Manas Mukherjee

Quantum computational advantage is generally attributed to coherent interference and other non-classical resources, yet their respective roles remain difficult to disentangle in experimental platforms where multipartite entanglement is inherently present. High-dimensional quantum systems provide an attractive route for investigating these resources while simultaneously reducing hardware overhead for quantum information processing. Here we realize a programmable four-dimensional optical qudit encoded in a single trapped $^{138}\mathrm{Ba}^{+}$ ion and demonstrate universal coherent control through phase-programmable optical rotations. Using this platform, we implement an entanglement-free realization of Grover's quantum search algorithm, achieving target-state identification probabilities of up to $94.5\pm2.0\%$. Within the same processor, we further demonstrate state-dependent quantum contextuality through a Clauser--Horne--Shimony--Holt (CHSH)-type noncontextuality inequality, obtaining a maximum violation of $S = 2.816 \pm 0.082$, in close agreement with the Tsirelson bound. By integrating programmable quantum computation and contextuality measurements within a single multilevel trapped-ion platform, our work establishes a versatile architecture for investigating the relationship between coherent interference and contextuality in quantum information processing and provides a scalable route toward high-dimensional quantum technologies.

Laser phase noise is one of the limiting factors that dictate gate fidelities and coherence times in trapped-ion quantum systems. Previous studies have reported that when the Rabi frequency of an ion qubit is close to the servo-bump phase-noise frequency, the driving laser limits the fidelity and coherence time. This issue has typically been mitigated by choosing a Rabi frequency outside the servo-bump region. However, this constrains the usable range of gate speeds and can limit the achievable fidelity. To address this issue, we developed an active phase-noise stabilization system for a barium-ion optical-qubit laser at 1762 nm, employing a fiber electro-optic modulator (EOM) with an electrical feedforward servo. Our results demonstrate that this setup, based on the Pound-Drever-Hall (PDH) feedforward method, can suppress servo-bump phase noise by 15 dB near the bump peak frequency in our locking system. The laser phase noise is analyzed using delayed self-heterodyne interferometry (DSHI). We further tested the stabilized laser on an optical qubit and observed a clear improvement in coherence time based on the measured amplitude decay of Rabi oscillations, even when the Rabi frequency lies within the servo-bump bandwidth. This technique can be readily adapted to other optical qubits with minimal modifications.

Classification is a fundamental task in machine learning, typically performed using classical models. Quantum machine learning (QML), however, offers distinct advantages, such as enhanced representational power through high-dimensional Hilbert spaces and energy-efficient reversible gate operations. Despite these theoretical benefits, the robustness of QML classifiers against adversarial attacks and inherent quantum noise remains largely under-explored. In this work, we implement a data re-uploading-based quantum classifier on an ion-trap quantum processor using a single qubit to assess its resilience under realistic conditions. We introduce a novel convolutional quantum classifier architecture leveraging data re-uploading and demonstrate its superior robustness on the MNIST dataset. Additionally, we quantify the effects of polarization noise in a realistic setting, where both bit and phase noises are present, further validating the classifier's robustness. Our findings provide insights into the practical security and reliability of quantum classifiers, bridging the gap between theoretical potential and real-world deployment.

Advancements in classical computing have significantly enhanced machine learning applications, yet inherent limitations persist in terms of energy, resource and speed. Quantum machine learning algorithms offer a promising avenue to overcome these limitations but poses its own hurdles. This experimental study explores the limits of training a real experimental quantum classical hybrid system using supervised training protocols, on an ion trap platform. Challenges associated with ion trap-coupled classical processors are addressed, highlighting the $robustness$ of the genetic algorithm as a classical optimizer in navigating the noisy channels of NISQ-devices and the complex optimization landscape inherent in binary classification problems with many local minima. We intricately discuss why gradient-based optimizers may not be suitable in the NISQ era through a thorough analysis. These findings contribute insights into the performance of quantum-classical hybrid systems, emphasizing the significance of efficient training strategies and hardware considerations for practical quantum machine learning applications. This work not only advances the understanding of hybrid quantum-classical systems but also underscores the potential impact on real-world challenges through the convergence of quantum and classical computing paradigms operating without the aid of classical simulators.

