SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Industry forecast of exponential growth in quantum computing power still predicts that transformative applications are decades away. System performance is limited by decoherence and requires 5-10x the number of physical qubits per effective qubit.
The objective of this work is to combine HEP simulation experience with HPC quantum systems simulations to provide an environment for executing large-scale experiments with advanced analysis capabilities for studying multi-qubit and multi-level reduced-noise, long coherence time quantum systems such as 3D Superconducting RF (SRF) cavities integrated with transmon qubits.
Given a (qubit) quantum system modeled by a Hamiltonian H, use a quantum computer to obtain time-evolved properties of the system. One way of solving this problem is by actually preparing the evolved quantum state (in some encoded fashion). The goal is to provide a sequence of elementary 1 and 2-qubit gates that would prepare such a state.
Semiconductor quantum dot devices can be challenging to configure into a regime where they are suitable for qubit operation. This challenge arises from variations in gate control of quantum dot electron occupation and tunnel coupling between quantum dots on a single device or across several devices. Furthermore, a single control gate usually has capacitive coupling to multiple quantum dots and tunnel barriers between dots. If the device operator, be it human or machine, has quantitative knowledge of how gates control the electrostatic and dynamic properties of multiqubit devices, the operator can more quickly and easily navigate the multidimensional gate space to find a qubit operating regime. We have developed and applied image analysis techniques to quantitatively detect where charge offsets from different quantum dots intersect, so called anticrossings. In this document we outline the details of our algorithm for detecting single anticrossings, which has been used to fine-tune the inter-dot tunnel rates for a three quantum dot system. Additionally, we show that our algorithm can detect multiple anticrossings in the same dataset, which can aid in the coarse tuning the electron occupation of multiple quantum dots. We also include an application of cross correlation to the imaging of magnetic fields using nitrogen vacancies.
This summer project started out to quantify the error associated with an approximate model of a superconducting circuit, where two qubits are dispersively coupled through a bus resonator. Understanding this error helps us decide if the approximate model is sufficient for the purpose of optimal control. We find out that the error of the approximate model covers up a nontrivial problem of the underlying circuit, which limits the fidelity of two-qubit gates under 99% for practical parameters. This suggests that high fidelity gates of this circuit cannot be achieved through optimal control alone, and changes of the parameters/design of the circuits appear necessary. Additionally, to carry out the algebra involved, a symbolic commutator algebra package HamMaker is developed in Julia. The package is capable of automating some difficult algebra in optimal control that previously had to be performed by hand. Within this work, HamMaker has been used to derived some simple but nontrivial corrections to the existing effective Hamiltonian.
The goal of this work is to develop a wideband Josephson Parametric Amplifier (JPA) by the combination of a coplanar impedance transformer and a lumped Josephson Parametric Amplifier. Josephson Parametric Amplifiers take advantage of the nonlinear inductance of Josephson junctions and the nearly lossless superconductor circuits to achieve wave mixing and amplification with only quantum-limited added noise, which makes it a key component in quantum measurements, such as single-shot qubit measurement. However, the current JPAs generally have a relatively high quality factor and small bandwidth (around 50MHz to 100MHz), which limits their ability in measuring signals from a broad spectrum. Therefore, the work planned to use a coplanar impedance transformer to enhance the coupling between JPA and the signals, resulting in a small quality factor and large bandwidth. By decreasing the quality factor of JPA to around 3, one can achieve around 20dB gain within a bandwidth of several hundred MHz. Moreover, the wideband feature allows this device to explore the frequency dependence of the coupling between the JPA and the signals. Because the impedance transformer is based on a coplanar transmission line and requires no modifications of the lumped JPA, it is relatively straightforward to embed it in the current fabrication, package, and testing process for lumped JPA. The impedance transformer can be fabricated by liftoff or etching process with the lumped JPA together, which adds no fabrication complexity. The only difference is the size, which is around 3~5 times larger than lumped JPA. This may make the number of available chips on a single wafer lower. As for the public benefits, the development of this wideband JPA will make it possible to use just a single JPA device for multi-frequency signals, for example, the readout signals of a superconducting qubit chip with multiplexed readout bus. This would greatly decrease the demand for JPAs in quantum measurements.
