Jungsang Kim

High-fidelity quantum logic operations in trapped ions often require the ions' collective motion to be cooled to near the ground state. Since cooling the ions' motion typically involves dissipative processes such as spontaneous photon scattering, sympathetic cooling is used on select coolant ions between gate sequences to cool the ion chain without affecting the data qubits. Common implementations for coolant ions include different atomic species, different isotopes of the same species or individually addressable ions. Each of these approaches have challenges associated with them, which include increased hardware complexity, reduced efficiency of radial mode cooling and re-ordering events which add additional experimental overhead. We demonstrate a sympathetic cooling scheme leveraging internal metastable atomic levels accessible via a narrow quadrupole transition, utilizing the natural Zeeman shift and individually addressed Raman transitions, to achieve isolation of the non-coolant or ``data ions" from coolant ions. We demonstrate modest decoherence of the data ions due to cooling, while preserving the coherence requirements for high-fidelity gate operations.

We present a compact design for a beam-steering system based on acousto-optic-deflectors (AODs) used as an individual addressing system for trapped-ion quantum computing. The design targets to minimize the optomechanical degrees of freedom and the optical beam paths to improve optical stability, and we successfully implemented a solution with a compact footprint of less than 1 square foot. The system characterization results show that we achieve clean Gaussian beams at 355nm wavelength with a beam steering range of $\sim$50 times the beam diameter, and an intensity crosstalk of $< 9 \times 10^{-4}$ at all neighboring ions in a five-ion chain. Based on these capabilities, we experimentally demonstrate individual addressing of a 30-ion chain. We estimate the beam switching time of the AOD to be $\sim$240 ns. The compact system design is expected to provide high optical stability, providing the potential for high-fidelity trapped-ion quantum computing with long ion chains.

Quantum machine learning (QML) has attracted growing interest with the rapid parallel advances in large-scale classical machine learning and quantum technologies. Similar to classical machine learning, QML models also face challenges arising from the scarcity of labeled data, particularly as their scale and complexity increase. Here, we introduce self-supervised pretraining of quantum representations that reduces reliance on labeled data by learning invariances from unlabeled examples. We implement this paradigm on a programmable trapped-ion quantum computer, encoding images as quantum states. In situ contrastive pretraining on hardware yields a representation that, when fine-tuned, classifies image families with higher mean test accuracy and lower run-to-run variability than models trained from random initialization. Performance improvement is especially significant in regimes with limited labeled training data. We show that the learned invariances generalize beyond the pretraining image samples. Unlike prior work, our pipeline derives similarity from measured quantum overlaps and executes all training and classification stages on hardware. These results establish a label-efficient route to quantum representation learning, with direct relevance to quantum-native datasets and a clear path to larger classical inputs.

Quantum networking can be realized by distributing pairs of entangled qubits between remote quantum processing nodes. Devoted communication qubits within each node can naturally interface with photons which bus quantum information between nodes. With the introduction of CQED to enhance interactions between communication qubits and photons, advanced protocols capable of achieving high entanglement distribution rates with high fidelity become feasible. In this paper, we consider two such protocols based on trapped ion communication qubits strongly coupled to small optical cavities. We study the rate and fidelity performance of these protocols as a function of critical device parameters and the photonic degree of freedom used to carry the quantum information. We compare the performance of these protocols with the traditional two-photon interference scheme, subjecting all protocols to the same experimentally relevant constraints. We find that adoption of the strong-coupling protocols could provide substantial distribution rate improvements of $30-75\%$ while maintaining the high-fidelities $\mathcal{F}\gtrsim99\%$ of the traditional scheme.

