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Kavyashree Ranawat

Publications and source records attributed to Kavyashree Ranawat.

4 recordsLinked to original sources

Sympathetic Cooling in Trapped Ions with Spectral Selectivity via the Zeeman Shift

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.

quant-ph

Design and Characterization of Compact Acousto-Optic-Deflector Individual Addressing System for Trapped-Ion Quantum Computing

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.

quant-ph

Tailored Quantum Device Calibration with Statistical Model Checking

Quantum devices require precisely calibrated analog signals, a process that is complex and time-consuming. Many calibration strategies exist, and all require careful analysis and tuning to optimize system availability. To enable rigorous statistical evaluation of quantum calibration procedures, we leverage statistical model checking (SMC), a technique used in fields that require statistical guarantees. SMC allows for probabilistic evaluation of properties of interest, such as a certain parameter's time to failure. We extend the SMC for Processor Analysis (SPA) framework, which uses SMC for evaluation of classical systems, to create SPA for Quantum calibration (SPAQ) enabling simplified tuning and analysis of quantum system calibration. We focus on a directed acyclic graph-based calibration optimization scheme and demonstrate how to craft properties of interest for its analysis. We show how to use SPAQ to find lower bounds of time to failure information, hidden node dependencies, and parameter threshold values and use that information to improve simulated quantum system availability through calibration scheme adjustments.

quant-ph

Artificial intelligence prediction of stock prices using social media

The primary objective of this work is to develop a Neural Network based on LSTM to predict stock market movements using tweets. Word embeddings, used in the LSTM network, are initialised using Stanford's GloVe embeddings, pretrained specifically on 2 billion tweets. To overcome the limited size of the dataset, an augmentation strategy is proposed to split each input sequence into 150 subsets. To achieve further improvements in the original configuration, hyperparameter optimisation is performed. The effects of variation in hyperparameters such as dropout rate, batch size, and LSTM hidden state output size are assessed individually. Furthermore, an exhaustive set of parameter combinations is examined to determine the optimal model configuration. The best performance on the validation dataset is achieved by hyperparameter combination 0.4,8,100 for the dropout, batch size, and hidden units respectively. The final testing accuracy of the model is 76.14%.

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