Our group pioneers the use of Quantum Machine Learning (QML) on High Energy Physics analysis at LHC. We have successfully employed several QML classification algorithms in the ttH (Higgs production in association with a top quark pair) and Higgs to two muons (Higgs coupling to second generation fermions), two recent LHC flagship physics analysis, on gate-model quantum computer simulators and hardware. The simulation studies have been performed with the IBM Quantum Framework, Google Tensorflow Quantum Framework, and Amazon Braket Framework, and we have achieved good classification performance that is similar to the performances of the classical machine learning methods currently used in LHC physics analyses, classical SVM, classical BDT, and classical deep neural network for example. We have also performed our studies using IBM superconducting quantum computer hardware and the performance is promising and is approaching the performance from IBM quantum simulators. Moreover, we extend our studies to other QML areas such as quantum anomaly detection and quantum generative adversarial, and some preliminary results have been obtained. Also, we have overcome the challenges of intensive computing resources in the cases of large qubits (25 qubits or more) and large numbers of events using NVIDIA cuQuantum with NERSC Perlmutter HPC. Our studies give an example that Quantum Machine Learning performs as well as its classical counterpart for realistic High Energy Physics analysis datasets. Furthermore, our result on noisy quantum hardware provides important validation for the result on noiseless quantum simulators.
Our main focus is to investigate the sensitivity of training any of the SNAP parameters in the SNAP-Displacement protocol. We analyze conditions that could potentially lead to the Barren Plateau problem in a qudit system and draw comparisons with multi-qubit systems. The parameterized ansatz we consider consists of SNAP -Displacement blocks. We utilize techniques similar to those in [8] and [2] along with the concept of $t-$design. Through this analysis, we make the following key observations: (a) The trainability of a SNAP-parameter does not exhibit a preference for any particular direction within our cost function landscape, (b) using Haar measure properties, we establish new lemmas concerning the expectation of certain polynomial functions, and (c) utilizing these new lemmas, we identify a general condition that indicates an expected trainability advantage in a qudit system when compared to multi-qubit systems.
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full potential of the quantum state space. It is thus a crucial guidepost to know when selecting a particular PQC ansatz. However, the existing technique for expressibility computation through statistical estimation requires a large number of samples, which poses significant challenges due to time and computational resource constraints. This paper introduces a novel approach for expressibility estimation of PQCs using Graph Neural Networks (GNNs). We demonstrate the predictive power of our GNN model with a dataset consisting of 25,000 samples from the noiseless IBM QASM Simulator and 12,000 samples from three distinct noisy quantum backends. The model accurately estimates expressibility, with root mean square errors (RMSE) of 0.05 and 0.06 for the noiseless and noisy backends, respectively. We compare our model’s predictions with reference circuits from Sim et al. and IBM Qiskit’s hardwareefficient ansatz sets to further evaluate our model’s performance. Our experimental evaluation in noiseless and noisy scenarios reveals a close alignment with ground truth expressibility values, highlighting the model’s efficacy. Moreover, our model exhibits promising extrapolation capabilities, predicting expressibility values with low RMSE for out-of-range qubit circuits trained solely on only up to 5-qubit circuit sets. This work thus provides a reliable means of efficiently evaluating the expressibility of diverse PQCs on noiseless simulators and hardware.
Quantum information processing has witnessed significant advancements through the application of qubit- based techniques within universal gate sets. Recently, exploration beyond the qubit paradigm to d-dimensional quantum units or qudits has opened new avenues for improving computational efficiency. This paper delves into the qudit-based approach, particularly addressing the challenges presented in the high-fidelity implementation of qudit-based circuits due to increased complexity. As an innovative approach towards enhancing qudit circuit fidelity, we explore algebraic relations, such as the Yang-Baxter-like turnover equation, which may enable circuit compression and optimization. The paper introduces the turnover relation for the three-qutrit time propagator and its potential use in reducing circuit depth. We further investigate whether this relation can be generalized for higher-dimensional quantum circuits, including a focused study on the one-dimensional spin-1 Heisenberg model. Our paper outlines both rigorous and numerically efficient approaches to potentially achieve this generalization, providing a foundation for further explorations in the field of qudit-based quantum computing.