Accurate calibration of control parameters in quantum gates is crucial for high-fidelity operations, yet it represents a significant time and resource challenge, necessitating periods of downtime for quantum computers. Robust Phase Estimation (RPE) has emerged as a practical and effective calibration technique aimed at tackling this challenge. It combines a provably efficient number of control pulses with a classical post-processing algorithm to estimate the phase accumulated by a quantum gate. We introduce Bayesian Robust Phase Estimation (BRPE), an innovative approach that integrates Bayesian parameter estimation into the classical post-processing phase to reduce the sampling overhead. Our numerical analysis shows that BRPE markedly reduces phase estimation errors, requiring approximately $50\%$ fewer samples than standard RPE. Specifically, in an ideal, noise-free setting, it achieves up to a $96\%$ reduction in average absolute estimation error for a fixed sample cost of $88$ shots when compared to RPE. Under a depolarizing noise model, it attains up to a $47\%$ reduction for a fixed cost of $176$ shots. Additionally, we adapt BRPE for Ramsey spectroscopy applications and successfully implement it experimentally in a trapped ion system.

The spin-boson model, involving spins interacting with a bath of quantum harmonic oscillators, is a widely used representation of open quantum systems. Trapped ions present a natural platform for simulating the quantum dynamics of such models, thanks to the presence of both high quality internal qubit states and the motional modes of the ions that can simulate the relevant quantum degrees of freedom. In our work, we extend the previous body of work that focused on coherent coupling of the spins and bosons to perform quantum simulations with structured dissipative baths using the motional states of trapped ions. We demonstrate the capability for adjusting the bath's temperature and continuous spectral density by adding randomness to fully programmable control parameters. Subsequently, we simulate the dynamics of various spin-boson models with noise spectral densities constructed from coupling to several dissipative harmonic oscillator modes. The experimental outcomes closely align with theoretical predictions, indicating successful simulation of open quantum systems using a trapped-ion system.

We present the design and characterization of individual addressing optics based on a multi-channel acousto-optic modulator (AOM) for trapped ytterbium-171 ions. The design parameters of the individual addressing system were determined based on the tradeoff between the expected crosstalk and the required numerical aperture of the projection objective lens. The target beam diameter and separation were 1.90 $μ$m and 4.28 $μ$m, respectively. The individual beams shaped by the projection optics were characterized by an imaging sensor and a field probe ion. The resulting effective beam diameters and separations were approximately 2.34--2.36 $μ$m and 4.31 $μ$m, respectively, owing to residual aberration.

Electron transfer within and between molecules is crucial in chemistry, biochemistry, and energy science. This study describes a quantum simulation method that explores the influence of light polarization on the electron transfer between two molecules. By implementing precise and coherent control among the quantum states of trapped atomic ions, we can induce quantum dynamics that mimic the electron transfer dynamics in molecules. We use $3$-level systems (qutrits), rather than traditional two-level systems (qubits) to enhance the simulation efficiency and realize high-fidelity simulations of electron transfer dynamics. We treat the quantum interference between the electron coupling pathways from a donor with two degenerate excited states to an acceptor and analyze the transfer efficiency. We also examine the potential error sources that enter the quantum simulations. The trapped ion systems have favorable scalings with system size compared to those of classical computers, promising access to electron-transfer simulations of increasing richness.

The universality theorem in quantum computing states that any quantum computational task can be decomposed into a finite set of logic gates operating on one and two qubits. However, the process of such decomposition is generally inefficient, often leading to exponentially many gates to realize an arbitrary computational task. Practical processor designs benefit greatly from availability of multi-qubit gates that operate on more than two qubits to implement the desired circuit. In 1995, Cirac and Zoller proposed a method to realize native multi-qubit controlled-$Z$ gates in trapped ion systems, which has a stringent requirement on ground-state cooling of the motional modes utilized by the gate. An alternative approach, the Mølmer-Sørensen gate, is robust against residual motional excitation and has been a foundation for many high-fidelity gate demonstrations. This gate does not scale well beyond two qubits, incurring additional overhead when used to construct many target algorithms. Here, we take advantage of novel performance benefits of long ion chains to realize fully programmable and scalable high-fidelity Cirac-Zoller gates.

