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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.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

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.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A linear response framework for quantum simulation of bosonic and fermionic correlation functions

Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiment. Within the context of quantum computing, and via a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and can be more efficient than competing methods. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green’s functions can be obtained, and apply these ideas to the study of a charge-density-wave material on the ibm_auckland superconducting quantum computer. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Scattering Processes from Quantum Simulation Algorithms for Scalar Field Theories

We provide practical simulation methods for scalar field theories on a quantum computer that yield improved asymptotics as well as concrete gate estimates for the simulation and physical qubit estimates using the surface code. We achieve these improvements through two optimizations. First, we consider a finite volume approach for estimating the elements of the S-matrix. This approach is appropriate in general for 1+1D and for certain low-energy elastic collisions in higher dimensions. Second, we implement our approach using a series of different fault-tolerant simulation algorithms for Hamiltonians formulated both in the field occupation basis and field amplitude basis. Our algorithms are based on either second-order Trotterization or qubitization. The cost of Trotterization in occupation basis scales as O ( λ N 7 | Ω | 3 / ( M 5 / 2 ϵ 3 / 2 ) ) where λ is the coupling strength, N is the occupation cutoff, | Ω | is the volume of the spatial lattice, M is the mass of the particles and ϵ is the uncertainty in the energy calculation used for the S -matrix determination. Qubitization in the field basis scales as O ( | Ω | 2 ( k 2 Λ + k M 2 ) / ϵ ) , where k is the cutoff in the field and Λ is a scaled coupling constant. We find in both cases that the bounds suggest physically meaningful simulations can be performed using on the order of 4 × 10 6 physical qubits and 10 12 T -gates which corresponds to roughly one day on a superconducting quantum computer with surface code and a cycle time of 100 ns. This places the simulation of scalar field theory within striking distance of the gate counts for the best available chemistry simulation results.

Hardy, Andrew [Toronto U.] (ORCID:0000000235817382↗
DOE OSTI2026

Measuring quasiparticle dynamics for particle impact reconstruction in a superconducting qubit chip

Quasiparticle poisoning following particle impacts poses a significant challenge to the development of fault-tolerant superconducting quantum computers, as a sudden excess of quasiparticles can simultaneously degrade the coherence of multiple qubits across large device arrays. In this work, we present a statistical analysis that models the time evolution of radiation-induced qubit energy relaxation through quasiparticle density dynamics. This study provides insight into quasiparticle loss processes by distinguishing between recombination and trapping decay channels and assessing their respective impact on qubit performance. We precisely measure quasiparticle recombination in multiple transmon qubits and uncover an unexpected dependence of qubit relaxation dynamics on deposited energy. By linking correlated relaxation events across qubits to ballistic phonon propagation, we introduce a statistical localization approach to extract the energy deposited in the substrate, which is in good agreement with Monte Carlo simulation. This work establishes the quantitative framework for using an arbitrary subset of superconducting transmon qubits in a QPU as energy-resolving witness particle detectors.

Celi, E. [Northwestern U.]↗
DOE OSTI2023

Visualizing heterogeneous dipole fields by terahertz light coupling in individual nano-junctions

The challenge underlying superconducting quantum computing is to remove materials bottleneck for highly coherent quantum devices. The nonuniformity and complex structural components in the underlying quantum circuits often lead to local electric field concentration, charge scattering, dissipation and ultimately decoherence. Here we visualize interface dipole heterogeneous distribution of individual Al/AlO$_{x}$/Al junctions employed in transmon qubits by broadband terahertz scanning near-field microscopy that enables the non-destructive and contactless identification of defective boundaries in nano-junctions at an extremely precise nanoscale level. Our THz nano-imaging tool reveals an asymmetry across the junction in electromagnetic wave-junction coupling response that manifests as hot (high intensity) vs cold (low intensity) spots in the spatial electrical field structures and correlates with defected boundaries from the multi-angle deposition processes in Josephson junction fabrication inside qubit devices. The demonstrated local electromagnetic scattering method offers high sensitivity, allowing for reliable device defect detection in the pursuit of improved quantum circuit fabrication for ultimately optimizing coherence times.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware

We implement a quantum generalization of a neural network on trapped-ion and IBM superconducting quantum computers to classify MNIST images, a common benchmark in computer vision. The network feedforward involves qubit rotations whose angles depend on the results of measurements in the previous layer. The network is trained via simulation, but inference is performed experimentally on quantum hardware. The classical-to-quantum correspondence is controlled by an interpolation parameter, $a$, which is zero in the classical limit. Increasing $a$ introduces quantum uncertainty into the measurements, which is shown to improve network performance at moderate values of the interpolation parameter. We then focus on particular images that fail to be classified by a classical neural network but are detected correctly in the quantum network. For such borderline cases, we observe strong deviations from the simulated behavior. We attribute this to physical noise, which causes the output to fluctuate between nearby minima of the classification energy landscape. Such strong sensitivity to physical noise is absent for clear images. We further benchmark physical noise by inserting additional single-qubit and two-qubit gate pairs into the neural network circuits. Our work provides a springboard toward more complex quantum neural networks on current devices: while the approach is rooted in standard classical machine learning, scaling up such networks may prove classically non-simulable and could offer a route to near-term quantum advantage.