The linewidth of a laser plays a pivotal role in ensuring the high fidelity of ion trap quantum processors and optical clocks. As quantum computing endeavors scale up in qubit number, the demand for higher laser power with ultra-narrow linewidth becomes imperative, and leveraging fiber amplifiers emerges as a promising approach to meet these requirements. This study explores the effectiveness of Thulium-doped fiber amplifiers (TDFAs) as a viable solution for addressing optical qubit transitions in trapped barium ion qubits. We demonstrate that by performing high-fidelity gates on the qubit while introducing minimal intensity noise, TDFAs do not significantly broaden the linewidth of the seed lasers. We employed a Voigt fitting scheme in conjunction with a delayed self-heterodyne method to accurately measure the linewidth independently, corroborating our findings through quadrupole spectroscopy with trapped barium ions. Our results show linewidth values of $160 \pm 15$ Hz and $156 \pm 16$ Hz, respectively, using these two methods, underscoring the reliability of our measurement techniques. The slight variation between the two methods can be attributed to factors such as amplified spontaneous emission in the TDFA or the influence of 1/f noise within the heterodyne setup delay line. These contribute to advancing our understanding of laser linewidth control in the context of ion trap quantum computing as well as stretching the availability of narrow linewidth, high-power tunable lasers beyond the C-band.

Quantum computers can provide solutions to classically intractable problems under specific and adequate conditions. However, current devices have only limited computational resources, and an effort is made to develop useful quantum algorithms under these circumstances. This work experimentally demonstrates that a single-qubit device can host a universal classifier. The quantum processor used in this work is based on ion traps, providing highly accurate control on small systems. The algorithm chosen is the re-uploading scheme, which can address general learning tasks. Ion traps suit the needs of accurate control required by re-uploading. In the experiment here presented, a set of non-trivial classification tasks are successfully carried. The training procedure is performed in two steps combining simulation and experiment. Final results are benchmarked against exact simulations of the same method and also classical algorithms, showing a competitive performance of the ion-trap quantum classifier. This work constitutes the first experimental implementation of a classification algorithm based on the re-uploading scheme.

Trapped ions are among the leading candidates for quantum computing technologies. Interfacing ion qubits in separate traps and interfacing ion qubits with superconducting qubits are two of the many challenges to scale up quantum computers. One approach to overcome both problems is to use a conducting wire to mediate the Coulomb interaction between ions in different traps, or between ions and superconducting qubits. To this end, a trapped charged particle inducing charge on a conductor has long been modeled as a system of equivalent lumped element electronic components. Careful consideration reveals two assumptions in the derivation of this model which are unjustified in many situations of interest. We identify these assumptions and explain their implications. In addition, we introduce an improved way to use linear relationships to describe the interaction of trapped ions with nearby conductors. The new method is based on realistic assumptions and reproduces results from other works that are not based on the circuit element model. It is targeted for trouble-shooting experimental designs and allows experiments to test and compare the accuracy of different theoretical models.

Magnetically induced coherent population trapping has been studied in a single trapped laser cooled ion. The magnetic field dependent narrow spectral feature is found to be an useful tool in determining the null point of magnetic field at the ion position. In particular, we use a double lambda scheme that allows us to measure the null magnetic field point limited by the detector shot noise. We analyzed the system theoretically and found certain long lived bright states as the dark state is generated under steady state condition.

Photon correlations are investigated for a single laser-excited ion trapped in front of a mirror. Varying the relative distance between the ion and the mirror, photon correlation statistics can be tuned smoothly from an antibunching minimum to a bunching-like maximum. Our analysis concerns the non-Markovian regime of the ion-mirror interaction and reveals the field establishment in a half-cavity interferometer.