Variational quantum eigensolvers (VQE) are among the most promising approaches for solving electronic structure problems on near-term quantum computers. A critical challenge for VQE in practice is that one needs to strike a balance between the expressivity of the VQE ansatz versus the number of quantum gates required to implement the ansatz, given the reality of noisy quantum operations on near-term quantum computers. In this work, we consider an orbital-optimized pair-correlated approximation to the unitary coupled cluster with singles and doubles (uCCSD) ansatz and report a highly efficient quantum circuit implementation for trapped-ion architectures. We show that orbital optimization can recover significant additional electron correlation energy without sacrificing efficiency through measurements of low-order reduced density matrices (RDMs). In the dissociation of small molecules, the method gives qualitatively accurate predictions in the strongly-correlated regime when running on noise-free quantum simulators. On IonQ's Harmony and Aria trapped-ion quantum computers, we run end-to-end VQE algorithms with up to 12 qubits and 72 variational parameters - the largest full VQE simulation with a correlated wave function on quantum hardware. We find that even without error mitigation techniques, the predicted relative energies across different molecular geometries are in excellent agreement with noise-free simulators.

Conical intersections are common in molecular physics and photochemistry, and are often invoked to explain observed reaction products. A conical intersection can occur when an excited electronic potential energy surface intersects with the ground electronic potential energy surface in the coordinate space of the nuclear positions. Theory predicts that the conical intersection will result in a geometric phase for a wavepacket on the ground potential energy surface. Although conical intersections have been observed experimentally, the geometric phase has not been observed in a molecular system. Here we use a trapped atomic ion system to perform a quantum simulation of a conical intersection. The internal state of a trapped atomic ion serves as the electronic state and the motion of the atomic nuclei are encoded into the normal modes of motion of the ions. The simulated electronic potential is constructed by applying state-dependent forces to the ion with a near-resonant laser. We experimentally observe the geometric phase on the ground-state surface using adiabatic state preparation followed by motional state measurement. Our experiment shows the advantage of combining spin and motion degrees of freedom in a quantum simulator.

Real-time control software and hardware is essential for operating quantum computers. In particular, the software plays a crucial role in bridging the gap between quantum programs and the quantum system. Unfortunately, current control software is often optimized for a specific system at the cost of flexibility and portability. We propose a systematic design strategy for modular real-time quantum control software and demonstrate that modular control software can reduce the execution time overhead of kernels by 63.3% on average while not increasing the binary size. Our analysis shows that modular control software for two distinctly different systems can share between 49.8% and 91.0% of covered code statements. To demonstrate the modularity and portability of our software architecture, we run a portable randomized benchmarking experiment on two different ion-trap quantum systems.

In trapped-ion quantum computers, two-qubit entangling gates are generated by applying spin-dependent force which uses phonons to mediate interaction between the internal states of the ions. To maintain high-fidelity two-qubit gates under fluctuating experimental parameters, robust pulse-design methods are applied to remove the residual spin-motion entanglement in the presence of motional mode frequency drifts. Here we propose an improved pulse-design method that also guarantees the robustness of the two-qubit rotation angle against uniform mode frequency drifts by combining pulses with opposite sensitivity of the angle to mode frequency drifts. We experimentally measure the performance of the designed gates and see an improvement on both gate fidelity and gate performance under uniform mode frequency offsets.

High-fidelity two-qubit gates in quantum computers are often hampered by fluctuating experimental parameters. The effects of time-varying parameter fluctuations lead to coherent noise on the qubits, which can be suppressed by designing control signals with appropriate filter functions. Here, we develop filter functions for Mølmer-Sørensen gates of trapped-ion quantum computers that accurately predict the change in gate error due to small parameter fluctuations at any frequency. We then design the filter functions of frequency-modulated laser pulses, and compare this method with pulses that are robust to static offsets of the motional-mode frequencies. Experimentally, we measure the noise spectrum of the motional modes and use it for designing the filter functions, which improves the gate fidelity from 99.23(7)% to 99.55(7)% in a five-ion chain.

