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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2026

Characterization and automated optimization of laser-driven proton beams from converging liquid sheet jet targets

Compact, stable, and versatile laser-driven ion sources hold great promise for applications ranging from medicine to materials science and fundamental physics. While single-shot sources have demonstrated favorable beam properties, including the peak fluxes necessary for several applications, high-repetition-rate operation will be necessary to generate and sustain the high average flux needed for many of the most exciting applications of laser-driven ion sources. Further, to navigate through the high-dimensional space of laser and target parameters toward experimental optima, it is essential to develop ion acceleration platforms compatible with machine learning techniques and capable of autonomous real-time optimization. Here, we present a multi-Hz ion acceleration platform employing a liquid sheet jet target. We characterize the laser-plasma interaction and the laser-driven proton beam across a variety of key parameters governing the interaction using an extensive suite of online diagnostics. We also demonstrate real-time, closed-loop optimization of the ion beam maximum energy by tuning the laser wave front using a Bayesian optimization scheme. This approach increased the maximum proton energy by 11% compared to a manually optimized wave front by enhancing the energy concentration within the laser focal spot, demonstrating the potential for closed-loop optimization schemes to tune future ion accelerators for robust high-repetition-rate operation.

Glenn, G. D. [SLAC National Accelerator Laboratory↗
DOE OSTI2020

Optimal control of coupled quantum systems based on the first-order Magnus expansion: Application to multiple dipole-dipole-coupled molecular rotors

This paper presents a method for performing approximate optimal control simulations for quantum systems with multiple coupled degrees of freedom. In this work, the time evolution is simulated using the first-order Magnus expansion in the interaction picture, where the couplings between different degrees of freedom are treated as the perturbation. A numerical implementation procedure is presented that leverages upon pairwise couplings and the separability of the zeroth-order time evolution operator to achieve a reduced computational cost, which is analyzed with respect to the number of degrees of freedom. The formulation is compatible with gradient-free methods to optimize the control field, and a stochastic hill climbing algorithm is adopted for this purpose. As illustrations, optimal control simulations are performed for systems of two and three dipole-dipole-coupled molecular rotors under the influence of a control field. For the two-rotor system, the field is optimized to achieve either orientation or entanglement objectives. For the three-rotor system, the field is optimized either to orient all three rotors in the same direction or to orient one rotor in a particular direction while the other two rotors point in the opposite direction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Efficient frequency allocation for superconducting quantum processors using improved optimization techniques

Building on previous research on frequency allocation optimization for superconducting circuit quantum processors, this work incorporates several techniques to improve overall solution quality. Here, we introduce constraints and imposed edgewise differences help to improve the optimization results. We also introduce optimization variables for the orientation of each edge, defined as the direction from the control qubit to the target qubit, to be chosen during optimization. To scale up to larger processors, multimodule designs are employed with various boundary conditions, thereby enhancing the collective yield. These enhancements allow for greater flexibility in processor design by eliminating the need for handpicked orientations. We support the efficient assembly of large processors with dense connectivity by choosing the best boundary conditions. Examples demonstrate that, at low computational cost, this optimization approach finds a frequency configuration for a square chip with over 1000 qubits and over 10% yield at much larger dispersion levels than required by previous approaches.

Zhang, Zewen [Argonne National Laboratory (ANL), A↗
DOE OSTI2017

Optimized pulses for Raman excitation through the continuum: Verification using the multiconfigurational time-dependent Hartree-Fock method

Here, we have verified a mechanism for Raman excitation of atoms through continuum levels previously obtained by quantum optimal control using the multiconfigurational time-dependent Hartree-Fock (MCTDHF) method. For the optimal control, which requires running multiple propagations to determine the optimal pulse sequence, we used the computationally inexpensive time-dependent configuration interaction singles (TDCIS) method. TDCIS captures all of the necessary correlation of the desired processes but assumes that ionization pathways reached via double excitations are not present. MCTDHF includes these pathways and all multiparticle correlations in a set of time-dependent orbitals. The mechanism that was determined to be optimal in the Raman excitation of the Ne 1s 2 2s 2 2p 5 3p 1 valence state via the metastable 1s 2 2s 1 2p 6 3p 1 resonance state involves a sequential resonance-valence excitation. First, a long pump pulse excites the core-hole state, and then a shorter Stokes pulse transfers the population to the valence state. This process represents the first step in a multidimensional x-ray spectroscopy scheme that will provide a local probe of valence electronic correlations. Although at the optimal pulse intensities at the TDCIS level of theory the MCTDHF method predicts multiple ionization or excitation ionization of the atom, at slightly lower intensities (reduced by a factor of about 4) the TDCIS mechanism is shown to hold qualitatively. Quantitatively, the MCTDHF populations are reduced from the TDCIS calculations by a factor of 4.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Three-dimensional alignment of asymmetric-top molecules induced by polarization-shaped optimal laser pulses

