Sandia Analysis Workbench (SAW) Workflow using Xyce-PyMi for Simulating Circuit-Level Response using Data-Driven Device Models
Explore the source record for details and available documents.
Kuberry, Paul Allen↗
SEARCH · Search NASA
Results for “circuit model”
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.
Operator dynamics in Floquet many-body systems
We study operator dynamics in many-body quantum systems, focusing on generic features of systems that are ergodic, spatially extended, and lack conserved densities. Quantum circuits of various types provide simple models for such systems. We focus on Floquet quantum circuits, comparing their behavior with what has been found previously for circuits that are random in time. Floquet circuits, which have discrete time-translation symmetry, represent an intermediate case between circuits that are random in time and lack any symmetry, and systems with a time-independent Hamiltonian and continuous time-translation invariance. By making this comparison, one of our aims is to identify signatures of time-translation symmetry in Floquet operator dynamics. To characterize behavior we examine a variety of quantities in solvable models and numerically: operator autocorrelation functions; the partial spectral form factor; the out-of-time-order correlator (OTOC); and the paths in operator space that make the dominant contributions to the ensemble-averaged autocorrelation functions. Our most striking result is that ensemble-averaged autocorrelation functions show behavior that is distinctively different in Floquet systems compared to systems in which successive time-steps are independent. Specifically, while average autocorrelation functions decay on a microscopic timescale for circuits that are random in time, in Floquet systems they have a late-time tail with a duration that grows parametrically with the size of the operator support. In the simplest models this tail is separated from the initial decay by a minimum, so that the average autocorrelation function has an intermediate-time peak. The existence of these tails provides a way to understand deviations of the spectral form factor from random matrix behavior at times shorter than the Thouless time. In contrast to this feature in autocorrelation functions, we find no new aspects to the behavior of OTOCs for Floquet models compared to random-in-time circuits. We show that this difference between averaged autocorrelation functions and OTOCs can be understood in terms of the paths in operator space that contribute to the two quantities: paths for the former retain a limited support at late times, while paths for the latter are dominated by operator spreading. Published by the American Physical Society 2025
Yoshimura, Takato (ORCID:0000000309159846)↗
Entanglement phases in large- N hybrid Brownian circuits with long-range couplings
Here, we develop solvable models of large-N hybrid quantum circuits on qubits and fermions with tunable long-range power-law interactions and continuous local monitoring. These models provide analytical access to the entanglement phase diagram and error-correcting properties of many-body entangled nonequilibrium states generated by such dynamics. In one dimension, the long-range couplings are irrelevant for α > 3/2, where α is the power-law exponent, and the models exhibit a conventional measurement-induced phase transition between volume- and area-law entangled phases. For 1/2 < α < 3/2, the long-range couplings become relevant, leading to a nontrivial dynamical exponent at the measurement-induced phase transition. More interestingly, for α < 1, the entanglement pattern receives a subvolume correction for both area-law and volume-law phases, indicating that the phase realizes a quantum error correcting code whose code distance scales as L 2–2α . While the entanglement phase diagram is the same for both the interacting qubit and fermionic hybrid Brownian circuits, we find that long-range free-fermionic circuits exhibit a distinct phase diagram with two different fractal entangled phases.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Short-Depth QAOA circuits and Quantum Annealing on Higher-Order Ising Models (Rev.2)
The Quantum Alternating Operator Ansatz (QAOA) and Quantum Annealing (QA) are quantum algorithms that are both based on the adiabatic theorem and both have the goal of sampling the optimal solution(s) of combinatorial optimization problems. Quantum annealing has been physically instantiated on D-Wave devices using superconducting flux qubits, and QAOA can be programmed on digital gate-model quantum computers such as the programmable superconducting transmon qubits devices of the IBMQ series, for instance ibm washington. QAOA and QA address the same types of problems, but it is unclear how they will scale to large problem sizes and to larger and higher-fidelity quantum computers. In this article, we present a direct comparison between QAOA, one and two rounds, run on all 127 qubits of ibm washington and QA run on D-Wave Advantage system4.1 and Advantage system6.1. The problems which allow for this comparison are random Ising model problems whose connectivity matches the heavy hexagonal lattice topology of ibm washington and the Pegasus graph connectivity of the two D-Wave devices. We create two classes of problem instances for this comparison: one with higher order terms (ZZZ variable interactions), linear terms, and quadratic terms, and a separate problem type with only linear and quadratic terms. Our QAOA circuits are novel and extremely short