Crosstalk between target and neighboring spectator qubits due to spillover of control signals represents a major error source limiting the fidelity of two-qubit entangling gates in quantum computers. We show that in our laser-driven trapped-ion system coherent crosstalk error can be modelled as residual $X\hatσ_φ$ interaction and can be actively cancelled by single-qubit echoing pulses. We propose and demonstrate a crosstalk suppression scheme that eliminates all first-order crosstalk, yet only requires local control by driving rotations solely on the target qubits. We report a two-qubit Bell state fidelity of $99.52(6) \%$ with the echoing pulses applied after collective gates and $99.37(5) \%$ with the echoing pulses applied to each gate in a 5-ion chain. This scheme is widely applicable to other platforms with analogous interaction Hamiltonians.

Trapped atomic ions are a versatile platform for studying interactions between spins and bosons by coupling the internal states of the ions to their motion. Measurement of complex motional states with multiple modes is challenging, because all motional state populations can only be measured indirectly through the spin state of ions. Here we present a general method to determine the Fock state distributions and to reconstruct the density matrix of an arbitrary multi-mode motional state. We experimentally verify the method using different entangled states of multiple radial modes in a 5-ion chain. This method can be extended to any system with Jaynes-Cummings type interactions.

Modeling joint probability distributions is an important task in a wide variety of fields. One popular technique for this employs a family of multivariate distributions with uniform marginals called copulas. While the theory of modeling joint distributions via copulas is well understood, it gets practically challenging to accurately model real data with many variables. In this work, we design quantum machine learning algorithms to model copulas. We show that any copula can be naturally mapped to a multipartite maximally entangled state. A variational ansatz we christen as a `qopula' creates arbitrary correlations between variables while maintaining the copula structure starting from a set of Bell pairs for two variables, or GHZ states for multiple variables. As an application, we train a Quantum Generative Adversarial Network (QGAN) and a Quantum Circuit Born Machine (QCBM) using this variational ansatz to generate samples from joint distributions of two variables for historical data from the stock market. We demonstrate our generative learning algorithms on trapped ion quantum computers from IonQ for up to 8 qubits and show that our results outperform those obtained through equivalent classical generative learning. Further, we present theoretical arguments for exponential advantage in our model's expressivity over classical models based on communication and computational complexity arguments.

Cryogenic environments benefit ion trapping experiments by offering lower motional heating rates, collision energies, and an ultra-high vacuum (UHV) environment for maintaining long ion chains for extended periods of time. Mechanical vibrations caused by compressors in closed-cycle cryostats can introduce relative motion between the ion and the wavefronts of lasers used to manipulate the ions. Here, we present a novel ion trapping system where a commercial low-vibration closed-cycle cryostat is used in a custom monolithic enclosure. We measure mechanical vibrations of the sample stage using an optical interferometer, and observe a root-mean-square relative displacement of 2.4 nm and a peak-to-peak displacement of 17 nm between free-space beams and the trapping location. We packaged a surface ion trap in a cryo-package assembly that enables easy handling, while creating a UHV environment for the ions. The trap cryo-package contains activated carbon getter material for enhanced sorption pumping near the trapping location, and source material for ablation loading. Using $^{171}$Yb$^{+}$ as our ion we estimate the operating pressure of the trap as a function of package temperature using phase transitions of zig-zag ion chains as a probe. We measured the radial mode heating rate of a single ion to be 13 quanta/s on average. The Ramsey coherence measurements yield 330 ms coherence time for counter-propagating Raman carrier transitions using a 355 nm mode-locked pulse laser, demonstrating the high optical stability.