Optimal control simulation is applied to numerically design nonresonant laser pulses that maximize the degrees of three-dimensional (3D) alignment of SO 2 using the lowest-order induced-dipole interaction. In our trials, combinations of more than two mutually orthogonal, linearly polarized subpulses are always obtained as the optimal solutions. Each subpulse in the optimal pulses impulsively excites the rotational wave packet. The optimal pulses effectively cooperate with the rotational dynamics up to only a few partial revival timings owing to the rotational dephasing that determines the effective control periods. The control mechanisms are interpreted in terms of the time derivatives of the expectation values of the squares of the direction cosines, that of the rotational energy, and the interplay between them. We find a special and important role of the last subpulses as they align the molecular axes using the interaction through the two smallest polarizability components, while the other subpulses excite the rotational wave packet mainly through the largest polarizability component. Lastly, the control pulses composed of the specified number of subpulses are also numerically optimized by actively utilizing the instantaneous penalty to systematically show the superiority of the use of more than two subpulses over that of two subpulses, the latter of which leads to the saturation of the degree of 3D alignment as a function of total fluence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Equidistant optimization of elliptical superconducting rf standing wave cavities

A record accelerating rate was achieved earlier in standing wave (SW) SRF cavities when their shape was optimized for a lower peak surface magnetic field sacrificing the peak surface electric field. In view of new materials with higher limiting magnetic fields, expected for SRF cavities, in the first line of the Nb 3 Sn, the approach to optimization of cavity shape should be revised. A method of equidistant optimization, offered earlier for traveling wave (TW) cavities is applied to SW cavities. It is shown here that without limitation by magnetic field, the maximal accelerating rate is defined not only by limitations of the electric field but to a significant degree by the cavity shape. For example, for a cavity with the aperture radius of $R$ $a$ = 35 mm, the minimal ratio of the peak surface electric field to the accelerating rate is about $E$ pk / $E$ acc = 1.54. So, with the maximal surface field experimentally achieved $E$ pk ~ 125 MV / m, the maximal achievable accelerating rate is about 80 MeV / m even if there are no restrictions by the magnetic field. Optimized cavity shapes with and without limitations by a magnetic field are presented. Another opportunity—optimization for a low magnetic field, is opening for the same material, Nb 3 Sn, with the purpose of having a high-quality factor and increased accelerating rate that can be used for industrial linacs with cryocooler-based cooling scheme.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Unified, Geometric Framework for Nonequilibrium Protocol Optimization

Controlling thermodynamic cycles to minimize the dissipated heat is a long-standing goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems. Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions in a minimally dissipative fashion. These protocols optimally transport a system along paths through the space of probability distributions that minimize the dissipative cost of a transformation. Furthermore, we show that the thermodynamic metric—determined via a linear response approach—can be directly derived from the same objective function that is optimized in the optimal transport problem, thus providing a unified perspective on thermodynamic geometries. As a result, we investigate this unified geometric framework in two model systems and observe that our procedure for optimizing control protocols is robust beyond linear response.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimal Twirling Depth for Classical Shadows in the Presence of Noise

The classical shadows protocol is an efficient strategy for estimating properties of an unknown state p using a small number of state copies and measurements. In its original form, it involves twirling the state with unitaries from some ensemble and measuring the twirled state in a fixed basis. It was recently shown that for computing local properties, optimal sample complexity (copies of the state required) is remarkably achieved for unitaries drawn from shallow depth circuits composed of local entangling gates, as opposed to purely local (zero depth) or global twirling (infinite depth) ensembles. Here, we consider the sample complexity as a function of the depth of the circuit, in the presence of noise. We find that this noise has important implications for determining the optimal twirling ensemble. Under fairly general conditions, we (i) show that any single-site noise can be accounted for using a depolarizing noise channel with an appropriate damping parameter f, (ii) compute thresholds f th at which optimal twirling reduces to local twirling for Pauli operators, (iii) nth order Renyi entropies (n ≥2), and (iv) provide a meaningful upper bound t max on the optimal circuit depth for any finite noise strength f, which applies to observables and entanglement entropy measurements. In conclusion, these thresholds strongly constrain the search for optimal strategies to implement shadow tomography and are easily tailored to the experimental system at hand.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Random coordinate descent: A simple alternative for optimizing parameterized quantum circuits

Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learning is not directly applicable in this setting due to the collapse of quantum states after measurements. Thus, gradient estimations constitute a significant overhead in a gradient-based optimization of such quantum circuits. This paper introduces a random coordinate descent algorithm as a practical and easy-to-implement alternative to the full gradient descent algorithm. This algorithm only requires one partial derivative at each iteration. Motivated by the behavior of measurement noise in the practical optimization of parameterized quantum circuits, this paper presents an optimization problem setting that is amenable to analysis. Under this setting, the random coordinate descent algorithm exhibits the same level of stochastic stability as the full gradient approach, making it as resilient to noise. The complexity of the random coordinate descent method is generally no worse than that of the gradient descent and can be much better for various quantum optimization problems with anisotropic Lipschitz constants. Theoretical analysis and extensive numerical experiments validate our findings. Published by the American Physical Society 2024

Ding, Zhiyan (ORCID:000000018863403X)↗
DOE OSTI2025

Optimizers for stabilizing likelihood-free inference

A growing number of applications in particle physics and beyond use neural networks as unbinned likelihood ratio estimators applied to real or simulated data. Precision requirements on the inference tasks demand a high-level of stability from these networks, which are affected by the stochastic nature of training. We show how physics concepts can be used to stabilize network training through a physics-inspired optimizer. In particular, the energy conserving descent (ECD) optimization framework uses classical Hamiltonian dynamics on the space of network parameters to reduce the dependence on the initial conditions while also stabilizing the result near the minimum of the loss function. We develop a version of this optimizer known as , which has few free hyperparameters with limited ranges guided by physical reasoning. We apply to representative likelihood-ratio estimation tasks in particle physics and find on average that it out-performs the widely used Adam optimizer. We expect that ECD will be a useful tool for wide array of data-limited problems, where it is computationally expensive to exhaustively optimize hyperparameters and mitigate fluctuations with ensembling.

Monte Carlo methods↗
DOE OSTI2022

A Comprehensive Approach towards Multi-Objective EMI Filter Design Optimization in High-Frequency SiC-Based Motor Drives

With rapid penetration of high-frequency, highdensity power electronics into the industry, controlling the system generated EMI becomes one of the major design challenges. Designing practical optimized EMI filters require simultaneous consideration of multiple aspects - including electromagnetic couplings, magnetic materials, practical component parasitics and their impacts at high-frequencies, and also the overall manufacturing costs. The multi-dimensional nature of the problem often results in overcompensated designs that hurt the system efficiency and power density. This paper focuses on a comprehensive design platform (developed in MATLAB) towards designing high-density, optimized, highly efficient EMI filters by analyzing the generated noise spectrum and evaluating a vast array of potential solutions before outputting an optimized filter solution. The proposed tool includes database-based component selection, optimal topology selection, multistage filter design, overall optimization for volume, mass, cost and total loss. The proposed filter has been thoroughly evaluated in a PSIM simulation environment emulating the hardware in CRD300DA12EXM3, a 300kW three-phase inverter from Wolfspeed with an RL load representing a motor drive application. Furthermore, a 20kHz switching frequency is chosen and impacts of the motor high-frequency characteristics on the generated EMI noise spectrum of the overall system have been analyzed.

42 ENGINEERING↗
DOE OSTI2019

Model-based Hyperparameter Optimization of Convolutional Neural Networks for Information Extraction from Cancer Pathology Reports on HPC

Finding optimal hyperparameters is necessary to identify the best performing deep learning models but the process is costly. In this paper, we applied model-based optimization, also known as Bayesian optimization, using the CANDLE framework implemented on a High-Performance Computing environment. As a use case we selected information extraction from cancer pathology reports using a multi-task convolutional neural network, and hierarchical convolutional attention network to be optimized. We utilized a synthesized text corpus of 8,000 training cases and 2,000 validation cases with four types of clinical task labels including primary cancer site, laterality, behavior, and histological grade. We demonstrated that hyperparameter optimization using the CANDLE framework is a feasible approach with respect to both scalability and clinical task performance.