depth, with a CNOT depth of 6 per round, which allows whole chip usage of ibm washington’s heavy hexagonal lattice and can be applied to future heavy-hex chips. We also test the effectiveness of the error suppression technique digital dynamical decoupling on the QAOA circuits. The QAOA circuits compiled to ibm washington are composed of several thousand circuit instructions, approximately 3, 000 depending on the details of the circuit, making these some the largest quantum circuits ever executed on a digital quantum processor. QAOA and QA are compared against the classical heuristic algorithm of simulated annealing and all problem instances are exactly solved using CPLEX in order to evaluate which samplers, if any, correctly found the ground state solution(s) of the problem instances. We find that (i) QA outperforms QAOA on all problem instances, (ii) QAOA samples the problems better than random sampling, and (iii) QAOA angle computation exhibits clear parameter concentration across the ensemble of Ising models.
127 Qubits↗
Parameter extraction for a SPICE model of an hTron superconducting thermal switch
Efficiently simulating large circuits is crucial to the development of superconducting nanowire-based electronics. However, current simulation tools for this technology are not adapted to the scaling of circuit size and complexity. We focus on the multilayered heater-nanocryotron (hTron), a promising superconducting nanowire-based switch used in applications such as superconducting nanowire single-photon detector readout. Previously, the hTron was modeled using traditional finite-element methods, which fall short in simulating systems at a larger scale. An empirical-based method would be better adapted to this task, enhancing both simulation speed and agreement with experimental data. In this work, we perform switching current and activation delay measurements on 17 hTron devices. We then develop a method for extracting physical fitting parameters used to characterize the devices. We build a SPICE behavioral model that reproduces the static and transient device behavior using these parameters, and validate it by comparing its performance to a model developed in prior work, showing an improvement in simulation time by several orders of magnitude. Furthermore, our model provides circuit designers with a tool to help understand the hTron’s behavior during all design stages, thus enabling broader use of the hTron across various new areas of application.
Caloritronics↗
Benchmarking characterization methods for noisy quantum circuits
Effective methods for characterizing the noise in quantum computing devices are essential for programming and debugging circuit performance. Existing approaches vary in the information obtained as well as the amount of quantum and classical resources required, with more information generally requiring more resources. Here we benchmark the characterization methods of gate set tomography, Pauli channel noise reconstruction, and empirical direct characterization for developing models that describe noisy quantum circuit performance on a 27-qubit superconducting transmon device. We evaluate these models by comparing the accuracy of noisy circuit simulations with the corresponding experimental observations. Here, we find that the agreement of noise model to experiment does not correlate with the information gained by characterization and that the underlying circuit strongly influences the best choice of characterization approach. Empirical direct characterization scales best of the methods we tested and produced the most accurate characterizations across our benchmarks.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Design and performance evaluation of a dual-circuit thermal energy storage module for air conditioners
We present experimental results and a validated numerical model of a dual-circuit phase-change thermal energy storage module for air conditioners. The module incorporates a phase-change material encapsulated in compressed expanded natural graphite foam. We used n-tetradecane as the PCM with a transition temperature (~4.5 °C) suitable for air-conditioning applications. Heat exchange to and from the module is accomplished through two fluid loops operating as a heat source and sink embedded inside multiple slabs of the composite material. This dual-circuit design enables easier integration with air-conditioning equipment and provides enhanced flexibility in system operation as compared to the state-of-the-art thermal storage systems. When integrated with an air-conditioner, this design will enable peak-load shaving and enhances operational efficiency. The thermal storage device was designed for a nominal storage capacity of ~ 3.5 kWh. We evaluated the heat transfer and energy storage performance of this device using standalone heat transfer experiments to estimate key thermal resistances and identify design improvements before integration with an air conditioner. The numerical model of the heat exchanger uses a combination of discretized and lumped parameter approaches to maintain a balance between accuracy and computational expense. Our analyses show that the geometric features and integration of fluid tubes are key contributors to the thermal contact resistance between the fluid and the thermal storage material, and consequently, to the overall performance of the thermal storage module. Our standalone experiments also identified important operating scenarios in which this thermal storage module can be used for air-conditioning in buildings.
