Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages

• URL: http://arxiv.org/abs/2411.01822v1
• Date: Mon, 04 Nov 2024 05:41:31 GMT
• Title: Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages
• Authors: Xi He, Feiyu Du, Xiaohan Yu, Yang Zhao, Tao Lei,
• Abstract summary: Two transfer fusion frameworks are proposed to predict the labels of a target domain data. The frameworks fuse the quantum data from two different, but related domains through a quantum information infusion channel. One framework, the quantum basic linear algebra subroutines (QBLAS) based implementation, can theoretically achieve the procedure of transfer fusion with quadratic speedup. The other framework, a hardware-scalable architecture, is implemented on the noisy intermediate-scale quantum (NISQ) devices.
• Score: 13.430382791978948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scarcity of labelled data is specifically an urgent challenge in the field of quantum machine learning (QML). Two transfer fusion frameworks are proposed in this paper to predict the labels of a target domain data by aligning its distribution to a different but related labelled source domain on quantum devices. The frameworks fuses the quantum data from two different, but related domains through a quantum information infusion channel. The predicting tasks in the target domain can be achieved with quantum advantages by post-processing quantum measurement results. One framework, the quantum basic linear algebra subroutines (QBLAS) based implementation, can theoretically achieve the procedure of transfer fusion with quadratic speedup on a universal quantum computer. In addition, the other framework, a hardware-scalable architecture, is implemented on the noisy intermediate-scale quantum (NISQ) devices through a variational hybrid quantum-classical procedure. Numerical experiments on the synthetic and handwritten digits datasets demonstrate that the variatioinal transfer fusion (TF) framework can reach state-of-the-art (SOTA) quantum DA method performance.

Related papers

• Quantum-Channel Matrix Optimization for Holevo Bound Enhancement [87.57725685513088]
  We propose a unified projected gradient ascent algorithm to optimize the quantum channel given a fixed input ensemble.<n> Simulation results demonstrate that the proposed quantum channel optimization yields higher Holevo bounds than input ensemble optimization.
  arXiv  Detail & Related papers  (2026-02-19T04:15:03Z)
• Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach [7.995944232955725]
  We propose a novel integrated sporadic-personalized approach called SPQFL that simultaneously handles quantum noise and data heterogeneity in a single QFL framework.<n>We conduct a rigorous convergence analysis for the proposed SPQFL framework, with both sporadic and personalized learning considerations.<n> Extensive simulation results in real-world datasets also illustrate that the proposed SPQFL approach yields significant improvements in terms of training performance and convergence stability.
  arXiv  Detail & Related papers  (2026-01-11T23:29:08Z)
• Learning quantum phase transition in parametrized quantum circuits with an attention mechanism [0.18416014644193066]
  Learning many-body quantum states and quantum phase transitions remains a major challenge in quantum many-body physics.<n>We propose a novel framework that bypasses the need to measure physical observables by directly learning the parameters of parameterized quantum circuits.
  arXiv  Detail & Related papers  (2025-06-07T06:21:40Z)
• Transport approach to quantum state tomography [0.0]
  Quantum state tomography (QST) is a central task for quantum information processing, enabling quantum cryptography, computation, and state certification.<n>Traditional QST relies on projective measurements of single- and two-qubit Pauli operators, requiring the system of interest to be isolated from environmental dissipation.<n>We demonstrate that measuring currents and associated transport quantities flowing through a quantum system in an open configuration enable the reconstruction of its quantum state.
  arXiv  Detail & Related papers  (2025-01-28T09:50:23Z)
• Simulation of Quantum Transduction Strategies for Quantum Networks [7.486717790185952]
  We extend SeQUeNCe, a discrete-event simulator of quantum networks, with a quantum transducer component. We explore two protocols for transmitting quantum information between superconducting nodes via optical channels. Our preliminary results align with theoretical predictions, offering simulation-based validation of the protocols.
  arXiv  Detail & Related papers  (2024-11-18T08:47:11Z)
• Distributed Quantum Computation via Entanglement Forging and Teleportation [13.135604356093193]
  Distributed quantum computation is a practical method for large-scale quantum computation on quantum processors with limited size. In this paper, we demonstrate the methods to implement a nonlocal quantum circuit on two quantum processors without any quantum correlations.
  arXiv  Detail & Related papers  (2024-09-04T08:10:40Z)
• Universal quantum computation using quantum annealing with the transverse-field Ising Hamiltonian [0.0]
  We present a practical method for implementing universal quantum computation using the transverse-field Ising Hamiltonian. Our proposal is compatible with D-Wave devices, opening up possibilities for realizing large-scale gate-based quantum computers.
  arXiv  Detail & Related papers  (2024-02-29T12:47:29Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Enhancing Quantum Annealing in Digital-Analog Quantum Computing [0.0]
  Digital-analog quantum computing (DAQC) offers a promising approach to addressing the challenges of building a practical quantum computer. We propose an algorithm designed to enhance the performance of quantum annealing. This study provides an example of how processing quantum data using a quantum circuit can outperform classical data processing, which discards quantum information.
  arXiv  Detail & Related papers  (2023-06-03T09:16:15Z)
• 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)
• Optimisation-free Classification and Density Estimation with Quantum Circuits [0.0]
  We demonstrate the implementation of a novel machine learning framework for probability density estimation and classification using quantum circuits. The framework maps a training data set or a single data sample to the quantum state of a physical system through quantum feature maps. We discuss a variational quantum circuit approach that could leverage quantum advantage for our framework.
  arXiv  Detail & Related papers  (2022-03-28T02:40:24Z)
• Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
  We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
  arXiv  Detail & Related papers  (2021-12-27T21:15:35Z)
• 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)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)

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.