Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface. The results show improvement in compensation using gradient descent and the machine learner over manual compensation. This improvement is inferred from an increase of the fluorescence rate of 78% and 96% respectively, for a trapped [Formula: see text]Yb[Formula: see text] ion driven by a laser tuned to [Formula: see text] MHz of the [Formula: see text]S[Formula: see text]P[Formula: see text] Doppler cooling transition at 369.5 nm.
Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface. The results show improvement in compensation using gradient descent and the machine learner over manual compensation. This improvement is inferred from an increase of the fluorescence rate of 78% and 96% respectively, for a trapped [Formula: see text]Yb[Formula: see text] ion driven by a laser tuned to [Formula: see text] MHz of the [Formula: see text]S[Formula: see text]P[Formula: see text] Doppler cooling transition at 369.5 nm.
Minimizing the micromotion of a trapped ion in a linear Paul trap is of great importance in maintaining long coherence time as well as implementing quantum logic gates with high fidelity, which is crucial for large-scale quantum computation with trapped ions. Here, by applying the RF (radio frequency)-photon correlation technique, we demonstrate that a machine learning method based on artificial neural networks can quickly search for optimal voltage settings of the electrodes to minimize the trapped ion's micromotion. This machine learning assisted RF-photon correlation technique can be straightforwardly applied to more complicated surface ion traps with many electrodes, where the manual minimization of the excess micromotion generated by stray electric fields would become extremely challenging for the larger number of electrodes with various voltage settings. Instead, the presented machine learning assisted method provides an effective and automatic way to address this need.
An ion trap is a device to confine charged particles by utilizing electromagnetic fields.Since the trapped ions have a feasibility of the individual state manipulation, a long coherence time, and an ideal isolation from the surroundings, the ion-trap technology becomes one of the leading candidates for the physical implementation of quantum information processing.In order to build a large-scale integrated iontrap system for realizing complex quantum algorithms, microfabrication technologies have been applied to construct ion traps.The use of micro-electromechanical system (MEMS) ion-trap chip allows a scalable architecture of ion-trap arrays and integration of functional components to the trap chip, but a few side effects also arise.One of the most particular problems is stray fields generated by the charges accumulated on the sidewalls of thick dielectric pillars or the native oxide grown on metal surfaces, since the stray fields lead to ion micromotions which can cause heating and escape of the trapped ions.This dissertation presents a silicon surface ion-trap chip with dielectric sidewalls shielded by metal films.The oxide pillars supporting the top electrodes are fabricated to have overhang structures, and the upper and lower part of the pillars are coated by separate aluminum layers which are electrically isolated from each other.In order to evaluate the effects of the native ii metal oxide on the electrode surface to the charging phenomenon, a trap chip with an additional gold layer on the aluminum electrodes is also fabricated.An ultra-high vacuum (UHV) chamber, electrical connections, and an optical setup are prepared to trap 174 Yb + ions, and the ions are successfully trapped by using the experimental setup including the fabricated ion-trap chip.To evaluate the effectiveness of the proposed electrode structures, charging is intentionally induced on the chip surface by injecting a 355-nm pulse laser with 40-μW power perpendicularly to the chip surface.The intensity of the stray field is estimated by measuring the displacement of ion position after charging is induced.When the Al-based chips with exposed and Al-coated dielectric sidewalls are used, the standard deviation of the intensities of stray fields are 3.53, and 5.63 V/m, respectively.However, the standard deviation of the intensity of stray fields in the case of using Au-coated chip is 1.56 V/m, and any stray field is measured when the laser was injected on the surface or sidewalls of the gold-coated electrodes.These experimental results indicate that the gold coating on the surface of the aluminum electrodes and the sidewalls of dielectric pillars is effective for eliminating the generation of stray fields induced by static charges.
Stray electric fields induce excess micromotion in ion traps, limiting experimental performance. We present a new micromotion-compensation technique that utilizes a dark ion in a multi-species bright-dark-bright linear ion crystal. Stray electric fields in the radial plane of the trap deform the crystal axially due to the different masses of dark and bright ions. We exploit the mode softening near the transition to the zig-zag configuration to enhance the crystal deformation and, as a result, increase the method’s sensitivity dramatically. We corroborate our results with a modified ion-displacement compensation method using a single bright ion. Our modification allows us to compensate stray fields on the 2D radial plane from a 1D measurement of the ion position on the camera by controlling the asymmetry of the two radial modes of the trap. Both methods require only a fixed imaging camera and continuous ion-fluorescence detection. As such, they can be readily implemented in most ion-trapping experiments without additional hardware modifications.
This paper develops a method for estimating the locations of laser-induced stray charges on surface-electrode ion traps. How the electric potential at the trapped-ion location changes in the presence of laser-induced stray charges is theoretically modeled, and the magnitude of the shift in the secular frequency of a trapped ion is derived from the model. This model can be used inversely to find the locations of stray charges from the secular frequency shift under certain conditions. Typically, this inverse relationship is not unique. However, in surface-electrode ion traps, the secular frequency shift caused by the stray charges can be measured at multiple ion positions, and therefore, the required number of secular-frequency-shift measurements can be performed to determine the stray-charge locations uniquely. This model is experimentally evaluated by intentionally producing electric charges at a single location by irradiating a laser onto that spot and by measuring the resulting secular frequency shift. The location of produced charges is estimated from the measured result, and the estimated location is consistent with where the laser is irradiating. The developed method can be applied to characterize the laser-induced stray charges, which in turn can be used to design experiments to avoid or reduce the occurrence of stray charges.
Ion trap systems built upon microfabricated chips have emerged as a promising platform for quantum computing to achieve reproducible and scalable structures. However, photoinduced charging of materials in such chips can generate undesired stray electric fields that disrupt the quantum state of the ion, limiting high-fidelity quantum control essential for practical quantum computing. While crude understanding of the phenomena has been gained heuristically over the past years, explanations for the microscopic mechanism of photogenerated charge carrier dynamics remains largely elusive. Here we present a photoinduced charging model for semiconductors, whose verification is enabled by a systematic interaction between trapped ions and photoinduced stray fields from exposed silicon surfaces in our chip. We use motion-sensitive qubit transitions to directly characterize the stray field and analyze its effect on the quantum dynamics of the trapped ion. In contrast to incoherent errors arising from the thermal motion of the ion, coherent errors are induced by the stray field, whose effect is significantly imprinted during the quantum control of the ion. These errors are investigated in depth, and methods to mitigate them are discussed. Finally, we extend the implications of our study to other photoinduced charging mechanisms prevalent in ion traps and discuss considerations in fabrication to reduce semiconductor charging.
Electrical charging of metal surfaces due to photoelectric generation of carriers is of concern in trapped ion quantum computation systems, due to the high sensitivity of the ions’ motional quantum states to deformation of the trapping potential. The charging induced by typical laser frequencies involved in Doppler cooling and quantum control is studied here, with microfabricated surface-electrode traps made of aluminum, copper, and gold, operated at 6 K with a single Sr+ ion trapped 100 μm above the trap surface. The lasers used are at 370, 405, 460, and 674 nm, and the typical photon flux at the trap is 1014 photons/cm2/sec. Charging is detected by monitoring the ion’s micromotion signal, which is related to the number of charges created on the trap. A wavelength and material dependence of the charging behavior is observed: Lasers at lower wavelengths cause more charging, and aluminum exhibits more charging than copper or gold. We describe the charging dynamic based on a rate-equation approach.