25 ENERGY STORAGE↗
Numerical and experimental analysis of mechanically induced failure in electric vehicle battery modules
Mitigating thermal runaway and cell-to-cell propagation is essential for improving the safety of electric and hybrid vehicles. Enhancing digital twin capabilities to predict battery mechanical abuse is particularly critical for automotive and aerospace applications, where crashworthiness is a key concern. Understanding failure conditions and propagation in battery modules during mechanical abuse is complex due to interactions between structural deformation, heat transfer, electrochemical processes, exothermic reactions and mechanical fracture. While prior studies have focused on modeling cell-level behavior, extending these models to module or pack level is necessary for a system level understating of electric vehicle safety. This study develops coupled large deformation finite element models that simultaneously solve for electrochemistry, material failure, internal short circuit and thermal runaway propagation. The models account for mechanical and thermal interactions between lithium-ion cells and other battery components while the contact interfaces are evolving with time. Model-predicted voltage, temperature and force responses are compared with experimental data for validation. The results demonstrate that the approach captures key failure mechanisms, including thermal propagation through heat transfer, electrical propagation from short circuits in parallel-connected cells, and mechanical propagation via penetration and crack formation. These findings show that computational models are valuable tools for understanding battery module failure and providing insight that can reduce the need for extensive experimental testing.
25 ENERGY STORAGE↗
Many-body quantum dynamics slows down at low density
We study quantum many-body systems with a global U(1) conservation law, focusing on a theory of N interacting fermions with charge conservation, or N interacting spins with one conserved component of total spin. We define an effective operator size at finite chemical potential through suitably regularized out-of-time-ordered correlation functions. The growth rate of this density-dependent operator size vanishes algebraically with charge density; hence we obtain new bounds on Lyapunov exponents and butterfly velocities in charged systems at a given density, which are parametrically stronger than any Lieb-Robinson bound. We argue that the density dependence of our bound on the Lyapunov exponent is saturated in the charged Sachdev-Ye-Kitaev model. We also study random automaton quantum circuits and Brownian Sachdev-Ye-Kitaev models, each of which exhibit a different density dependence for the Lyapunov exponent, and explain the discrepancy. We propose that our results are a cartoon for understanding Planckian-limited energy-conserving dynamics at finite temperature.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Modeling and Validation of Offset Cancellation for Hybrid Photonic-Electronic Transimpedance Amplifier Using All-Electronic Circuits
Hybrid photonic-electronic circuits involving crossdomain feedback signals are difficult to validate and troubleshoot. This paper describes an approach to model and validate an offset-cancellation loop for a hybrid transimpedance amplifier (TIA) using all-electronic circuits. An equivalent electrical model is developed for the tunable interferometer attenuators used for offset trimming, and it includes test features to extract the system performance. The stability analysis of the system shows 75 ◦ of phase margin and 102 dB of gain margin. Measurement results demonstrate good agreement with analysis and successful TIA offset cancellation.
