Quantum Computing enhanced Electron Microscopy (QCEM) aims to supplement the classical detector in electron microscopy (EM) with a quantum computer consisting of a trapped-ion lattice. This approach opens the door to a world of novel sensing and imaging techniques. The key ingredient to their development is a thorough understanding of the coupling of free electrons to the vibrational modes of a trapped-ion quantum computer. Here we present initial results, evaluating the scattering matrix elements in different coupling strength regimes, demonstrating the applicability of approximations, and exploring possible use cases of the coupling of a free-electron beam to a quantum computer.
Quantum Computing enhanced Electron Microscopy (QCEM) aims to supplement the classical detector in electron microscopy (EM) with a quantum computer consisting of a trapped-ion lattice. This approach opens the door to a world of novel sensing and imaging techniques. The key ingredient to their development is a thorough understanding of the coupling of free electrons to the vibrational modes of a trapped-ion quantum computer. Here we present initial results, evaluating the scattering matrix elements in different coupling strength regimes, demonstrating the applicability of approximations, and exploring possible use cases of the coupling of a free-electron beam to a quantum computer.
Quantum electron microscopy is an emerging research field at the intersection of electron microscopy, quantum optics, and nanophotonics. Free electrons can serve as powerful quantum probes, on par with photons in their ability to carry and transfer quantum information, generate entanglement within and with a specimen, and reveal previously inaccessible details on nanoscale quantum phenomena [1]. However, quantum experiments are generally highly sensitive to decoherence arising from interactions with residual gas particles, making ultra-high vacuum (UHV) conditions sometimes essential. UHV environments are also required in transmission electron microscopes (TEMs) for the investigation of contamination-sensitive materials [2, 3]. In our project, we aim to combine a trapped-ion quantum computer with a TEM to realise Quantum-Computer-Enhanced Electron Microscopy (QCEM) (www.qcem.info). In this scheme, coherent interactions between free electrons and a trapped ion require vacuum conditions beyond the standard TEM operation [4]. Although UHV-TEM systems have previously been realised [2,3, 5-7], achieving base pressures in the 10⁻⁹ mbar range or better typically involves significant financial investment, long implementation times, and close collaboration with manufacturers. We present a cost-effective, minimally invasive modification to the vacuum system of a JEOL JEM-2100F to reduce the base pressure at the sample position to below 5 × 10⁻⁹ mbar. Our approach is to integrate two additional 50 L/s NEG-ion pumps mounted on flanges near the sample region, with no structural modifications to the microscope. The NEG pumps are integrated into the microscope workflow through controlled activation and regeneration cycles compatible with standard TEM operation. This work represents a practical step toward enabling the realisation of QCEM, and the concept enables a relatively rapid implementation at a moderate cost, which can, in principle, be adapted to any TEM that provides suitable flange access to the sample chamber.
Freely propagating electrons may serve as quantum probes that can become coherently correlated with other quantum systems, offering access to advanced metrological resources. We propose a setup that coherently couples free electrons in an electron microscope to a trapped-ion quantum processor, enabling non-destructive, quantum-coherent detection and the accumulation of information across multiple electrons. Our analysis shows that single electrons can induce resolvable qubit excitations, establishing a platform for practical applications such as quantum-enhanced, dose-efficient electron microscopy.
In electron microscopy, the pursuit of atomic-scale resolution for imaging biological [1] samples and soft-matter systems [2] is perpetually constrained by beam-induced sample degredation. While classical approaches attempt to optimize the trade-off between dose efficiency and image quality, they remain fundamentally limited by the physical interactions between electrons and the sample. Quantum technologies, however, present a transformative opportunity by enhancing information extraction per electron without increasing exposure [3], thereby breaking through classical constraints. In this work, we integrate for the first time a trapped-ion qubit into a transmission electron microscope, leveraging the strong Coulomb interaction between swift electrons and Ca⁺ ions [4], along with the precise control and readout capabilities of trapped Ca⁺ ions [5]. By harnessing quantum coherence and controlled interactions, this hybrid approach seeks to advance the capabilities of low-dose imaging, improving the analysis of radiation-sensitive samples while minimizing damage. For this purpose, we customize a transmission electron microscope to provide the required ultra-high-vacuum integrity and adapt the Ca⁺ trap design, including laser cooling and state readout/manipulation, to the geometric constraints of the microscope’s pole-piece gap.
