Quantum amplitude estimation with error mitigation for time-evolving probabilistic networks

• URL: http://arxiv.org/abs/2303.16588v1
• Date: Wed, 29 Mar 2023 10:45:11 GMT
• Title: Quantum amplitude estimation with error mitigation for time-evolving probabilistic networks
• Authors: M.C. Braun, T. Decker, N. Hegemann, S.F. Kerstan, C. Maier, J. Ulmanis
• Abstract summary: We consider networks of nodes, where each node can be in one of two states: good or failed. Our method can evaluate arbitrary network topologies for any number of time steps. We present the results of a low-depth quantum amplitude estimation on a simulator with a realistic noise model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method to model a discretized time evolution of probabilistic networks on gate-based quantum computers. We consider networks of nodes, where each node can be in one of two states: good or failed. In each time step, probabilities are assigned for each node to fail (switch from good to failed) or to recover (switch from failed to good). Furthermore, probabilities are assigned for failing nodes to trigger the failure of other, good nodes. Our method can evaluate arbitrary network topologies for any number of time steps. We can therefore model events such as cascaded failure and avalanche effects which are inherent to financial networks, payment and supply chain networks, power grids, telecommunication networks and others. Using quantum amplitude estimation techniques, we are able to estimate the probability of any configuration for any set of nodes over time. This allows us, for example, to determine the probability of the first node to be in the good state after the last time step, without the necessity to track intermediate states. We present the results of a low-depth quantum amplitude estimation on a simulator with a realistic noise model. We also present the results for running this example on the AQT quantum computer system PINE. Finally, we introduce an error model that allows us to improve the results from the simulator and from the experiments on the PINE system.

Related papers

• Success probabilities in time-reversal based hybrid quantum state transfer [0.0]
  We consider two memory nodes of a quantum network connected by flying qubits. We show how and why the probability of interfacing successfully is determined by the overlap of the spectral shape of the actual flying qubit and the ideal shape.
  arXiv  Detail & Related papers  (2024-01-16T04:38:10Z)
• Scalable tensor-network error mitigation for near-term quantum computing [0.0]
  We introduce the tensor-network error mitigation (TEM) algorithm, which acts in post-processing to correct the noise-induced errors in estimations of physical observables. TEM does not require additional quantum operations other than the implementation of informationally complete POVMs. We test TEM extensively in numerical simulations in different regimes.
  arXiv  Detail & Related papers  (2023-07-21T17:54:05Z)
• 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)
• Implementing arbitrary quantum operations via quantum walks on a cycle graph [8.820803742534677]
  We use a simple discrete-time quantum walk (DTQW) on a cycle graph to model an arbitrary unitary operation $U(N)$. Our model is essentially a quantum neural network based on DTQW.
  arXiv  Detail & Related papers  (2022-10-26T03:51:46Z)
• On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks [91.3755431537592]
  We study how random pruning of the weights affects a neural network's neural kernel (NTK) In particular, this work establishes an equivalence of the NTKs between a fully-connected neural network and its randomly pruned version.
  arXiv  Detail & Related papers  (2022-03-27T15:22:19Z)
• Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks [79.74580058178594]
  We analyze the performance of training a pruned neural network by analyzing the geometric structure of the objective function. We show that the convex region near a desirable model with guaranteed generalization enlarges as the neural network model is pruned.
  arXiv  Detail & Related papers  (2021-10-12T01:11:07Z)
• Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss [61.26793005355441]
  Cluster-Promoting Quantization (CPQ) finds the optimal quantization grids for neural networks. DropBits is a new bit-drop technique that revises the standard dropout regularization to randomly drop bits instead of neurons. We experimentally validate our method on various benchmark datasets and network architectures.
  arXiv  Detail & Related papers  (2021-09-05T15:15:07Z)
• A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
  We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems. We numerically demonstrate that the MNIST image dataset satisfies such conditions. We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
  arXiv  Detail & Related papers  (2021-07-19T23:41:03Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)
• Quantum circuit representation of Bayesian networks [5.057312718525522]
  We develop a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states. The proposed approach is demonstrated for three examples: a 4-node oil company stock prediction, a 10-node network for liquidity risk assessment, and a 9-node naive Bayes classifier for bankruptcy prediction.
  arXiv  Detail & Related papers  (2020-04-29T04:58:54Z)
• Quantum State Discrimination on Reconfigurable Noise-Robust Quantum Networks [6.85316573653194]
  A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system. In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Walk. We optimize the parameters of the network to obtain the highest probability of correct discrimination.
  arXiv  Detail & Related papers  (2020-03-25T19:07:03Z)

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.