Marchant, Jared↗
Final Technical Report: Innovative Magnetron Power Sources for Superconducting RF (SRF) Accelerators
In this project, MUONS is designing and building custom magnetrons at Richardson Electronics (RELL) to test the predictions of the models and make improvements to the designs to match the needs of NP SRF accelerators and their life-cycle costs. To increase lifetime, the filament temperature is controlled by a feedback circuit that includes a model of back streaming of electrons. The gain of the magnetron is optimized by the design of an input port. A permanent magnet design with trim coil for amplitude adjustment reduces energy consumption. Moving the construction of the magnetron from prototype to manufacturing includes the utilization of RELL processes and designs. To the extent that outgassing of materials influences the lifetime of the magnetron, methods for minimizing outgassing was explored. Further experimentation of the potential of sub-critical voltage operation continues to be explored. The commercialization goal of the project will be naturally achieved as RELL already has world-wide marketing, sales, and support to exploit the assembly-line capabilities generated by this project to enable magnetron technologies for NP accelerators.
43 PARTICLE ACCELERATORS↗
Quantum circuit fidelity estimation using machine learning
The computational power of real-world quantum computers is limited by errors. When using quantum computers to perform algorithms which cannot be efficiently simulated classically, it is important to quantify the accuracy with which the computation has been performed. In this work, we introduce a machine learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the target state corresponding to ideal noise-free computation. Our machine learning model is trained in a supervised manner, using smaller or simpler circuits for which the fidelity can be estimated using other techniques like direct fidelity estimation and quantum state tomography. Here we demonstrate that, for simulated random quantum circuits with a realistic noise model, the trained model can predict the fidelities of more complicated circuits for which such methods are infeasible. In particular, we show that the trained model may make predictions for circuits with higher degrees of entanglement than were available in the training set and that the model may make predictions for non-Clifford circuits even when the training set included only Clifford-reducible circuits. This empirical demonstration suggests classical machine learning may be useful for making predictions about beyond-classical quantum circuits for some non-trivial problems.
97 MATHEMATICS AND COMPUTING↗
The battle of clean and dirty qubits in the era of partial error correction
When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be run, but each additional physical qubit can potentially contribute an exponential increase in computational space, so there is a trade-off between using qubits for error correction or using them as noisy qubits. In this work we look at the effects of using noisy qubits in conjunction with noiseless qubits (an idealized model for error-corrected qubits), which we call the "clean and dirty" setup. We employ analytical models and numerical simulations to characterize this setup. Numerically we show the appearance of Noise-Induced Barren Plateaus (NIBPs), i.e., an exponential concentration of observables caused by noise, in an Ising model Hamiltonian variational ansatz circuit. We observe this even if only a single qubit is noisy and given a deep enough circuit, suggesting that NIBPs cannot be fully overcome simply by error-correcting a subset of the qubits. On the positive side, we find that for every noiseless qubit in the circuit, there is an exponential suppression in concentration of gradient observables, showing the benefit of partial error correction. Finally, our analytical models corroborate these findings by showing that observables concentrate with a scaling in the exponent related to the ratio of dirty-to-total qubits.
97 MATHEMATICS AND COMPUTING↗
Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage
To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.
59 BASIC BIOLOGICAL SCIENCES↗
Unsupervised quantum circuit learning in high energy physics
Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. Here, in this work, we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use nonadversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over two and three variables.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
The battle of clean and dirty qubits in the era of partial error correction
When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be run, but each additional physical qubit can potentially contribute an exponential increase in computational space, so there is a trade-off between using qubits for error correction or using them as noisy qubits. In this work, we look at the effects of using noisy qubits in conjunction with noiseless qubits (an idealized model for error-corrected qubits), which we call the “clean and dirty” setup. We employ analytical models and numerical simulations to characterize this setup. Numerically we show the appearance of Noise-Induced Barren Plateaus (NIBPs), i.e., an exponential concentration of observables caused by noise, in an Ising model Hamiltonian variational ansatz circuit. We observe this even if only a single qubit is noisy and given a deep enough circuit, suggesting that NIBPs cannot be fully overcome simply by error-correcting a subset of the qubits. On the positive side, we find that for every noiseless qubit in the circuit, there is an exponential suppression in the concentration of gradient observables, showing the benefit of partial error correction. Finally, our analytical models corroborate these findings by showing that observables concentrate with a scaling in the exponent related to the ratio of dirty-to-total qubits.
97 MATHEMATICS AND COMPUTING↗
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↗