In the field of microscopy and microanalysis, technological advancements continually push the boundaries of what is possible in data analysis and simulation. Quantum computing, with its ability to perform complex calculations at unprecedented speeds, represents a promising frontier in this ongoing evolution. By harnessing the principles of quantum mechanics, such as superposition and entanglement, quantum computing offers a novel approach to processing and analyzing vast and complex datasets more efficiently than traditional computing methods [1,2]. In this context, our work focuses on leveraging quantum algorithms to enhance the processing, simulation, and analysis of image and spectroscopic data in microscopy. The primary goal is to utilize the unique capabilities of quantum computing to achieve not only faster but also more precise analysis of microscopy data. Our work primarily involves developing and applying quantum machine learning (QML) [3,4] techniques that are specifically tailored to address the challenges faced in microscopic analysis, such as handling complex data structures and improving the accuracy of simulations. Through this approach, we aim to open new pathways for advancements in microscopy, contributing to more detailed and faster analysis processes that can support a wide range of scientific investigations. Figure 1A illustrates the key parts of QML designed for the analysis and processing of microscopy data. It outlines the steps from data acquisition through preprocessing, quantum feature extraction, and quantum-enhanced machine learning. In addition, we have explored the potential incorporation of quantum algorithms into traditional image and electron diffraction pattern simulations. This approach aims to harness the unique capabilities of quantum computing to perform calculations at exponentially faster rates than classical computing. To this end, Figure 1B illustrates the methodologies we have developed, providing a visual representation of how quantum algorithms can be integrated into the image simulation process, leading to significant improvements in both accuracy and efficiency. This diagram outlines the sequential steps involved in simulating electron microscopy images and diffraction patterns through the application of quantum computing. Starting from the initialization of sample and beam parameters, it progresses through quantum state preparation, the application of quantum algorithms (including Hamiltonian evolution and Quantum Fourier Transform), measurement and data retrieval, post-processing and analysis, and concludes with the visualization of simulation results. A critical aspect of this work is the performance comparison between classical and quantum methodologies. Benchmarking against traditional methods indicates significant performance improvements when utilizing quantum algorithms. These improvements are not limited to computational speed but also include enhanced accuracy and the ability to handle complex data sets, which are often challenging for classical computing methods. Figure 2 summarizes key quantum algorithms, their associated subroutines, the theoretical speedup they offer over classical algorithms, and the main equations underlying each algorithm. These algorithms represent advanced quantum computing techniques aimed at enhancing data processing, analysis, and machine learning tasks, showcasing the potential for exponential and quadratic speedups in specific computational domains. Furthermore, we have conducted quantum cost estimations to compare the resource efficiency of quantum simulations with classical simulations, particularly in the context of electron scattering simulations. This analysis provides valuable insights into the scalability and practicality of quantum computing in microscopy, laying the groundwork for future developments in quantum-enhanced microscopy. In summary, our work demonstrates the feasibility and benefits of integrating quantum computing into the analysis pipeline of microscopy data. By leveraging the power of quantum algorithms, including quantum machine learning, we open the door to new capabilities in microscopy data analysis, promising significant advancements in the field [5-6] A) Quantum Machine Learning (QML) workflow for microscopy data analysis. B) Outlines the steps involved in simulating electron microscopy images and diffraction patterns through the application of quantum computing Overview of quantum algorithms and their applications in electron microscopy data analysis
Over the last few decades, quantum chemistry has progressed through the development of computational methods based on modern digital computers. However, these methods can hardly fulfill the exponentially-growing resource requirements when applied to large quantum systems. As pointed out by Feynman, this restriction is intrinsic to all computational models based on classical physics. Recently, the rapid advancement of trapped-ion technologies has opened new possibilities for quantum control and quantum simulations. Here, we present an efficient toolkit that exploits both the internal and motional degrees of freedom of trapped ions for solving problems in quantum chemistry, including molecular electronic structure, molecular dynamics, and vibronic coupling. We focus on applications that go beyond the capacity of classical computers, but may be realizable on state-of-the-art trapped-ion systems. These results allow us to envision a new paradigm of quantum chemistry that shifts from the current transistor to a near-future trapped-ion-based technology.