, we show how certain data-structures and procedures that are established in conventional design automation can be used to improve the simulation and verification of quantum algorithms.