Two-qubit gates in trapped-ion quantum computers are generated by applying spin-dependent forces that temporarily entangle the internal state of the ion with its motion. Laser pulses are carefully designed to generate a maximally entangling gate between the ions while minimizing any residual entanglement between the motion and the ion. The quality of the gates suffers when the actual experimental parameters differ from the ideal case. Here, we improve the robustness of frequency-modulated Mølmer-Sørensen gates to motional mode-frequency offsets by optimizing the average performance over a range of systematic errors using batch optimization. We then compare this method with frequency-modulated gates optimized for ideal parameters that include an analytic robustness condition. Numerical simulations show good performance up to 12 ions, and the method is experimentally demonstrated on a two-ion chain.

Coherent gate errors are a concern in many proposed quantum computing architectures. These errors can be effectively handled through composite pulse sequences for single-qubit gates, however, such techniques are less feasible for entangling operations. In this work, we benchmark our coherent errors by comparing the actual performance of composite single-qubit gates to the predicted performance based on characterization of individual single-qubit rotations. We then propose a compilation technique, which we refer to as hidden inverses, that creates circuits robust to these coherent errors. We present experimental data showing that these circuits suppress both overrotation and phase misalignment errors in our trapped ion system.

Quantum computers have the potential to advance material design and drug discovery by performing costly electronic structure calculations. A critical aspect of this application requires optimizing the limited resources of the quantum hardware. Here, we experimentally demonstrate an end-to-end pipeline that focuses on minimizing quantum resources while maintaining accuracy. Using density matrix embedding theory as a problem decomposition technique, and an ion-trap quantum computer, we simulate a ring of 10 hydrogen atoms without freezing any electrons. The originally 20-qubit system is decomposed into 10 two-qubit problems, making it amenable to currently available hardware. Combining this decomposition with a qubit coupled cluster circuit ansatz, circuit optimization, and density matrix purification, we accurately reproduce the potential energy curve in agreement with the full configuration interaction energy in the minimal basis set. Our experimental results are an early demonstration of the potential for problem decomposition to accurately simulate large molecules on quantum hardware.

Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.

We present the design and vacuum performance of a compact room-temperature trapped ion system for quantum computing, consisting of a ultra-high vacuum (UHV) package, a micro-fabricated surface trap and a small form-factor ion pump. The system is designed to maximize mechanical stability and robustness by minimizing the system size and weight. The internal volume of the UHV package is only 2 cm$^3$, a significant reduction in comparison with conventional vacuum chambers used in trapped ion experiments. We demonstrate trapping of $^{174}$Yb$^+$ ions in this system and characterize the vacuum level in the UHV package by monitoring both the rates of ion hopping in a double-well potential and ion chain reordering events. The calculated pressure in this vacuum package is about 1.5e-11 Torr, which is sufficient for the majority of current trapped ion experiments.

In a large scale trapped atomic ion quantum computer, high-fidelity two-qubit gates need to be extended over all qubits with individual control. We realize and characterize high-fidelity two-qubit gates in a system with up to 4 ions using radial modes. The ions are individually addressed by two tightly focused beams steered using micro-electromechanical system (MEMS) mirrors. We deduce a gate fidelity of 99.49(7)% in a two-ion chain and 99.30(6)% in a four-ion chain by applying a sequence of up to 21 two-qubit gates and measuring the final state fidelity. We characterize the residual errors and discuss methods to further improve the gate fidelity towards values that are compatible with fault-tolerant quantum computation.

Quantum computing leverages the quantum resources of superposition and entanglement to efficiently solve computational problems considered intractable for classical computers. Examples include calculating molecular and nuclear structure, simulating strongly-interacting electron systems, and modeling aspects of material function. While substantial theoretical advances have been made in mapping these problems to quantum algorithms, there remains a large gap between the resource requirements for solving such problems and the capabilities of currently available quantum hardware. Bridging this gap will require a co-design approach, where the expression of algorithms is developed in conjunction with the hardware itself to optimize execution. Here, we describe a scalable co-design framework for solving chemistry problems on a trapped ion quantum computer, and apply it to compute the ground-state energy of the water molecule. The robust operation of the trapped ion quantum computer yields energy estimates with errors approaching the chemical accuracy, which is the target threshold necessary for predicting the rates of chemical reaction dynamics.

