Information-acquiring von Neumann architecture of a computer: A theoretical design

• URL: http://arxiv.org/abs/2505.01605v7
• Date: Mon, 27 Oct 2025 07:12:47 GMT
• Title: Information-acquiring von Neumann architecture of a computer: A theoretical design
• Authors: Eiji Konishi,
• Abstract summary: We design the information-acquiring von Neumann architecture of a computer in a fine-grained or coarse-grained model of the registers.<n>This architecture enables both a Hamiltonian process converting a given input pure state to another output pure state of the system to be considered (functionality) and a physical process to acquire information.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We design the information-acquiring von Neumann architecture of a computer in a fine-grained or coarse-grained model of the registers (quickly accessible memories) in the central processing unit, where information is carried by classical bits. This architecture enables both a Hamiltonian process converting a given input pure state to another output pure state of the system to be considered (functionality) and a physical process to acquire information. The latter process is identified with the projection hypothesis (state reduction) in projective quantum measurement in the ensemble interpretation of quantum mechanics. As a novelty of this work, we treat projective quantum measurement as a classical measurement in the coarse-grained model. The main objective is to examine the present author's previously proposed state-reduction mechanism in the architecture within quantum electrodynamics in the presence of the orbital superselection rule. As a result, the electric potential serves as a binary switch for the state reduction. As a consequence of this architecture, information-acquiring artificial intelligence can be established.

Related papers

• Analog Quantum Phase Estimation with Single-Mode Readout [0.46040036610482665]
  Eigenvalue estimation is a central problem for demonstrating quantum advantage.<n>We present an analog quantum phase estimation protocol that extracts the eigenenergies of a target Hamiltonian.<n>Our results provide a resource-efficient and scalable framework for implementing quantum phase estimation in near-term quantum platforms.
  arXiv  Detail & Related papers  (2025-06-18T17:50:42Z)
• Minimal Quantum Reservoirs with Hamiltonian Encoding [72.27323884094953]
  We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding.<n>This approach circumvents many of the experimental overheads typically associated with quantum machine learning.
  arXiv  Detail & Related papers  (2025-05-28T16:50:05Z)
• Ground-State Preparation of the Fermi-Hubbard Model on a Quantum Computer with 2D Topology via Quantum Eigenvalue Transformation of Unitary Matrices [0.0]
  We apply the QETU algorithm to the $2 times 2$ Fermi-Hubbard model.<n>We present circuit simplifications tailored to the model and introduce a mapping to a 9-qubit grid-like hardware architecture inspired by fermionic swap networks.
  arXiv  Detail & Related papers  (2024-11-27T17:32:17Z)
• Mixed-Dimensional Qudit State Preparation Using Edge-Weighted Decision Diagrams [3.393749500700096]
  Quantum computers have the potential to solve intractable problems. One key element to exploiting this potential is the capability to efficiently prepare quantum states for multi-valued, or qudit, systems. In this paper, we investigate quantum state preparation with a focus on mixed-dimensional systems.
  arXiv  Detail & Related papers  (2024-06-05T18:00:01Z)
• Quantum reservoir probing: an inverse paradigm of quantum reservoir computing for exploring quantum many-body physics [0.0]
  This study proposes a reciprocal research direction: probing quantum systems themselves through their information processing performance. Building upon this concept, here we develop quantum reservoir probing (QRP), an inverse extension of the Quantum Reservoir Computing (QRC) paradigm. Unifying quantum information and quantum matter, the QRP holds great promise as a potent tool for exploring various aspects of quantum many-body physics.
  arXiv  Detail & Related papers  (2023-08-02T01:26:36Z)
• A survey of universal quantum von Neumann architecture [0.0]
  We study the recently proposed model of quantum von Neumann architecture, by putting it in a practical and broader setting. We analyze the structures of quantum CPU and quantum control unit, and draw their connections with computational advantages.
  arXiv  Detail & Related papers  (2023-07-26T14:35:40Z)
• 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)
• Explainable Representation Learning of Small Quantum States [0.0]
  We train a generative model on two-qubit density matrices generated by a parameterized quantum circuit. We observe that the model learns an interpretable representation which relates the quantum states to their underlying entanglement characteristics. Our approach offers insight into how machines learn to represent small-scale quantum systems autonomously.
  arXiv  Detail & Related papers  (2023-06-09T06:30:25Z)
• Unified Information Dynamic Analysis of Quantum Decision-Making and Search Algorithms: Computational Intelligence Measure [0.0]
  The evolution of a quantum algorithm (QA) is examined from an information theory viewpoint. The analysis of the classical and quantum information flow in Deutsch-Jozsa, Shor and Grover algorithms is used.
  arXiv  Detail & Related papers  (2023-05-30T17:04:39Z)
• A Quantum-Classical Model of Brain Dynamics [62.997667081978825]
  Mixed Weyl symbol is used to describe brain processes at the microscopic level. Electromagnetic fields and phonon modes involved in the processes are treated either classically or semi-classically. Zero-point quantum effects can be incorporated into numerical simulations by controlling the temperature of each field mode.
  arXiv  Detail & Related papers  (2023-01-17T15:16:21Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Trapped Ions as an Architecture for Quantum Computing [110.83289076967895]
  We describe one of the most promising platforms for the construction of a universal quantum computer. We discuss from the physics involved in trapping ions in electromagnetic potentials to the Hamiltonian engineering needed to generate a universal set of logic gates.
  arXiv  Detail & Related papers  (2022-07-23T22:58:50Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach [47.19265172105025]
  We propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN) egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. The architecture is based on a novel mapping from real-world data to Hilbert space.
  arXiv  Detail & Related papers  (2022-01-13T16:35:45Z)
• A prototype of quantum von Neumann architecture [0.0]
  We propose a model of universal quantum computer system, the quantum version of the von Neumann architecture. It uses ebits as elements of the quantum memory unit, and qubits as elements of the quantum control unit and processing unit. Our primary study demonstrates the manifold power of quantum information and paves the way for the creation of quantum computer systems.
  arXiv  Detail & Related papers  (2021-12-17T06:33:31Z)
• A quantum processor based on coherent transport of entangled atom arrays [44.62475518267084]
  We show a quantum processor with dynamic, nonlocal connectivity, in which entangled qubits are coherently transported in a highly parallel manner. We use this architecture to realize programmable generation of entangled graph states such as cluster states and a 7-qubit Steane code state.
  arXiv  Detail & Related papers  (2021-12-07T19:00:00Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Experimental quantum memristor [0.5396401833457565]
  We introduce and experimentally demonstrate a novel quantum-optical memristor based on integrated photonics and acts on single photons. Our device could become a building block of immediate and near-term quantum neuromorphic architectures.
  arXiv  Detail & Related papers  (2021-05-11T08:42:14Z)
• Tracing Information Flow from Open Quantum Systems [52.77024349608834]
  We use photons in a waveguide array to implement a quantum simulation of the coupling of a qubit with a low-dimensional discrete environment. Using the trace distance between quantum states as a measure of information, we analyze different types of information transfer.
  arXiv  Detail & Related papers  (2021-03-22T16:38:31Z)
• 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)
• State preparation and measurement in a quantum simulation of the O(3) sigma model [65.01359242860215]
  We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site. We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes. We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
  arXiv  Detail & Related papers  (2020-06-28T23:44:12Z)

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.