Experimental neuromorphic computing based on quantum memristor

• URL: http://arxiv.org/abs/2504.18694v1
• Date: Fri, 25 Apr 2025 21:03:19 GMT
• Title: Experimental neuromorphic computing based on quantum memristor
• Authors: Mirela Selimović, Iris Agresti, Michał Siemaszko, Joshua Morris, Borivoje Dakić, Riccardo Albiero, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Magdalena Stobińska, Philip Walther,
• Abstract summary: We report the first neuromorphic architecture based on a photonic quantum memristor.<n>We show how the memristive feedback loop enhances the non-linearity and hence the performance of the algorithm.
• Score: 0.2618499987393917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such an approach retains the universality of standard methods, while attempting to circumvent the excessive requirements, which hinder their scalability. In this context, quantum (or quantum inspired) algorithms may bring further enhancement. However, high-performing neural networks invariably display non-linear behaviours, which is diametrically opposed to the linear evolution of closed quantum systems. We propose a solution to this issue and report the first neuromorphic architecture based on a photonic quantum memristor. In detail, we show how the memristive feedback loop enhances the non-linearity and hence the performance of the algorithm. We benchmark our model on two tasks, i.e. non-linear function and time series prediction. In these cases, we highlight the essential role of the quantum memristive element and demonstrate the possibility of using it as a building block in more sophisticated networks.

Related papers

• Characterizing Non-Markovian Dynamics of Open Quantum Systems [0.0]
  We develop a structure-preserving approach to characterizing non-Markovian evolution using the time-convolutionless (TCL) master equation.<n>We demonstrate our methodology using experimental data from a superconducting qubit at the Quantum Device Integration Testbed (QuDIT) at Lawrence Livermore National Laboratory.<n>These findings provide valuable insights into efficient modeling strategies for open quantum systems, with implications for quantum control and error mitigation in near-term quantum processors.
  arXiv  Detail & Related papers  (2025-03-28T04:43:24Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
  We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems. We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
  arXiv  Detail & Related papers  (2023-08-22T09:11:53Z)
• Information-driven Nonlinear Quantum Neuron [0.0]
  In this study, a hardware-efficient quantum neural network operating as an open quantum system is proposed. We show that this dissipative model based on repeated interactions, which allows for easy parametrization of input quantum information, exhibits differentiable, non-linear activation functions.
  arXiv  Detail & Related papers  (2023-07-18T07:12:08Z)
• Quantum Neural Network for Quantum Neural Computing [0.0]
  We propose a new quantum neural network model for quantum neural computing. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks.
  arXiv  Detail & Related papers  (2023-05-15T11:16:47Z)
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning [52.77024349608834]
  Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. Key issue is how to address the inherent non-linearity of classical deep learning. We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
  arXiv  Detail & Related papers  (2022-12-22T16:06:24Z)
• Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor [0.0]
  In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions. As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions. This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs.
  arXiv  Detail & Related papers  (2022-12-21T03:26:32Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• Markovian Quantum Neuroevolution for Machine Learning [0.0]
  We introduce a quantum neuroevolution algorithm that autonomously finds near-optimal quantum neural networks for different machine-learning tasks. In particular, we establish a one-to-one mapping between quantum circuits and directed graphs, and reduce the problem of finding the appropriate gate sequences.
  arXiv  Detail & Related papers  (2020-12-30T12:42:38Z)

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.