Computer-inspired Quantum Experiments

• URL: http://arxiv.org/abs/2002.09970v1
• Date: Sun, 23 Feb 2020 18:59:00 GMT
• Title: Computer-inspired Quantum Experiments
• Authors: Mario Krenn, Manuel Erhard, Anton Zeilinger
• Abstract summary: In many disciplines, computer-inspired design processes, also known as inverse-design, have augmented the capability of scientists. We will meet vastly diverse computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning.
• Score: 1.2891210250935146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The design of new devices and experiments in science and engineering has historically relied on the intuitions of human experts. This credo, however, has changed. In many disciplines, computer-inspired design processes, also known as inverse-design, have augmented the capability of scientists. Here we visit different fields of physics in which computer-inspired designs are applied. We will meet vastly diverse computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we draw our attention specifically on quantum physics. In the quest for designing new quantum experiments, we face two challenges: First, quantum phenomena are unintuitive. Second, the number of possible configurations of quantum experiments explodes combinatorially. To overcome these challenges, physicists began to use algorithms for computer-designed quantum experiments. We focus on the most mature and \textit{practical} approaches that scientists used to find new complex quantum experiments, which experimentalists subsequently have realized in the laboratories. The underlying idea is a highly-efficient topological search, which allows for scientific interpretability. In that way, some of the computer-designs have led to the discovery of new scientific concepts and ideas -- demonstrating how computer algorithm can genuinely contribute to science by providing unexpected inspirations. We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future. Finally, we discuss what we can learn from the different approaches in the fields of physics, and raise several fascinating possibilities for future research.

Related papers

• The QUATRO Application Suite: Quantum Computing for Models of Human Cognition [49.038807589598285]
  We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling. We release QUATRO, a collection of quantum computing applications from cognitive models.
  arXiv  Detail & Related papers  (2023-09-01T17:34:53Z)
• 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: 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)
• Digital Discovery of a Scientific Concept at the Core of Experimental Quantum Optics [1.2891210250935146]
  We present Halo, a new form of multiphoton quantum interference with surprising properties. Our manuscript demonstrates how artificial intelligence can act as a source of inspiration for the scientific discoveries of new actionable concepts in physics.
  arXiv  Detail & Related papers  (2022-10-18T16:45:33Z)
• Digital Discovery of 100 diverse Quantum Experiments with PyTheus [0.4517077427559345]
  PyTheus is an open-source digital discovery framework for quantum optics. It can employ a wide range of experimental devices from modern quantum labs to solve various tasks. This includes the discovery of highly entangled quantum states, quantum measurement schemes, quantum communication protocols, multi-particle quantum gates.
  arXiv  Detail & Related papers  (2022-10-18T16:45:32Z)
• Standard Model Physics and the Digital Quantum Revolution: Thoughts about the Interface [68.8204255655161]
  Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress. From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
  arXiv  Detail & Related papers  (2021-07-10T06:12:06Z)
• Simulating Quantum Materials with Digital Quantum Computers [55.41644538483948]
  Digital quantum computers (DQCs) can efficiently perform quantum simulations that are otherwise intractable on classical computers. The aim of this review is to provide a summary of progress made towards achieving physical quantum advantage.
  arXiv  Detail & Related papers  (2021-01-21T20:10:38Z)
• Scientific intuition inspired by machine learning generated hypotheses [2.294014185517203]
  We shift the focus on the insights and the knowledge obtained by the machine learning models themselves. We apply gradient boosting in decision trees to extract human interpretable insights from big data sets from chemistry and physics. The ability to go beyond numerics opens the door to use machine learning to accelerate the discovery of conceptual understanding.
  arXiv  Detail & Related papers  (2020-10-27T12:12:12Z)
• Quantum Computing Methods for Supervised Learning [0.08594140167290096]
  Small-scale quantum computers and quantum annealers have been built and are already being sold commercially. We provide a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems.
  arXiv  Detail & Related papers  (2020-06-22T06:34:42Z)
• Conceptual understanding through efficient inverse-design of quantum optical experiments [1.1470070927586016]
  We present Theseus, an explainable AI algorithm that can contribute to science at a conceptual level. We introduce an interpretable representation of quantum optical experiments amenable to algorithmic use. We solve several crucial open questions in quantum optics, which is expected to advance photonic technology.
  arXiv  Detail & Related papers  (2020-05-13T17:33:02Z)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)

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.