Related papers: Research on quantum compilation of neutral atom quantum computing platform

Research on quantum compilation of neutral atom quantum computing platform

URL: http://arxiv.org/abs/2501.05266v1
Date: Thu, 09 Jan 2025 14:20:32 GMT
Title: Research on quantum compilation of neutral atom quantum computing platform
Authors: Chongyuan Xu,
Abstract summary: Neutral atom quantum computing platform is a quantum computing implementation method with high controllability and scalability. We systematically review the quantum compilation methods based on matrix decomposition, and propose a compilation algorithm suitable for neutral atom quantum computing.
Score: 0.0
License:
Abstract: Quantum compilation is the process of decomposing high-level quantum algorithms or arbitrary unitary operations into quantum circuits composed of a specific set of quantum gates. Neutral atom quantum computing platform is a quantum computing implementation method with high controllability and scalability, but its quantum compilation method is not mature. We systematically review the quantum compilation methods based on matrix decomposition, and propose a compilation algorithm suitable for neutral atom quantum computing, which can effectively decompose any unitary operation into a series of quantum gates suitable for the neutral atom platform, and ensure that the generated quantum circuits can run directly on the platform.

Related papers

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)
The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z)
Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
arXiv Detail & Related papers (2024-06-05T17:16:07Z)
Quantum Circuit Ansatz: Patterns of Abstraction and Reuse of Quantum Algorithm Design [3.8425905067219492]
The paper presents a categorized catalog of quantum circuit ansatzes. Each ansatz is described with details such as intent, motivation, applicability, circuit diagram, implementation, example, and see also. Practical examples are provided to illustrate their application in quantum algorithm design.
arXiv Detail & Related papers (2024-05-08T12:44:37Z)
Power Characterization of Noisy Quantum Kernels [52.47151453259434]
We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small. We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
arXiv Detail & Related papers (2024-01-31T01:02:16Z)
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)
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)
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 walk processes in quantum devices [55.41644538483948]
We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
arXiv Detail & Related papers (2020-12-28T18:04:16Z)
Quantum ensemble of trained classifiers [2.048335092363436]
A quantum computer is capable of representing an exponentially large set of states, according to the number of qubits available. Quantum machine learning explores the potential of quantum computing to enhance machine learning algorithms.
arXiv Detail & Related papers (2020-07-18T01:01:33Z)
On quantum ensembles of quantum classifiers [0.0]
Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques. A specific implementation of the quantum ensemble of quantum classifiers, called the accuracy-weighted quantum ensemble, can be fully dequantised. On the other hand, the general quantum ensemble framework is shown to contain the well-known Deutsch-Jozsa algorithm that notably provides a quantum speedup.
arXiv Detail & Related papers (2020-01-29T13:46:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.