Procedural generation using quantum computation

• URL: http://arxiv.org/abs/2007.11510v1
• Date: Wed, 22 Jul 2020 16:05:55 GMT
• Title: Procedural generation using quantum computation
• Authors: James R. Wootton
• Abstract summary: Quantum computation is an emerging technology that promises to be a powerful tool in many areas. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• QCRMut: Quantum Circuit Random Mutant generator tool [0.0]
  Quantum computing has been on the rise in recent years, evidenced by a surge in publications on quantum software engineering and testing. As this technology edges closer to practical application, ensuring the efficacy of our software becomes imperative. We introduce QCRMut, a mutation tool tailored for quantum programs, leveraging the inherent Quantum Circuit structure.
  arXiv  Detail & Related papers  (2024-10-02T10:54:00Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Quantum Machine Learning Implementations: Proposals and Experiments [0.0]
  The article reviews specific high-impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors. The field of quantum machine learning could be among the first quantum technologies producing results that are beneficial for industry and, in turn, to society.
  arXiv  Detail & Related papers  (2023-03-11T01:02:16Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Assessing requirements to scale to practical quantum advantage [56.22441723982983]
  We develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required for large-scale quantum applications. We assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack.
  arXiv  Detail & Related papers  (2022-11-14T18:50:27Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• A practical guide for building superconducting quantum devices [2.7080431315882967]
  We present some of the most crucial building blocks developed by the cQED community in recent years. We aim to provide a synoptic outline of the core techniques that underlie most cQED experiments and offer a practical guide for a novice experimentalist to design, construct, and characterize their first quantum device.
  arXiv  Detail & Related papers  (2021-06-11T05:28:01Z)
• Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
  Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems. We propose an algorithm to implement imaginary time propagation on a quantum computer.
  arXiv  Detail & Related papers  (2021-02-24T12:48:00Z)
• How to enhance quantum generative adversarial learning of noisy information [5.8010446129208155]
  We show how different training problems may occur during the optimization process. We propose new strategies to achieve a faster convergence in any operating regime. Our results pave the way for new experimental demonstrations of such hybrid classical-quantum protocols.
  arXiv  Detail & Related papers  (2020-12-10T21:48:26Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)
• A quantum procedure for map generation [0.0]
  We explore whether near-term quantum computers could provide tools that are useful in the creation and implementation of computer games. 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.
  arXiv  Detail & Related papers  (2020-05-20T19:29:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.