Qubits used in quantum computing tend to suffer from errors, either from the qubit interacting with the environment, or from imperfect control when quantum logic gates are applied. Fault-tolerant construction based on quantum error correcting codes (QECC) can be used to recover from such errors. Effective implementation of QECC requires a high fidelity readout of the ancilla qubits from which the error syndrome can be determined, without affecting the data qubits in which relevant quantum information is stored for processing. Here, we present a detection scheme for \yb trapped ion qubits, where we use superconducting nanowire single photon detectors and utilize photon time-of-arrival statistics to improve the fidelity and speed. Qubit shuttling allows for creating a separate detection region where an ancilla qubit can be measured without disrupting a data qubit. We achieve an average qubit state detection time of 11$μ$s with a fidelity of $99.931(6)\%$. The error due to the detection crosstalk, defined as the probability that the coherence of the data qubit is lost due to the process of detecting an ancilla qubit, is reduced to $\sim2\times10^{-5}$ by creating a separation of 370$μ$m between them.

We report a demonstration of a surface ion trap fabricated directly on a highly reflective mirror surface, which includes a secondary set of radio frequency (RF) electrodes allowing for translation of the quadrupole RF null location. We introduce a position-dependent photon scattering rate for a $^{174}$Yb$^+$ ion in the direction perpendicular to the trap surface using a standing wave of retroreflected light off the mirror surface directly below the trap. Using this setup, we demonstrate the capability of fine-tuning the RF trap location with nanometer scale precision and characterize the charging effects of the dielectric mirror surface upon exposure to ultra-violet light.

The optimal design of a fault-tolerant quantum computer involves finding an appropriate balance between the burden of large-scale integration of noisy components and the load of improving the reliability of hardware technology. This balance can be evaluated by quantitatively modeling the execution of quantum logic operations on a realistic quantum hardware containing limited computational resources. In this work, we report a complete performance simulation software tool capable of (1) searching the hardware design space by varying resource architecture and technology parameters, (2) synthesizing and scheduling fault-tolerant quantum algorithm within the hardware constraints, (3) quantifying the performance metrics such as the execution time and the failure probability of the algorithm, and (4) analyzing the breakdown of these metrics to highlight the performance bottlenecks and visualizing resource utilization to evaluate the adequacy of the chosen design. Using this tool we investigate a vast design space for implementing key building blocks of Shor's algorithm to factor a 1,024-bit number with a baseline budget of 1.5 million qubits. We show that a trapped-ion quantum computer designed with twice as many qubits and one-tenth of the baseline infidelity of the communication channel can factor a 2,048-bit integer in less than five months.

The fidelity of laser-driven quantum logic operations on trapped ion qubits tend to be lower than microwave-driven logic operations due to the difficulty of stabilizing the driving fields at the ion location. Through stabilization of the driving optical fields and use of composite pulse sequences, we demonstrate high fidelity single-qubit gates for the hyperfine qubit of a $^{171}\text{Yb}^+$ ion trapped in a microfabricated surface electrode ion trap. Gate error is characterized using a randomized benchmarking protocol, and an average error per randomized Clifford group gate of $3.6(3)\times10^{-4}$ is measured. We also report experimental realization of palindromic pulse sequences that scale efficiently in sequence length.

Many of the challenges of scaling quantum computer hardware lie at the interface between the qubits and the classical control signals used to manipulate them. Modular ion trap quantum computer architectures address scalability by constructing individual quantum processors interconnected via a network of quantum communication channels. Successful operation of such quantum hardware requires a fully programmable classical control system capable of frequency stabilizing the continuous wave lasers necessary for trapping and cooling the ion qubits, stabilizing the optical frequency combs used to drive logic gate operations on the ion qubits, providing a large number of analog voltage sources to drive the trap electrodes, and a scheme for maintaining phase coherence among all the controllers that manipulate the qubits. In this work, we describe scalable solutions to these hardware development challenges.