The QICK (Quantum Instrumentation Control Kit) integration aims to enhance the readout of superconducting qubits by leveraging machine learning (ML) techniques. These techniques offer high accuracy, increased speed, and better state preservation for qubit readouts. The integration process employs neural network algorithms to optimize system performance and achieve high accuracy rates. The QICK board uses an FPGA, allowing for efficient real-time processing and high-performance execution of machine learning algorithms.
In the era of modern computing, quantum bits (or qubits) are extensively researched due to their ability to perform computations exponentially faster than classical computers. One promising method for synthesizing qubits on a macroscopic scale is through superconductor diodes (SDs). Unfortunately, SDs are currently limited by their dependence on extremely low temperatures (T ≈ 0K), which restricts their practical usability at larger scales. To address this limitation, researchers are exploring new materials and methods to identify superconductors with higher critical temperatures. My research at the Lawrence Livermore National Laboratory (LLNL) focuses on synthesizing and characterizing superconductor diodes to determine their efficiency for quantum computing applications. Bismuth thin films were synthesized with magnetron sputtering with separate thicknesses which provides information on methods to alter their resistivity. Bismuth was characterized as the topological insulator used in the diode, and its resistivity was measured using a standard four-point probe technique with 1 mA current. Bismuth (99.99% pure) was then purified in a tubular furnace at 400C to use within a Josephson junction as a topological insulator. Niobium and Tantalum Josephson junctions were then synthesized, and they were characterized using a lock-in amplifier four-point probe technique. Materials such as niobium and tantalum are used in high-entropy alloy research, which is a relatively new class of material that has little research within the superconductor diode community, yet the current research shows promise. We anticipate that testing high-entropy superconductors will reveal crucial factors, potentially leading to the development of high-temperature superconductors in the future.
The transfer of quantum properties and entanglement between photons and matter (transduction) is a major challenge in QIS, especially with regard to networking quantum information processing. In order to overcome this challenge, it is important to know how non-classical light (single or entangled photons) interacts with matter. This project specifically addresses fundamental questions about the interaction of single and entangled photons with molecules and nanostructures to produce electrons with prescribed spin polarizations and pairs of electrons with entangled spins, thus resulting in transduction by addressing two major objectives: (2) In Objective 1, we will investigate the formation of polarization-encoded radical (spin qubit) pairs and molecular triplet states photoexcited by single quantum emitter light sources. (2) In Objective 2, we will demonstrate the use of two entangled photons, generated by spontaneous parametric down conversion (SPDC) or by the coherent biexcitonic states of perovskite NPLs, to produce two entangled radical (spin qubit) pairs or molecular triplet states.
The Cosmic Quantum group at Fermilab operates three cryogenic facilities dedicated to the development and calibration of superconducting low-threshold detectors and qubits. One of which (LOUD) is located at the surface, while the other two (NEXUS and QUIET) are located 100 m underground enabling low-background device characterization and rare event searches. Recently, we have demonstrated world-leading resolution in the quasiparticle channel for kinetic inductance phonon-mediated detectors as well as the lowest rate of spatially- and temporally- correlated errors ( charge jumps ) in superconducting qubit chips. We have additionally made significant progress in expanding the suite of tools for simulating signal production and readout of these devices. In this talk, I will review these recent results, discuss some nascent projects focused on enhancing sensitivity of these detectors to sub-GeV dark matter, and highlight activities in these facilities over the next year.
