Efficient algorithms to solve atom reconfiguration problems. II. The
assignment-rerouting-ordering (aro) algorithm
- URL: http://arxiv.org/abs/2212.05586v1
- Date: Sun, 11 Dec 2022 19:48:25 GMT
- Title: Efficient algorithms to solve atom reconfiguration problems. II. The
assignment-rerouting-ordering (aro) algorithm
- Authors: Remy El Sabeh, Jessica Bohm, Zhiqian Ding, Stephanie Maaz, Naomi
Nishimura, Izzat El Hajj, Amer E. Mouawad and Alexandre Cooper
- Abstract summary: atom reconfiguration problems require solving an atom problem quickly and efficiently.
A typical approach to solve atom reconfiguration problems is to use an assignment algorithm to determine which atoms to move to which traps.
This approach does not optimize for the number of displaced atoms nor the number of times each atom is displaced.
We propose the assignment-rerouting-ordering (aro) algorithm to improve the performance of assignment-based algorithms in solving atom reconfiguration problems.
- Score: 51.02512563152503
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Programmable arrays of optical traps enable the assembly of configurations of
single atoms to perform controlled experiments on quantum many-body systems.
Finding the sequence of control operations to transform an arbitrary
configuration of atoms into a predetermined one requires solving an atom
reconfiguration problem quickly and efficiently. A typical approach to solve
atom reconfiguration problems is to use an assignment algorithm to determine
which atoms to move to which traps. This approach results in control protocols
that exactly minimize the number of displacement operations; however, this
approach does not optimize for the number of displaced atoms nor the number of
times each atom is displaced, resulting in unnecessary control operations that
increase the execution time and failure rate of the control protocol. In this
work, we propose the assignment-rerouting-ordering (aro) algorithm to improve
the performance of assignment-based algorithms in solving atom reconfiguration
problems. The aro algorithm uses an assignment subroutine to minimize the total
distance traveled by all atoms, a rerouting subroutine to reduce the number of
displaced atoms, and an ordering subroutine to guarantee that each atom is
displaced at most once. The ordering subroutine relies on the existence of a
partial ordering of moves that can be obtained using a polynomial-time
algorithm that we introduce within the formal framework of graph theory. We
numerically quantify the performance of the aro algorithm in the presence and
in the absence of loss, and show that it outperforms the exact, approximation,
and heuristic algorithms that we use as benchmarks. Our results are useful for
assembling large configurations of atoms with high success probability and fast
preparation time, as well as for designing and benchmarking novel atom
reconfiguration algorithms.
Related papers
- An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks.
The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions.
We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
arXiv Detail & Related papers (2023-09-30T15:57:14Z) - Accelerating Cutting-Plane Algorithms via Reinforcement Learning
Surrogates [49.84541884653309]
A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms.
Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability.
We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
arXiv Detail & Related papers (2023-07-17T20:11:56Z) - Sequential minimum optimization algorithm with small sample size
estimators [0.06445605125467573]
Sequential minimum optimization is a machine-learning global search training algorithm.
We apply it to photonics circuits where the additional challenge appears: low frequency of coincidence events lowers the speed of the algorithm.
arXiv Detail & Related papers (2023-03-02T06:02:46Z) - Efficient algorithms to solve atom reconfiguration problems. I. The
redistribution-reconfiguration (red-rec) algorithm [51.02512563152503]
We numerically quantify the performance of the red-rec algorithm, both in the absence and in the presence of loss.
We show that the number of traps required to prepare a compact-centered configuration of atoms on a grid with a mean success probability of one half scales as the 3/2 power of the number of desired atoms.
The red-rec algorithm admits an efficient implementation that can readily be deployed on real-time control systems.
arXiv Detail & Related papers (2022-12-07T19:00:01Z) - Parallel compression algorithm for fast preparation of defect-free atom arrays [2.9592586928462308]
We propose a novel parallel compression algorithm which leverages multiple mobile tweezers to transfer atoms simultaneously.
The total time cost could be reduced to scale linearly with the number of target sites.
arXiv Detail & Related papers (2022-12-06T15:20:40Z) - Parallel assembly of arbitrary defect-free atom arrays with a
multi-tweezer algorithm [0.0]
Large-scale defect-free atom arrays are an important precursor for quantum information processing and quantum simulation.
Here, we demonstrate a novel parallel rearrangement algorithm that uses multiple mobile tweezers to sort and compress atom arrays.
With a high degree of parallelism, our algorithm offers a reduced move complexity compared to both single-tweezer algorithms and existing multi-tweezer algorithms.
arXiv Detail & Related papers (2022-09-16T16:34:29Z) - 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) - Resource Optimisation of Coherently Controlled Quantum Computations with
the PBS-calculus [55.2480439325792]
Coherent control of quantum computations can be used to improve some quantum protocols and algorithms.
We refine the PBS-calculus, a graphical language for coherent control inspired by quantum optics.
arXiv Detail & Related papers (2022-02-10T18:59:52Z) - Exploiting Higher Order Smoothness in Derivative-free Optimization and
Continuous Bandits [99.70167985955352]
We study the problem of zero-order optimization of a strongly convex function.
We consider a randomized approximation of the projected gradient descent algorithm.
Our results imply that the zero-order algorithm is nearly optimal in terms of sample complexity and the problem parameters.
arXiv Detail & Related papers (2020-06-14T10:42:23Z)
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.