Recent Highlighted Publications
Jacob R. Taylor and Sankar Das Sarma
Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physical devices, particularly quantum devices, there is potential to learn about disordered landscapes and subsequently tune devices based on those insights. In this work, we introduce a novel methodology that employs machine learning, specifically convolutional neural networks (CNNs), to discern the disorder landscape in the parameters of the disordered extended Hubbard model underlying the semiconductor quantum dot spin qubit architectures. This technique takes advantage of experimentally obtainable charge stability diagrams from neighboring quantum dot pairs, enabling the CNN to accurately identify disorder in each parameter of the extended Hubbard model. Remarkably,our CNN can process site-specific disorder in Hubbard parameters, including variations in hopping constants, on-site potentials (gate voltages), and both intra-site and inter-site Coulomb terms. This advancement facilitates the prediction of spatially dependent disorder across all parameters simultaneously with high accuracy (R^2 > 0.994) and fewer parameter constraints, marking a significant improvement over previous methods that were focused only on analyzing on-site potentials at low coupling. Furthermore, our approach allows for the tuning of five or more quantum dots at a time,effectively addressing the often-overlooked issue of crosstalk. Not only does our method streamline the tuning process, potentially enabling fully automated adjustments, but it also introduces a “no-trust” verification method to rigorously validate the neural network’s predictions. Ultimately, this work aims to lay the groundwork for generalizing our method to tackle a broad spectrum of physical problems. In particular, our work establishes that the microscopic parameters controlling the semiconductor quantum dot quantum computing platforms can be uniquely determined in an automated manner by using a CNN based machine learning technique using only the measured charge stability diagrams as the input.
Machine learning Majorana nanowire disorder landscape
Jacob R. Taylor, Jay D. Sau, Sankar Das Sarma
We develop a practical machine learning approach to determine the disorder landscape of Majorana nanowires by using training of the conductance matrix and inverting the conductance data in order to obtain the disorder details in the system. The inversion carried out through machine learning using different disorder parametrizations turns out to be unique in the sense that any input tunnel conductance as a function of chemical potential and Zeeman energy can indeed be inverted to provide the correct disorder landscape. Our work opens up a qualitatively new direction of directly determining the topological invariant and the Majorana wave-function structure corresponding to a transport profile of a device using simulations that quantitatively match the specific conductance profile. In addition, this also opens up the possibility for optimizing Majorana systems by figuring out the (generally unknown) underlying disorder only through the conductance data. An accurate estimate of the applicable spin-orbit coupling in the system can also be obtained within the same scheme.
Assessing quantum dot SWAP gate fidelity using tensor network methods
Jacob R. Taylor, Nathan L. Foulk, and Sankar Das Sarma
In this work, we employ advanced tensor network numerical methods to explore the fidelity of repeated SWAP operations on systems comprising 20–100 quantum dot spin qubits affected by valley leakage and electrostatic crosstalk. We find that the fidelity of SWAP gates is largely unaffected by Zeeman splitting and valley splitting, except when these parameters are in resonance. Interestingly, the fidelity remains independent of the overall valley phase for valley eigenstates, although minor corrections appear for generic valley states. Additionally, we analyze the fidelity scaling for long chains of qubits without valley effects, identifying crosstalk as the sole error source in these scenarios.
Quantum Control of Rydberg Atoms for Mesoscopic Quantum State and Circuit Preparation
Valerio Crescimanna, Jacob Taylor, Aaron Z. Goldberg, and Khabat Heshami
Individually trapped Rydberg atoms show significant promise as a platform for scalable quantum simulation and for the development of programmable quantum computers. In particular, the Rydberg-blockade effect can be used to facilitate both fast qubit-qubit interactions and long coherence times via low-lying electronic states encoding the physical qubits. To bring existing Rydberg-atom-based platforms a step closer to fault-tolerant quantum computation, we demonstrate high-fidelity state and circuit preparation in a system of five atoms. We specifically show that quantum control can be used to reliably generate fully connected cluster states and to simulate the error-correction encoding circuit based on the “Perfect Quantum Error Correcting Code” by Laflamme et al. [Phys. Rev. Lett. 77, 198 (1996)]. Our results make these ideas and their implementation directly accessible to experiments and demonstrate a promising level of noise tolerance with respect to experimental errors. With this approach, we motivate the application of quantum control in small subsystems in combination with the standard gate-based quantum circuits for direct and high-fidelity implementation of few-qubit modules.
Waveguide-QED platform for synthetic quantum matter
Ying Dong, J. Taylor, Youn Seok Lee, H. R. Kong, and K. S. Choi
In quantum information science, the development of complex quantum many-body systems has been advanced by the hybrid nanophotonic system using cold atoms. This system is pivotal for building long-range spin models through specific modal geometries and group dispersion. Challenges arise from the limitations set by the photonic bath, which restricts the types of local Hamiltonians and confines spatial dimensions to the dielectric media. Nonetheless, significant driven-dissipative quantum forces at the nanoscopic scale offer new ways to control atomic states through atom-field interactions. We present a quantum optics toolbox for creating universal quantum materials using individual atoms near one-dimensional photonic crystal waveguides, enabling the synthesis of analog quantum materials. These materials use phononic superfluids to mediate 2-local Hamiltonian graphs. Our approach extends to developing dynamical gauge fields and exploring emergent lattice models with strong SU(n) interactions and a minimal model of the SU(n) Wess-Zumino-Witten quantum field theory. We also introduce a diagnostic tool for mapping the conformal data of this field theory to the correlators of photons in the guided mode.