The collaboration between the Superconducting Quantum Materials and Systems Center (SQMS) and Rigetti Computing produced several advancements in our understanding of the role of materials characteristics in quantum processor performance. This partnership leverages SQMS's extensive characterization infrastructure and cutting-edge research in materials, and Rigetti's expertise in quantum hardware and robust nanofabrication to improve precision and performance of Rigetti's test QPUs. Qubit frequency is determined in large part by the properties of Josephson junctions (JJs) made of amorphous oxide tunnel barriers; the Alternating-Bias Assisted Annealing (ABAA) process allows us to tune JJs to their desired frequency [1]. Work by SQMS researchers in characterizing high-precision JJs post-processed (using ABAA) have yielded crucial information on the nature of the structure and chemical bonding uniformity of the ABAA processed amorphous oxides. Performance has also been improved through a comprehensive series of experiments that tested encapsulation and surface treatment. Encapsulation of the niobium metal layer with tantalum resulted in an T1 improvement of 80%, experimentally confirming the role of Nb surface losses in qubit performance [2]. Pre-treatment of the underlying silicon surface prior to JJ fabrication by replacing a buffered oxide etch (BOE) with hydrofluoric acid (HF) followed by aqueous ammonium fluoride (NH4F) has shown a statistically significant improvement of T1 by 22%, and reduction in the number of strongly-coupled TLS [3]. These examples, as well as many other published and ongoing investigations, demonstrate the mutual benefits that come from Rigetti's involvement in the SQMS collaboration. [1] - Pappas, D.P., et al. (2024). https://doi.org/10.1038/s43246-024-00596-z [2] - Bal, M., et al. (2024). https://doi.org/10.1038/s41534-024-00840-x [3] Kopas, C. J. et al. Preprint at https://doi.org/10.48550/arXiv.2408.02863 (2024).
Coplanar waveguide resonators are a perfect tool to evaluate the losses induced by defects and interfaces in superconducting devices. Even if niobium is the most used superconductor for resonators and qubits, its native oxide at the metal-air (MA) interface limits the device results. Tantalum has recently significantly improved qubit performances due to a thinner and less disordered oxide layer compared to Nb. To further improve the MA interface, we used rhenium, a superconducting material with 1.7 K critical temperature resistant to oxidation: it forms an oxide layer thinner than 1 nm. In this study, we will present a thorough investigation of rhenium CPW resonator measurements with internal quality factors at the single photon level exceeding 2 million. The devices have been fabricated on a sapphire substrate while the processing parameters have been varying and optimized. The measurements have been performed as a function of power and temperature to disentangle different sources of losses, such as two-level systems (TLS) and quasi-particles. A peculiar TLS temperature dependence has been measured and analyzed. In this work, we also vary the participation ratio of the devices to extract the losses introduced by the involved interfaces with higher fidelity and precision. We carried on a deep material characterization effort to link the results to the differences in the fabrication steps, and we will present material characterization measurements performed via AFM, ToF-SIMS, XPS, and TEM.
In our work we present on-chip thermometry concept aimed at improving temperature control during quantum measurements. The key issue addressed is lack of precise temperature monitoring, especially considering the unavoidable thermal resistance between the qubits’ chip and mixing plate of dilution refrigerator. The suggested solution includes a Coulomb Blockade Thermometer (CBT) based on an array of intrinsically normal-conducting tunnel junctions, currently developed at Aalto University. This is a practical solution for superconducting qubit systems.
The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermi National Accelerator Laboratory, is one of five U.S. Department of Energy-funded research centers dedicated to advancing quantum computing and quantum sensing. SQMS leverages its expertise in superconducting radio-frequency (SRF) cavities and transmon qubits to develop sensitive quantum sensors to probe fundamental physics. This presentation will highlight the progress and innovations in SQMS’s quantum sensing thrust, with a particular focus on searches for particles beyond the Standard Model, dark matter candidates, and gravitational waves. Recent developments include the search for dark photon dark matter using transmon qubits and widely tunable superconducting cavities (4 GHz–7 GHz), a quantum-enhanced axion dark matter search employing an itinerant single microwave photon counter, and efforts to detect low-mass axions and high-frequency gravitational waves using multimode superconducting cavities.