We trap individual $^{171}$Yb$^+$ ions in a surface trap microfabricated on a silicon substrate, and demonstrate a complete set of high fidelity single qubit operations for the hyperfine qubit. Trapping times exceeding 20 minutes without laser cooling, and heating rates as low as 0.8(0.1) quanta/ms indicate stable trapping conditions in these microtraps. A coherence time of more than one second, high fidelity qubit state detection and single qubit rotations are demonstrated.

Fast and efficient detection of the qubit state in trapped ion quantum information processing is critical for implementing quantum error correction and performing fundamental tests such as a loophole-free Bell test. In this work we present a simple qubit state detection protocol for a $^{171}$Yb$^+$ hyperfine atomic qubit trapped in a microfabricated surface trap, enabled by high collection efficiency of the scattered photons and low background photon count rate. We demonstrate average detection times of 10.5, 28.1 and 99.8\,$\upmu$s, corresponding to state detection fidelities of 99%, 99.85(1)% and 99.915(7)%, respectively.

A collection of trapped atomic ions represents one of the most attractive platforms for the quantum simulation of interacting spin networks and quantum magnetism. Spin-dependent optical dipole forces applied to an ion crystal create long-range effective spin-spin interactions and allow the simulation of spin Hamiltonians that possess nontrivial phases and dynamics. Here we show how appropriate design of laser fields can provide for arbitrary multidimensional spin-spin interaction graphs even for the case of a linear spatial array of ions. This scheme uses currently existing trap technology and is scalable to levels where classical methods of simulation are intractable.

Interference and coincidence detection of two photons emitted by two remote ions can lead to an entangled state which is a critical resource for scalable quantum information processing. Currently, the success probabilities of experimental realizations of this protocol are mainly limited by low coupling efficiency of a photon emitted by an ion into a single mode fiber. Here, we consider two strategies to enhance the collection probability of a photon emitted from a trapped Yb ion, using analytic methods that can be easily applied to other types of ion or neutral atoms. Our analysis shows that we can achieve fiber coupling efficiency of over 30% with an optical cavity made of a flat fiber tip and a spherical mirror. We also investigate ways to increase the fiber coupling efficiency using high numerical aperture optics, and show that collection probability of over 15% is possible with proper control of aberration.

We present a model as well as experimental results for a surface electrode radio-frequency Paul trap that has a circular electrode geometry well-suited for trapping of single ions and two-dimensional planar ion crystals. The trap design is compatible with microfabrication and offers a simple method by which the height of the trapped ions above the surface may be changed \emph{in situ}. We demonstrate trapping of single and few Sr+ ions over an ion height range of 200-1000 microns for several hours under Doppler laser cooling, and use these to characterize the trap, finding good agreement with our model.

Recent experimental progress in quantum information processing with trapped ions have demonstrated most of the fundamental elements required to realize a scalable quantum computer. The next set of challenges lie in realization of a large number of qubits and the means to prepare, manipulate and measure them, leading to error-protected qubits and fault tolerant architectures. The integration of qubits necessarily require integrated optical approach as most of these operations involve interaction with photons. In this paper, we discuss integrated optics technologies and concrete optical designs needed for the physical realization of scalable quantum computer.

In order to provide scalability to quantum information processors utilizing trapped atoms or ions as quantum bits (qubits), the capability to address multiple individual qubits in a large array is needed. Micro-electromechanical systems (MEMS) technology can be used to create a flexible and scalable optical system to direct the necessary laser beams to multiple qubit locations. We developed beam steering optics using controllable MEMS mirrors that enable one laser beam to address multiple qubit locations in a 2 dimensional trap lattice. MEMS mirror settling times of 10 us were demonstrated which allow for fast access time between qubits.