Harnessing quantumness of states using discrete Wigner functions under (non)-Markovian quantum channels

• URL: http://arxiv.org/abs/2303.05291v2
• Date: Tue, 4 Jul 2023 19:12:12 GMT
• Title: Harnessing quantumness of states using discrete Wigner functions under (non)-Markovian quantum channels
• Authors: Jai Lalita, K. G. Paulson, Subhashish Banerjee
• Abstract summary: The study of Wigner negativity and its evolution under different quantum channels can provide insight into the stability and robustness of quantum states. We construct different negative quantum states which can be used as a resource for quantum computation and quantum teleportation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The negativity of the discrete Wigner functions (DWFs) is a measure of non-classicality and is often used to quantify the degree of quantum coherence in a system. The study of Wigner negativity and its evolution under different quantum channels can provide insight into the stability and robustness of quantum states under their interaction with the environment, which is essential for developing practical quantum computing systems. We investigate the variation of DWF negativity of qubit, qutrit, and two-qubit systems under the action of (non)-Markovian random telegraph noise (RTN) and amplitude damping (AD) quantum channels. We construct different negative quantum states which can be used as a resource for quantum computation and quantum teleportation. The success of quantum computation and teleportation is estimated for these states under (non)-Markovian evolutions.

Related papers

• Quantum Equilibrium Propagation for efficient training of quantum systems based on Onsager reciprocity [0.0]
  Equilibrium propagation (EP) is a procedure that has been introduced and applied to classical energy-based models which relax to an equilibrium. Here, we show a direct connection between EP and Onsager reciprocity and exploit this to derive a quantum version of EP. This can be used to optimize loss functions that depend on the expectation values of observables of an arbitrary quantum system.
  arXiv  Detail & Related papers  (2024-06-10T17:22:09Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Protecting quantum correlations of negative quantum states using weak measurement under non-Markovian noise [0.0]
  Weak measurement (WM) and quantum measurement reversal (QMR) are crucial in protecting the collapse of quantum states. We study the quantum correlations, maximal fidelity, and fidelity deviation of the two-qubit negative quantum states developed using discrete Wigner functions. Some negative quantum states perform better with WM and QMR than the Bell state for different cases under evolution via noisy quantum channels.
  arXiv  Detail & Related papers  (2023-09-12T11:19:20Z)
• 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)
• Non-Markovian noise sources for quantum error mitigation [0.0]
  We present a non-Markovian model of quantum state evolution and a quantum error mitigation cost function tailored for NISQ devices. Our findings reveal that the cost function for quantum error mitigation increases as the coupling strength between the quantum system and its environment intensifies.
  arXiv  Detail & Related papers  (2023-02-10T05:10:27Z)
• 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)
• The power of noisy quantum states and the advantage of resource dilution [62.997667081978825]
  Entanglement distillation allows to convert noisy quantum states into singlets. We show that entanglement dilution can increase the resilience of shared quantum states to local noise.
  arXiv  Detail & Related papers  (2022-10-25T17:39:29Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
  We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
  arXiv  Detail & Related papers  (2021-08-30T23:50:05Z)
• Variational Quantum Eigensolver for SU($N$) Fermions [0.0]
  Variational quantum algorithms aim at harnessing the power of noisy intermediate-scale quantum computers. We apply the variational quantum eigensolver to study the ground-state properties of $N$-component fermions. Our approach lays out the basis for a current-based quantum simulator of many-body systems.
  arXiv  Detail & Related papers  (2021-06-29T16:39:30Z)
• Trainable Discrete Feature Embeddings for Variational Quantum Classifier [4.40450723619303]
  We show how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC) We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning.
  arXiv  Detail & Related papers  (2021-06-17T12:02:01Z)

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.