FOS: Physical sciences↗
DOE OSTI2025

Quantifying trapped magnetic vortex losses in niobium resonators at mK temperatures

Trapped magnetic vortices in niobium introduce microwave losses that degrade the performance of superconducting resonators. While such losses have been extensively studied above 1 K, we report here their direct quantification in the millikelvin and low-photon regime relevant to quantum devices. Using a high-quality factor 3D niobium cavity cooled through its superconducting transition in controlled magnetic fields, we isolate vortex-induced losses and find the resistive component of the sensitivity to trapped flux S to be approximately 2 n Ω/mG at 10 mK and 6 GHz. The decay rate is initially dominated by two-level system (TLS) losses from the native niobium pentoxide, with vortex-induced degradation of T 1 occurring above B trap ∼ 50 mG. In the absence of the oxide, even 10 mG of trapped flux limits performance, Q 0 ∼ 10 10 , or T 1 ∼ 350 ms, underscoring the need for stringent magnetic shielding. The resistive sensitivity, S, decreases with temperature and remains largely field-independent, whereas the reactive component, S′, exhibits a maximum near 0.8 K. These behaviors are well modeled within the Coffey–Clem framework in the zero-creep limit, under the assumption that vortex pinning is enhanced by thermally activated processes. Our results suggest that niobium-based transmon qubits can tolerate vortex-induced dissipation at trapped field levels up to several hundred mG, but achieving long coherence times still requires careful magnetic shielding to suppress lower-field losses from other mechanisms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Suppression of ferromagnetic correlations in hole-doped SrCo 2 As 2

We report the growth of high-quality single crystals of hole-doped composition Sr 0.95 K 0.05 Co 2 As 2 and present an investigation of its structural, electronic, thermal, and magnetic properties. Our results show that hole doping leads to a substantial suppression of ferromagnetic (FM) correlations present in SrCo 2 As 2 leading to nearly isotropic magnetic susceptibilities χ ab,c (T) of Sr 0.95 K 0.05 Co 2 As 2 . The presence of FM fluctuations is considered one of the detrimental reasons why SrCo 2 As 2 does not achieve a superconducting ground state despite having a precursor for it in the form of stripe-like antiferromagnetic fluctuations. The suppression of FM fluctuations leads to qualitative similarities between the properties of hole-doped SrCo 2 As 2 and the end member of superconducting 122 iron-arsenide family KFe 2 As 2 . Our results also infer the presence of strongly correlated electronic states in Sr 0.95 K 0.05 Co 2 As 2 . Further, they show that the electronic structure of SrCo 2 As 2 as well as this hole-doped composition is exceptionally unstable in the vicinity of Fermi level EF for small alterations in the structural parameters and sensitively depends upon the details of simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Systematic Construction of Time-Dependent Hamiltonians for Microwave-Driven Josephson Circuits

Time-dependent electromagnetic drives are fundamental for controlling complex quantum systems, including superconducting Josephson circuits. In these devices, accurate time-dependent Hamiltonian models are imperative for predicting their dynamics and designing high-fidelity quantum operations. Existing numerical methods, such as black-box quantization (BBQ) and energy-participation ratio (EPR), excel at modeling the static Hamiltonians of Josephson circuits. However, these techniques do not fully capture the behavior of driven circuits stimulated by external microwave drives, nor do they include a generalized approach to account for the inevitable noise and dissipation that enter through microwave ports. Here, we introduce numerical techniques that leverage classical microwave simulations, efficiently executable in finite-element solvers, to obtain the time-dependent Hamiltonian of microwave-driven superconducting circuits with arbitrary geometries under charge, flux, or mixed electromagnetic modulation. Importantly, our techniques do not rely on a lumped-element description of the superconducting circuit, in contrast to previous approaches to tackling this problem. We demonstrate the versatility of our approach by characterizing the driven properties of realistic circuit devices in complex electromagnetic environments, including coherent dynamics due to charge and flux modulation, as well as drive-induced relaxation and dephasing. Our techniques offer a powerful toolbox for optimizing circuit designs and advancing practical applications in superconducting quantum computing.

Lu, Yao [Yale U.; Yale U. (main); Fermilab] (ORCID↗
DOE OSTI2021

Engineering Dynamical Sweet Spots to Protect Qubits from 1/$f$ Noise

Protecting superconducting qubits from low-frequency noise is essential for advancing superconducting quantum computation. Based on the application of a periodic drive field, we develop a protocol for engineering dynamical sweet spots which reduce the susceptibility of a qubit to low-frequency noise. Using the framework of Floquet theory, we prove rigorously that there are manifolds of dynamical sweet spots marked by extrema in the quasi-energy differences of the driven qubit. In particular, for the example of fluxonium biased slightly away from half a flux quantum, we predict an enhancement of pure-dephasing by three orders of magnitude. Employing the Floquet eigenstates as the computational basis, we show that high-fidelity single- and two-qubit gates can be implemented while maintaining dynamical sweet-spot operation. We further confirm that qubit readout can be performed by adiabatically mapping the Floquet states back to the static qubit states, and subsequently applying standard measurement techniques. Our work provides an intuitive tool to encode quantum information in robust, time-dependent states, and may be extended to alternative architectures for quantum information processing.

