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Quantum noise is seen by many researchers as a problem to be resolved. Current solutions increase quantum computing system costs significantly by requiring numerous hardware qubits to represent a logical qubit to average the noise away. However, despite its deleterious effects on system performance and the increased costs it creates, it may have some potential uses. This paper evaluates those. Specifically, it considers how quantum noise could be used to support the fuzzing cybersecurity and testing technique and AI techniques such as certain swarm artificial intelligence algorithms. Fuzzing is used to identify vulnerabilities in software by generating massive amounts of input cases for a program. Quantum noise provides an effective built-in fuzzing capability that is centered around the actual answer to a computation. These same phenomena, of clustered and centered fuzz-noise around the answer of an operation, could be similarly useful to AI techniques that can make effective use of lots of point values for optimization. Effectively, by concurrently considering the ‘multiverse’ of possible results to an operation, created by compounding noise, more beneficial solutions that are proximal to the actual result of an operation can be identified via testing quantum noise points with an effectiveness algorithm. Both of these potential uses for quantum noise are considered herein. 

This paper discusses the benefits that can be enjoyed from the possible applications of quantum computing to the aerospace sector. It discusses these benefits for various tasks and applications such as artificial intelligence, key distribution and simulation. The challenge of determining what aerospace applications will be best allocated to quantum devices is also discussed. The method for best utilizing quantum computing for aerospace applications is also considered and the benefits of a grid-based implementation of computational resources are discussed. A collection of network-connected heterogenous devices could be used to support the effective use of computational resources. It could be used for the operations and development of simulations as well as machine learning and other software applications. This paper also presents a summary of potential use-cases, as well as a discussion of the work necessary to expedite the process of quantum computing development. 

Quantum computing is poised to dramatically change the computational landscape, worldwide. Quantum computers can solve complex problems that are, at least in some cases, beyond the ability of even advanced future classical-style computers. In addition to being able to solve these classical computer-unsolvable problems, quantum computers have demonstrated a capability to solve some problems (such as prime factoring) much more efficiently than classical computing. This will create problems for encryption techniques, which depend on the difficulty of factoring for their security. Security, scientific, and other applications will require access to quantum computing resources to access their unique capabilities, speed and economic (aggregate computing time cost) benefits. Many scientific applications, as well as numerous other ones, use grid computing to provide benefits such as scalability and resource access. As these applications may benefit from quantum capabilities - and some future applications may require quantum capabilities - identifying how to integrate quantum computing systems into grid computing environments is critical. This paper discusses the benefits of grid-connected quantum computers and what is required to achieve this.