Machine learning-enhanced optical tweezers for defect-free rearrangement
- URL: http://arxiv.org/abs/2401.04893v1
- Date: Wed, 10 Jan 2024 02:53:06 GMT
- Title: Machine learning-enhanced optical tweezers for defect-free rearrangement
- Authors: Yongwoong Lee, Eunmi Chae
- Abstract summary: We introduce a machine learning approach that uses the Proximal Policy Optimization model to optimize this rearrangement process.
This method focuses on efficiently solving the shortest path problem, ensuring the formation of defect-free tweezer arrays.
This advancement presents new opportunities in tweezer array, potentially boosting the efficiency and precision of computing research.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Optical tweezers constitute pivotal tools in Atomic, Molecular, and
Optical(AMO) physics, facilitating precise trapping and manipulation of
individual atoms and molecules. This process affords the capability to generate
desired geometries in both one-dimensional and two-dimensional spaces, while
also enabling real-time reconfiguration of atoms. Due to stochastic defects in
these tweezers, which cause catastrophic performance degradation especially in
quantum computations, it is essential to rearrange the tweezers quickly and
accurately. Our study introduces a machine learning approach that uses the
Proximal Policy Optimization model to optimize this rearrangement process. This
method focuses on efficiently solving the shortest path problem, ensuring the
formation of defect-free tweezer arrays. By implementing machine learning, we
can calculate optimal motion paths under various conditions, resulting in
promising results in model learning. This advancement presents new
opportunities in tweezer array rearrangement, potentially boosting the
efficiency and precision of quantum computing research.
Related papers
- Optical tweezer generation using automated alignment and adaptive optics [0.0]
State-of-the-art precision of optical alignment to achieve fine-tuning is reaching the limits of manual control.
One of the elementary techniques of manual alignment of optics is cross-walking of laser beams.
We apply this technique to mechanically align high numerical aperture objectives and show that we can produce high-quality tweezers.
arXiv Detail & Related papers (2023-12-20T18:40:37Z) - Gradual Optimization Learning for Conformational Energy Minimization [69.36925478047682]
Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks significantly reduces the required additional data.
Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules.
arXiv Detail & Related papers (2023-11-05T11:48:08Z) - Generating extreme quantum scattering in graphene with machine learning [0.0]
Graphene quantum dots provide a platform for manipulating electron behaviors in two-dimensional (2D) Dirac materials.
There are applications such as cloaking or superscattering where the challenging problem of inverse design needs to be solved.
We articulate a machine-learning approach to addressing the inverse-design problem.
Our physics-based machine-learning approach can be a powerful design tool for 2D Dirac material-based electronics.
arXiv Detail & Related papers (2022-12-13T22:54:24Z) - Efficient algorithms to solve atom reconfiguration problems. II. The
assignment-rerouting-ordering (aro) algorithm [51.02512563152503]
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.
arXiv Detail & Related papers (2022-12-11T19:48:25Z) - Retrieving space-dependent polarization transformations via near-optimal
quantum process tomography [55.41644538483948]
We investigate the application of genetic and machine learning approaches to tomographic problems.
We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time.
We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
arXiv Detail & Related papers (2022-10-27T11:37:14Z) - Gaussian Moments as Physically Inspired Molecular Descriptors for
Accurate and Scalable Machine Learning Potentials [0.0]
We propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks.
The accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models.
arXiv Detail & Related papers (2021-09-15T16:46:46Z) - Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions.
Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
arXiv Detail & Related papers (2021-06-24T20:16:02Z) - SE(3)-equivariant prediction of molecular wavefunctions and electronic
densities [4.2572103161049055]
We introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data.
Our model reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art.
We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference wavefunctions implicitly learns to correct for electronic many-body interactions.
arXiv Detail & Related papers (2021-06-04T08:57:46Z) - Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry.
New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications.
We present a novel technique to reconstruct the transfer matrix of linear optical networks.
arXiv Detail & Related papers (2020-10-01T16:04:22Z) - Preparation of excited states for nuclear dynamics on a quantum computer [117.44028458220427]
We study two different methods to prepare excited states on a quantum computer.
We benchmark these techniques on emulated and real quantum devices.
These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
arXiv Detail & Related papers (2020-09-28T17:21:25Z)
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.