Huang, Ziwen↗
DOE OSTI2021

Control Design for Inhomogeneous-Broadening Compensation in Single-Photon Transducers

We report a transducer of single photons between microwave and optical frequencies can be used to realize quantum communication over optical fiber links between distant superconducting quantum computers. A promising scalable approach to constructing such a transducer is to use ensembles of quantum emitters interacting simultaneously with electromagnetic fields at optical and microwave frequencies. However, inhomogeneous broadening in the transition frequencies of the emitters can be detrimental to this collective action. In this paper, we utilize a gradient-based optimization strategy to design the temporal shape of the laser field driving the transduction system to mitigate the effects of inhomogeneous broadening. We study the improvement of transduction efficiencies as a function of inhomogeneous broadening in different single-emitter cooperativity regimes and correlate it with a restoration of super-radiance effects in the emitter ensembles. Furthermore, to assess the optimality of our pulse designs, we provide certifiable bounds on the design problem and compare them to the achieved performance.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

Johnson, Hans↗
DOE OSTI2020

Impact of ionizing radiation on superconducting qubit coherence

The practical viability of any qubit technology stands on long coherence times and high-fidelity operations, with the superconducting qubit modality being a leading example. However, superconducting qubit coherence is impacted by broken Cooper pairs, referred to as quasiparticles, with a density that is empirically observed to be orders of magnitude greater than the value predicted for thermal equilibrium by the Bardeen-Cooper-Schrieffer (BCS) theory of superconductivity. Previous work has shown that infrared photons significantly increase the quasiparticle density, yet even in the best isolated systems, it still remains higher than expected, suggesting that another generation mechanism exists. In this Letter, we provide evidence that ionizing radiation from environmental radioactive materials and cosmic rays contributes to this observed difference, leading to an elevated quasiparticle density that would ultimately limit superconducting qubits of the type measured here to coherence times in the millisecond regime. We further demonstrate that introducing radiation shielding reduces the flux of ionizing radiation and positively correlates with increased coherence time. Albeit a small effect for today’s qubits, reducing or otherwise mitigating the impact of ionizing radiation will be critical for realizing fault-tolerant superconducting quantum computers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Single Photon Detection with On-Chip Number Resolving Capability

Single photon detection (SPD) plays an important role in many forefront areas of fundamental science and advanced engineering applications. In recent years, rapid developments in superconducting quantum computation, quantum key distribution, and quantum sensing call for SPD in the microwave frequency range. We have explored in this LDRD project a new approach to SPD in an effort to provide deterministic photon-number-resolving capability by using topological Josephson junction structures. In this SAND report, we will present results from our experimental studies of microwave response and theoretical simulations of microwave photon number resolving detector in topological Dirac semimetal Cd 3 As 2 . These results are promising for SPD at the microwave frequencies using topological quantum materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Improving Signal-to-Noise Ratio (SNR) for Quantum Readout Signals Using Adaptive Hardware Filters on FPGA

This research presents an innovative approach to improving the Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals, utilizing FPGA-based adaptive filtering on the control board. Our solution aims to bolster quantum computations' reliability by providing a more accurate and precise way to obtain our readout traces and achieve a higher discernability between excitation states in real-time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the n oise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Deriving Effective Coupling Strength With Born-Oppenheimer Approx.

Tunable couplers have been important in the development of superconducting quantum computers. The ability to maintain zero coupling between qubits during operation, a feature of high-fidelity quantum gates, has been shown to be possible with the use of tunable couplers. As an example, Sete et al., 2021 showed this was possible with a tunable coupler between transmons: the qubit-coupler and qubit-qubit couplings create a net effective coupling strength between the qubits that can be tuned to zero. However, the derivation of the effective coupling strength between the qubits is quite complex. We propose a simpler method of obtaining this effective coupling with the Born-Oppenheimer approximation.

Wichmann, Conrad↗
DOE OSTI2025

Low-latency quantum control using AI algorithms on cryogenic microelectronics platforms

Superconducting quantum computers are a particular technology that involve the realization of qubits by means of LC circuits with a Josephson junction. As shown in Fig. 1, this latter addition has the effect of introducing a nonlinearity (anharmonicity) that makes the frequency spacing between each energy level non-uniform. This is a desired effect for separating the two lowest levels, used for computation, from the higher ones, that are theoretically infinite and which are not desired to be used.

97 MATHEMATICS AND COMPUTING↗