What is my quantum computer good for? Quantum capability learning with physics-aware neural networks

• URL: http://arxiv.org/abs/2406.05636v1
• Date: Sun, 9 Jun 2024 04:11:41 GMT
• Title: What is my quantum computer good for? Quantum capability learning with physics-aware neural networks
• Authors: Daniel Hothem, Ashe Miller, Timothy Proctor,
• Abstract summary: We present a quantum-physics-aware neural network architecture for learning capability models. Our architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to $sim50%$ reductions in mean absolute error on both experimental and simulated data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computers have the potential to revolutionize diverse fields, including quantum chemistry, materials science, and machine learning. However, contemporary quantum computers experience errors that often cause quantum programs run on them to fail. Until quantum computers can reliably execute large quantum programs, stakeholders will need fast and reliable methods for assessing a quantum computer's capability-i.e., the programs it can run and how well it can run them. Previously, off-the-shelf neural network architectures have been used to model quantum computers' capabilities, but with limited success, because these networks fail to learn the complex quantum physics that determines real quantum computers' errors. We address this shortcoming with a new quantum-physics-aware neural network architecture for learning capability models. Our architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to $\sim50\%$ reductions in mean absolute error on both experimental and simulated data, over state-of-the-art models based on convolutional neural networks.

Related papers

• 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)
• Tensor networks for quantum machine learning [0.0]
  We discuss how layouts like MPS, PEPS, TTNs and MERA can be mapped to a quantum computer. We also discuss how they can be used for machine learning and data encoding and which implementation techniques improve their performance.
  arXiv  Detail & Related papers  (2023-03-21T10:46:56Z)
• 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)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• 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 Hybrid Quantum-Classical Neural Network Architecture for Binary Classification [0.0]
  We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit. On simulated hardware, we observe that the hybrid neural network achieves roughly 10% higher classification accuracy and 20% better minimization of cost than an individual variational quantum circuit.
  arXiv  Detail & Related papers  (2022-01-05T21:06:30Z)
• Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs [23.747282946165097]
  This paper makes the first attempt to mix quantum neuron designs to build quantum neural architectures. Existing quantum neuron designs may be quite different but complementary, such as neurons from variation quantum circuits (VQC) and QuantumFlow. We propose to mix them together and figure out a way to connect them seamlessly without additional costly measurement.
  arXiv  Detail & Related papers  (2021-09-08T17:47:54Z)
• QuantumFed: A Federated Learning Framework for Collaborative Quantum Training [10.635097939284751]
  We propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.
  arXiv  Detail & Related papers  (2021-06-16T20:28:11Z)
• On quantum neural networks [91.3755431537592]
  We argue that the concept of a quantum neural network should be defined in terms of its most general function. Our reasoning is based on the use of the Feynman path integral formulation in quantum mechanics.
  arXiv  Detail & Related papers  (2021-04-12T18:30:30Z)
• Quantum Deformed Neural Networks [83.71196337378022]
  We develop a new quantum neural network layer designed to run efficiently on a quantum computer. It can be simulated on a classical computer when restricted in the way it entangles input states.
  arXiv  Detail & Related papers  (2020-10-21T09:46:12Z)
• Quantum algorithms for quantum chemistry and quantum materials science [2.867517731896504]
  We briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics, that are of potential interest for solution on a quantum computer. We take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal state simulation, and analyze their strengths and weaknesses for future developments.
  arXiv  Detail & Related papers  (2020-01-10T22:49:56Z)

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.