Enhancing associative memory recall and storage capacity using confocal cavity QED

• URL: http://arxiv.org/abs/2009.01227v1
• Date: Wed, 2 Sep 2020 17:59:15 GMT
• Title: Enhancing associative memory recall and storage capacity using confocal cavity QED
• Authors: Brendan P. Marsh, Yudan Guo, Ronen M. Kroeze, Sarang Gopalakrishnan, Surya Ganguli, Jonathan Keeling, and Benjamin L. Lev
• Abstract summary: We introduce a near-term experimental platform for realizing an associative memory. It can simultaneously store many memories by using spinful bosons coupled to a multimode optical cavity. We show that this nonequilibrium quantum-optical scheme has significant advantages for associative memory over Glauber dynamics.
• Score: 15.696215759892052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a near-term experimental platform for realizing an associative memory. It can simultaneously store many memories by using spinful bosons coupled to a degenerate multimode optical cavity. The associative memory is realized by a confocal cavity QED neural network, with the cavity modes serving as the synapses, connecting a network of superradiant atomic spin ensembles, which serve as the neurons. Memories are encoded in the connectivity matrix between the spins, and can be accessed through the input and output of patterns of light. Each aspect of the scheme is based on recently demonstrated technology using a confocal cavity and Bose-condensed atoms. Our scheme has two conceptually novel elements. First, it introduces a new form of random spin system that interpolates between a ferromagnetic and a spin-glass regime as a physical parameter is tuned---the positions of ensembles within the cavity. Second, and more importantly, the spins relax via deterministic steepest-descent dynamics, rather than Glauber dynamics. We show that this nonequilibrium quantum-optical scheme has significant advantages for associative memory over Glauber dynamics: These dynamics can enhance the network's ability to store and recall memories beyond that of the standard Hopfield model. Surprisingly, the cavity QED dynamics can retrieve memories even when the system is in the spin glass phase. Thus, the experimental platform provides a novel physical instantiation of associative memories and spin glasses as well as provides an unusual form of relaxational dynamics that is conducive to memory recall even in regimes where it was thought to be impossible.

Related papers

• Dense Associative Memory Through the Lens of Random Features [48.17520168244209]
  Dense Associative Memories are high storage capacity variants of the Hopfield networks. We show that this novel network closely approximates the energy function and dynamics of conventional Dense Associative Memories.
  arXiv  Detail & Related papers  (2024-10-31T17:10:57Z)
• Theoretical framework for quantum associative memories [0.8437187555622164]
  Associative memory refers to the ability to relate a memory with an input and targets the restoration of corrupted patterns. We develop a comprehensive framework for a quantum associative memory based on open quantum system dynamics.
  arXiv  Detail & Related papers  (2024-08-26T13:46:47Z)
• Emulating Complex Synapses Using Interlinked Proton Conductors [17.304569471460013]
  We experimentally realize the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.
  arXiv  Detail & Related papers  (2024-01-26T18:16:06Z)
• Modeling Non-Covalent Interatomic Interactions on a Photonic Quantum Computer [50.24983453990065]
  We show that the cQDO model lends itself naturally to simulation on a photonic quantum computer. We calculate the binding energy curve of diatomic systems by leveraging Xanadu's Strawberry Fields photonics library. Remarkably, we find that two coupled bosonic QDOs exhibit a stable bond.
  arXiv  Detail & Related papers  (2023-06-14T14:44:12Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• Optimal storage capacity of quantum Hopfield neural networks [0.0]
  It is a challenging open problem to analyze quantum associative memories with an extensive number of patterns. We propose and explore a general method for evaluating the maximal storage capacity of quantum neural network models.
  arXiv  Detail & Related papers  (2022-10-14T15:21:21Z)
• Quantum associative memory with a single driven-dissipative nonlinear oscillator [0.0]
  We propose a realization of associative memory with a single driven-dissipative quantum oscillator. The model can improve the storage capacity of discrete neuron-based systems in a large regime. We show that the associative-memory capacity is inherently related to the existence of a spectral gap in the Liouvillian superoperator.
  arXiv  Detail & Related papers  (2022-05-19T12:00:35Z)
• Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks [69.42260428921436]
  Cross-frequency coupling (CFC) is associated with information integration across populations of neurons. We construct a model of CFC which predicts a computational role for observed $theta - gamma$ oscillatory circuits in the hippocampus and cortex. We show that the presence of CFC increases the memory capacity of a population of neurons connected by plastic synapses.
  arXiv  Detail & Related papers  (2022-04-05T17:13:36Z)
• Astrocytes mediate analogous memory in a multi-layer neuron-astrocytic network [52.77024349608834]
  We show how a piece of information can be maintained as a robust activity pattern for several seconds then completely disappear if no other stimuli come. This kind of short-term memory can keep operative information for seconds, then completely forget it to avoid overlapping with forthcoming patterns. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.
  arXiv  Detail & Related papers  (2021-08-31T16:13:15Z)
• Signatures of associative memory behavior in a multi-mode spin-boson model [0.0]
  We show that non-equilibrium strongly interacting spin-boson systems can mimic some fundamental properties of an associative memory. We identify two phases, a "paramagnetic" and a "ferromagnetic" one, and a crossover behavior between these regimes. We highlight similarities and differences with the thermal dynamics of a Hopfield associative memory and show that indeed elements of "machine learning behavior" emerge in strongly coupled spin-boson systems.
  arXiv  Detail & Related papers  (2020-03-02T16:34:02Z)
• Multidimensional dark space and its underlying symmetries: towards dissipation-protected qubits [62.997667081978825]
  We show that a controlled interaction with the environment may help to create a state, dubbed as em dark'', which is immune to decoherence. To encode quantum information in the dark states, they need to span a space with a dimensionality larger than one, so different states act as a computational basis. This approach offers new possibilities for storing, protecting and manipulating quantum information in open systems.
  arXiv  Detail & Related papers  (2020-02-01T15:57:37Z)

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.