Yoon, Hong-Jun↗
DOE OSTI2019

Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems

Designing a neuromorphic computing system involves selection of several hyperparameters that not only affect the accuracy of the framework, but also the energy efficiency and speed of inference and training. These hyperparameters might be inherent to the training of the spiking neural network (SNN), the input/output encoding of the real-world data to spikes, or the underlying neuromorphic hardware. In this work, we present a Bayesian-based hyperparameter optimization approach for spiking neuromorphic systems, and we show how this optimization framework can lead to significant improvement in designing accurate neuromorphic computing systems. In particular, we show that this hyperparameter optimization approach can discover the same optimal hyperparameter set for input encoding as a grid search, but with far fewer evaluations and far less time. We also show the impact of hardware-specific hyperparameters on the performance of the system, and we demonstrate that by optimizing these hyperparameters, we can achieve significantly better application performance.

Parsa, Maryam↗
DOE OSTI2018

Coordinated Optimal Control of Constrained DERs

In this paper, a coordinated optimal control is designed to simultaneously satisfy multiple power system objectives at the distribution network level. With high penetration of renewables, traditional generation and control may not be enough to compensate for intermittent changes in net load. Dynamic coordination of battery storage devices and demand response at aggregated distributed energy resource (DER) sites becomes necessary to maintain power system operation, however, each dynamic power component operates at different time scales. Frequency stabilization can be achieved by designing optimal controls for traditional generation, storage devices and demand response. The proposed optimal controls naturally render frequency separation of these load balancing mechanisms. Given a 24-hour net load forecast, optimal tracking control inputs are designed subject to constraints of selected system elements to ensure optimal performance of the power system. Simulation results are presented and discussed.

Harvey, Roland↗
DOE OSTI2012

Optimizing Process-to-Core Mappings for Application Level Multi-dimensional MPI Communications

Multi-dimensional MPI communications, where MPI communications have to be performed in each dimension of a Cartesian communicator, have been frequently used in many of today's high performance computing applications. While individual MPI collective communications for regular communicators with a one-dimensional linear-ranking of processes have been extensively studied and optimized, little optimizations have been performed for multi-dimensional MPI collective communications on multi-dimensional Cartesian topology. In this paper, we optimize multi-dimensional MPI collective communications for SMP and multi-core systems at the application level. We show that the default Cartesian topology built by the state-of-the-art MPI implementations produce sub-optimal performance for multi-dimensional MPI collective communications. We design optimal process-to-core mapping schemes for Cartesian communicators to minimize the total inter-node communications. The proposed technique improves the performance by up to 80% over the default Cartesian topology built by Cray's MPI implementation MPT 3.1.02 on the world's current second fastest supercomputer Jaguar at Oak Ridge National Laboratory.

Computer Science↗
DOE OSTI2020

Fourier Analysis-Based Evolutionary Multi-Objective Multiphysics Optimization of Liquid-Cooled Heat Sinks

Optimal heat dissipation in power modules can significantly increase their power density. Removing the generated heat is critical for capturing the benefits of advanced semiconductor materials and improving the reliability of the device operation. This article proposes a design optimization method for liquid-cooled heat sinks that use a Fourier analysis-based tool and an evolutionary optimization algorithm to optimize the heat sink geometry for specified objectives. The optimized heat sink geometry is then compared with state-of-the-art solutions in literature based on finite element analysis of different designs. The proposed methodology can develop complex geometries that outperform the conventional heat sink geometries.

Sahu, Raj↗
DOE OSTI2019

Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information

A parameter tuning based co-optimization scheme for the hybrid electric vehicles (HEV) powertrain system is designed to maximize the fuel efficiency. The optimization controlled input parameters are chosen based on sensitivity study of powertrain control parameters. The vehicle to vehicle (V2V) and vehicle to infrastructure information is another optimization input, to have the driving conditions taking in to considerations for maximizing fuel efficiency. The catalyst temperature is considered as an additional constraint as the speed to reach light-off temperature should not decrease during optimized operation. Neural network is used to develop a simplified yet equivalent model for the optimization problem model. We have achieved an average of 9.22% fuel savings for a random driving cycle on a Toyota Prius test model.

V2V, Cooptimization↗

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