Quantum optimization with globally driven neutral atom arrays

• URL: http://arxiv.org/abs/2410.03902v1
• Date: Fri, 4 Oct 2024 20:09:10 GMT
• Title: Quantum optimization with globally driven neutral atom arrays
• Authors: Martin Lanthaler, Kilian Ender, Clemens Dlaska, Wolfgang Lechner,
• Abstract summary: We propose a scalable encoding of optimization problems with arbitrary connectivity. We show, that these auxiliary atoms can be simultaneously used for both problem-specific programming and the mitigation of unwanted effects.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a scalable encoding of combinatorial optimization problems with arbitrary connectivity, including higher-order terms, on arrays of trapped neutral atoms requiring only a global laser drive. Our approach relies on modular arrangements of a small number of problem-independent gadgets. These gadgets represent maximum-weight independent set (MWIS) problems on unit-disk graphs, which are native to such devices. Instead of programming MWIS weights with site-dependent laser detunings, the scheme relies on systematic placements of auxiliary atoms. We show, that these auxiliary atoms can be simultaneously used for both problem-specific programming and the mitigation of unwanted effects originating from the tails of long-range interactions.

Related papers

• A quantum wire approach to weighted combinatorial graph optimisation problems [0.0]
  We present an efficient scheme based on chains of Rydberg-blockaded atoms, which we call quantum wires, to embed optimisation problems into a layout compatible with the neutral atom architecture. Our work expands the existing toolkit to explore the potential use of neutral atom arrays to solve large scale optimisation problems.
  arXiv  Detail & Related papers  (2025-03-21T13:00:51Z)
• qReduMIS: A Quantum-Informed Reduction Algorithm for the Maximum Independent Set Problem [10.162280421416208]
  We propose and implement a quantum-informed reduction algorithm for the maximum independent set problem. We show that qReduMIS can help address fundamental performance limitations faced by a broad set of solvers including Rydberg quantum devices. We outline implementations of qReduMIS with alternative platforms, such as superconducting qubits or trapped ions.
  arXiv  Detail & Related papers  (2025-03-16T15:41:42Z)
• Solving Constrained Optimization Problems Using Hybrid Qubit-Qumode Quantum Devices [7.954263125127824]
  We show how to solve Quadratic Unconstrained Binary Optimization problems using hybrid qubit$qux2014$ devices. The potential of hybrid quantum computers to tackle complex optimization problems in both academia and industry is highlighted.
  arXiv  Detail & Related papers  (2025-01-20T20:40:58Z)
• Encoding arbitrary Ising Hamiltonians on Spatial Photonic Ising Machines [0.0]
  We introduce and experimentally validate a SPIM instance that enables direct control over the full interaction matrix. We demonstrate the conformity of the experimentally measured Ising energy with the theoretically expected values and then proceed to solve both the unweighted and weighted graph problems. Our approach greatly expands the applicability of SPIMs for real-world applications without sacrificing any of the inherent advantages of the system.
  arXiv  Detail & Related papers  (2024-07-12T10:54:07Z)
• Exploring the impact of graph locality for the resolution of MIS with neutral atom devices [0.755972004983746]
  We build upon recent progress made for using 3D arrangements to embed more complex classes of graphs. We report experimental and theoretical results which represent important steps towards tackling hard problems on quantum computers.
  arXiv  Detail & Related papers  (2023-06-23T08:53:16Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Quantum Worst-Case to Average-Case Reductions for All Linear Problems [66.65497337069792]
  We study the problem of designing worst-case to average-case reductions for quantum algorithms. We provide an explicit and efficient transformation of quantum algorithms that are only correct on a small fraction of their inputs into ones that are correct on all inputs.
  arXiv  Detail & Related papers  (2022-12-06T22:01:49Z)
• Quantum optimization with arbitrary connectivity using Rydberg atom arrays [0.0]
  We extend the classes of problems that can be efficiently encoded in Rydberg arrays by constructing explicit mappings from the original problems. We analyze several examples, including: maximum weighted independent set on graphs with arbitrary connectivity, quadratic unconstrained binary optimization problems with arbitrary or restricted connectivity, and integer factorization.
  arXiv  Detail & Related papers  (2022-09-08T18:00:00Z)
• General Hamiltonian Representation of ML Detection Relying on the Quantum Approximate Optimization Algorithm [74.6114458993128]
  The quantum approximate optimization algorithm (QAOA) conceived for solving optimization problems can be run on the existing noisy intermediate-scale quantum (NISQ) devices. We solve the maximum likelihood (ML) detection problem for general constellations by appropriately adapting the QAOA. In particular, for an M-ary Gray-mapped quadrature amplitude modulation (MQAM) constellation, we show that the specific qubits encoding the in-phase components and those encoding the quadrature components are independent in the quantum system of interest.
  arXiv  Detail & Related papers  (2022-04-11T14:11:24Z)
• Efficient two-dimensional defect-free dual-species atom arrays rearrangement algorithm with near-fewest atom moves [12.346877792340315]
  We propose an efficient connectivity optimization algorithm (HCOA) to rearrange theally loaded atoms into arbitrary configurations. Our algorithm shows a high success rate (> 97%), low extra atom moves ratio, good scalability, and flexibility.
  arXiv  Detail & Related papers  (2022-03-22T17:03:09Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• Demonstration of multi-qubit entanglement and algorithms on a programmable neutral atom quantum computer [0.0]
  Neutral atom hyperfine qubits provide inherent scalability due to their identical characteristics, long coherence times, and ability to be trapped in dense multi-dimensional arrays. We demonstrate several quantum algorithms on a programmable gate model neutral atom quantum computer in an architecture based on individual addressing of single atoms with tightly focused optical beams scanned across a two-dimensional array of qubits.
  arXiv  Detail & Related papers  (2021-12-29T15:02:43Z)
• Hardware-Efficient, Fault-Tolerant Quantum Computation with Rydberg Atoms [55.41644538483948]
  We provide the first complete characterization of sources of error in a neutral-atom quantum computer. We develop a novel and distinctly efficient method to address the most important errors associated with the decay of atomic qubits to states outside of the computational subspace. Our protocols can be implemented in the near-term using state-of-the-art neutral atom platforms with qubits encoded in both alkali and alkaline-earth atoms.
  arXiv  Detail & Related papers  (2021-05-27T23:29:53Z)
• Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays [54.43962058166702]
  millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays have received great attention. In this work, we investigate the joint design of a beam precoding matrix for mmWave MU-MIMO systems with DLA.
  arXiv  Detail & Related papers  (2021-01-05T03:55:04Z)

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.