Engineering topological states and quantum-inspired information processing using classical circuits

• URL: http://arxiv.org/abs/2409.09919v1
• Date: Mon, 16 Sep 2024 01:30:55 GMT
• Title: Engineering topological states and quantum-inspired information processing using classical circuits
• Authors: Tian Chen, Weixuan Zhang, Deyuan Zou, Yifan Sun, Xiangdong Zhang,
• Abstract summary: We analyze the similarity between circuit Laplacian and lattice Hamiltonian, introducing topological physics based on classical circuits. We provide reviews of the research progress in quantum-inspired information processing based on the electric circuit.
• Score: 9.20739577443966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Based on the correspondence between circuit Laplacian and Schrodinger equation, recent investigations have shown that classical electric circuits can be used to simulate various topological physics and the Schrodinger's equation. Furthermore, a series of quantum-inspired information processing have been implemented by using classical electric circuit networks. In this review, we begin by analyzing the similarity between circuit Laplacian and lattice Hamiltonian, introducing topological physics based on classical circuits. Subsequently, we provide reviews of the research progress in quantum-inspired information processing based on the electric circuit, including discussions of topological quantum computing with classical circuits, quantum walk based on classical circuits, quantum combinational logics based on classical circuits, electric-circuit realization of fast quantum search, implementing unitary transforms and so on.

Related papers

• 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)
• Bridging Classical and Quantum: Group-Theoretic Approach to Quantum Circuit Simulation [0.0]
  Efficiently simulating quantum circuits on classical computers is a fundamental challenge in quantum computing. This paper presents a novel theoretical approach that achieves exponential speedups (polynomial runtime) over existing simulators. The findings may have implications for quantum algorithm design, error correction, and the development of more efficient quantum simulators.
  arXiv  Detail & Related papers  (2024-07-28T20:02:21Z)
• 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)
• Tensor Networks or Decision Diagrams? Guidelines for Classical Quantum Circuit Simulation [65.93830818469833]
  tensor networks and decision diagrams have independently been developed with differing perspectives, terminologies, and backgrounds in mind. We consider how these techniques approach classical quantum circuit simulation, and examine their (dis)similarities with regard to their most applicable abstraction level. We provide guidelines for when to better use tensor networks and when to better use decision diagrams in classical quantum circuit simulation.
  arXiv  Detail & Related papers  (2023-02-13T19:00:00Z)
• 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)
• Time-Optimal Quantum Driving by Variational Circuit Learning [2.9582851733261286]
  Digital quantum simulation and hybrid circuit learning opens up new prospects for quantum optimal control. We simulate the wave-packet expansion of a trapped quantum particle on a quantum device with a finite number qubits. We discuss the robustness of our method against errors and demonstrate the absence of barren plateaus in the circuit.
  arXiv  Detail & Related papers  (2022-11-01T11:53:49Z)
• Classical Half-Adder using Trapped-ion Quantum Bits: Towards Energy-efficient Computation [0.43981305860983716]
  Reversible computation has been proposed as a future paradigm for energy efficient computation. We provide a proof-of-principle of classical logical gates running on quantum technologies. We analyse the energy required to operate the logic gates, both theoretically and experimentally.
  arXiv  Detail & Related papers  (2022-10-19T11:23:18Z)
• Classical circuits can simulate quantum aspects [0.0]
  This study introduces a method for simulating quantum systems using electrical networks. By synthesizing interaction networks, we accurately simulate quantum systems of varying complexity, from $2-$state to $n-$state systems.
  arXiv  Detail & Related papers  (2022-09-19T18:42:06Z)
• Quantum-Classical Hybrid Information Processing via a Single Quantum System [1.1602089225841632]
  Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing. We propose a quantum reservoir processor to harness quantum dynamics in computational tasks requiring both classical and quantum inputs.
  arXiv  Detail & Related papers  (2022-09-01T14:33:40Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Gaussian initializations help deep variational quantum circuits escape from the barren plateau [87.04438831673063]
  Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respect to the circuit depth and the qubit number. This result leads to a general belief that deep quantum circuits will not be feasible for practical tasks.
  arXiv  Detail & Related papers  (2022-03-17T15:06:40Z)

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.