Quantum computation is an emerging technology that promises a wide range of possible use cases. This promise is primarily based on algorithms that are unlikely to be viable over the coming decade. For near-term applications, quantum software needs to be carefully tailored to the hardware available. In this paper, we begin to explore whether near-term quantum computers could provide tools that are useful in the creation and implementation of computer games. The procedural generation of geopolitical maps and their associated history is considered as a motivating example. This is performed by encoding a rudimentary decision making process for the nations within a quantum procedure that is well-suited to near-term devices. Given the novelty of quantum computing within the field of procedural generation, we also provide an introduction to the basic concepts involved.
Quantum computation is an emerging technology that promises a wide range of possible use cases. This promise is primarily based on algorithms that are unlikely to be viable over the coming decade. For near-term applications, quantum software needs to be carefully tailored to the hardware available. In this paper, we begin to explore whether near-term quantum computers could provide tools that are useful in the creation and implementation of computer games. The procedural generation of geopolitical maps and their associated history is considered as a motivating example. This is performed by encoding a rudimentary decision making process for the nations within a quantum procedure that is well-suited to near-term devices. Given the novelty of quantum computing within the field of procedural generation, we also provide an introduction to the basic concepts involved.
Quantum computation is an emerging technology that promises to be a powerful tool in many areas. Though some years likely still remain until significant quantum advantage is demonstrated, the development of the technology has led to a range of valuable resources. These include publicly available prototype quantum hardware, advanced simulators for small quantum programs and programming frameworks to test and develop quantum software. In this provocation paper we seek to demonstrate that these resources are sufficient to provide the first useful results in the field of procedural generation. This is done by introducing a proof-of-principle method: a quantum generalization of a blurring process, in which quantum interference is used to provide a unique effect. Through this we hope to show that further developments in the technology are not required before it becomes useful for procedural generation. Rather, fruitful experimentation with this new technology can begin now.
The emergence of noisy medium-scale quantum devices has led to proof-of-concept applications for quantum computing in various domains.Examples include Natural Language Processing (NLP) where sentence classification experiments have been carried out, as well as procedural generation, where tasks such as geopolitical map creation, and image manipulation have been performed.We explore applications at the intersection of these two areas by designing a hybrid quantum-classical algorithm for sentence generation.Our algorithm is based on the well-known simulated annealing technique for combinatorial optimisation.An implementation is provided and used to demonstrate successful sentence generation on both simulated and real quantum hardware.A variant of our algorithm can also used for music generation.This paper aims to be self-contained, introducing all the necessary background on NLP and quantum computing along the way.
Vision paper Abstract-Quantum computers have long been more of a toy for researchers than a tool for solving complex problems. However, recent advances in the field make exploiting the advantages of fault-tolerant quantum computers feasible in the next 5 to 10 years. It is now time to begin imagining how such devices could be used in practice for game development and deployment. In this work we identify procedural content generation as a very promising area of application and exploration. We examine a selection of algorithmic approaches used in classical procedural content generation and propose promising quantum algorithms that could provide an alternative approach or a computational advantage. We then end with a hypothetical game that exploits a recent quantum algorithm for computing the Jones polynomial exponentially faster than classical computers could.
Although the concept of quantum computing has existed for decades, the technology needed to successfully implement a quantum computing system has not yet reached the level of sophistication, reliability, and scalability necessary for commercial viability until very recently. Significant progress on this front was made in the past few years, with IBM planning to create a 1000-qubit chip by the end of 2023, and Google already claiming to have achieved quantum supremacy. Other major industry players such as Intel and Microsoft have also invested significant amounts of resources into quantum computing research. Any viable computing system requires both hardware and software to work together harmoniously in order to perform useful computations. While the achievements of IBM and other companies represent a large step forward for quantum hardware, many gaps remain to be filled with respect to the corresponding software. Specifically, there is currently no clear path towards a complete process for translating quantum algorithms into physical operations that are directly executable on quantum hardware. Such a process is analogous to a compiler that translates programs written in a high-level language into executable machine instructions on a conventional digital computer, and it is necessary if quantum computers are to be harnessed to perform practically useful computations. Existing work has addressed individual components of this process, but so far no unified method for translating the whole of a quantum algorithm into executable operations has been described. I make substantial progress towards filling this gap by describing a set of high-level and low-level quantum circuit design techniques, which when taken together reduce the need of a circuit designer to be concerned with low-level details. On the high-level side, I describe an approach or strategy to designing quantum oracles for Grover's algorithm that allows it to be applied to several types of problems. This approach involves designing oracles in terms of high-level blocks such as counters, multiplexers, comparators, and arbitrary Boolean functions. The implementations of these blocks in terms of lower-level quantum gates are demonstrated in a way that makes it clear that scaled-up versions of them can be generated in a completely automated fashion. For a specific class of problems, which I call state-space path planning problems, I also introduce a paradigm for quantum oracle design that involves representing the problem in terms of individual states and moves. Problems of this sort have applications in robotics and games. Low-level techniques that I introduce
The concept of quantum computing has inspired a whole new generation of scientists, including physicists, engineers, and computer scientists, to fundamentally change the landscape of information technology. With experimental demonstrations stretching back more than two decades, the quantum computing community has achieved a major milestone over the past few years: the ability to build systems that are stretching the limits of what can be classically simulated, and which enable cloud-based research for a wide range of scientists, thus increasing the pool of talent exploring early quantum systems. While such noisy near-term quantum computing systems fall far short of the requirements for fault-tolerant systems, they provide unique test beds for exploring the opportunities for quantum applications. Here, we highlight an IBM-specific perspective of the facets associated with these systems, including quantum software, cloud access, benchmarking quantum systems, error correction and mitigation in such systems, understanding the complexity of quantum circuits, and how early quantum applications can run on near-term quantum computers.
MACHINE learning and Scientific Computing stand to gain significantly from Quantum Computing (QC), which holds promise to deliver new tools for solving complex and computationally demanding problems. Hardware-informed approaches, which attempt to lower the bar in resources required for the utilization of quantum paradigms, could accelerate such development. Simultaneously, as traditional workflows routinely employed in a number of applications evolve, so too do the opportunities for integrating quantum algorithms in the pipeline and unlock classical limitations. By developing the understanding for distinct advantages of quantum devices, new methods can emerge to enhance predictive accuracy, accelerate simulations, and uncover novel insights in scientific and industrial applications. However, limited resources offered by first generations of quantum devices typically necessitate of classical co-processing power, and require tailored software-hardware approach, often referred as ’co-design’. Furthermore, the nature of quantum computing and machine learning in science & technology applications increasingly call for methods that are physics-informed. This review explores the intersection of quantum computing and machine learning, with a particular emphasis on scientific machine learning (SciML). Unlike conventional machine learning, SciML is deeply rooted in formal and domain-specific constraints, making it an ideal candidate for quantum-enhanced approaches. Crucially, we argument how effective quantum algorithms need to both be aligned with the structures and requirements of targeted scientific problems, whilst carefully understanding the interplay between hardware and problem formulation. A key focus of this review is the role of neutral atomic quantum processors in advancing SciML. These platforms offer unique computational capabilities that remain under-explored compared to other quantum platforms. With their ability to naturally encode spatial and graph-based problems, neutral atom systems provide a promising avenue for tackling optimization and machine learning tasks that are particularly relevant in scientific computing. By highlighting in few examples the advantages and open questions surrounding these processors, we aim to present a forward-looking perspective on how quantum computing can reshape computational approaches in science and engineering.