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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Compiling Quantum Circuits for Dynamically Field-Programmable Neutral Atoms Array Processors

Dynamically field-programmable qubit arrays (DPQA) have recently emerged as a promising platform for quantum information processing. In DPQA, atomic qubits are selectively loaded into arrays of optical traps that can be reconfigured during the computation itself. Leveraging qubit transport and parallel, entangling quantum operations, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. Such reconfigurability and non-local connectivity present new challenges for compilation, especially in the layout synthesis step which places and routes the qubits and schedules the gates. In this paper, we consider a DPQA architecture that contains multiple arrays and supports 2D array movements, representing cutting-edge experimental platforms. Within this architecture, we discretize the state space and formulate layout synthesis as a satisfiability modulo theories problem, which can be solved by existing solvers optimally in terms of circuit depth. For a set of benchmark circuits generated by random graphs with complex connectivities, our compiler OLSQ-DPQA reduces the number of two-qubit entangling gates on small problem instances by 1.7x compared to optimal compilation results on a fixed planar architecture. To further improve scalability and practicality of the method, we introduce a greedy heuristic inspired by the iterative peeling approach in classical integrated circuit routing. Using a hybrid approach that combined the greedy and optimal methods, we demonstrate that our DPQA-based compiled circuits feature reduced scaling overhead compared to a grid fixed architecture, resulting in 5.1X less two-qubit gates for 90 qubit quantum circuits. These methods enable programmable, complex quantum circuits with neutral atom quantum computers, as well as informing both future compilers and future hardware choices.

Physics↗
DOE OSTI2020

Method of authenticating integrated circuits using optical characteristics of physically unclonable functions

A method and apparatus for reading unique identifiers of an integrated circuit. The unique identifiers may be physically unclonable functions (PUFs), formed by high energy ions implanted into semiconductor material of the integrated circuit. The method may include electrically or optically stimulating each of the PUFs and sensing with an optical sensor optical characteristics of resulting light emitted from the PUFs. Then the method may include comparing values associated with the optical characteristics of the PUFs with groups of stored values in a circuit database. Each of the groups of stored values may be associated with optical characteristics of PUFs of a known authentic circuit. The method may then include the controller providing verification of authenticity of the integrated circuit when each of the values associated with the optical characteristics of the PUFs match the stored values of at least one of the groups in the circuit database.

42 ENGINEERING↗
DOE OSTI2020

Performance CMOS voltage level-up shifter circuit topology using pre-drive pull-up transistors

In a first example a voltage level-shifting device includes a level-shifting stage circuit. The level-shifting stage circuit includes a first level-shifting inverter circuit to invert a buffered input signal to drive a first internal node, a second level-shifting inverter circuit to invert a buffered inverted input signal to drive a second internal node, a first pre-drive circuit that receives the buffered inverted input signal, and drives the second internal node based on the state of the buffered inverted input signal, and a second pre-drive circuit that receives the buffered input signal, and drives the first internal node based on the state of the buffered input signal.

Armstrong, Paul R.↗
DOE OSTI2022

Coolant delivery via an independent cooling circuit

A coolant delivery system for a component of a gas turbine system includes: a plurality of independent circuits of cooling channels embedded within an exterior wall of the component, each independent circuit of cooling channels including a plurality of headers and a plurality of feed tubes fluidly coupling the plurality of headers to a supply of cooling fluid; and an impingement plate connected to the exterior wall of the component by the plurality of feed tubes of the independent circuits of cooling channels, wherein, in each of the plurality of independent circuits of cooling channels, the cooling fluid flows through the plurality of feed tubes and the plurality of headers into the circuit of cooling channels only in response to a formation of a breach in the exterior wall of the component that exposes at least one of the cooling channels of the circuit of cooling channels.

Hafner, Matthew Troy↗
DOE OSTI2023

Neutralization Short-Circuiting with Weak Electrolytes Erodes the Efficiency of Bipolar Membranes

Bipolar membranes (BPMs) are critical components of a variety of electrochemical energy technologies. Many electrochemical applications require the use of buffers to maintain stable, nonextreme pH environments, yet the impact of buffers or weak acids/bases on the electrochemical behavior of BPMs remains poorly understood. Here, our data for a cell containing weak electrolytes is consistent with internal pH gradients within the anion exchange membrane (AEM) or cation exchange membrane (CEM) component of the BPM that form via ionic short-circuiting processes at open-circuit. Short-circuiting results from the coupling of co-ion crossover and parasitic neutralization and leads to buffering of the bipolar interface. This phenomenon, which we term neutralization short-circuiting, serves to erode BPM efficiency by attenuating the open-circuit membrane voltage and introducing parasitic reverse bias currents associated with weak acid/base dissociation at the interface. These findings establish a mechanistic basis for the operation of BPM cells in the presence of weak acid/base electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Quantum Circuit Cutting for Classical Shadows

Classical shadow tomography is a sample-efficient technique for characterizing quantum systems and predicting many of their properties. Circuit cutting is a technique for dividing large quantum circuits into smaller fragments that can be executed more robustly using fewer quantum resources. We introduce a divide-and-conquer circuit cutting method for estimating the expectation values of observables using classical shadows. We derive a general formula for making predictions using the classical shadows of circuit fragments from arbitrarily cut circuits and provide the sample complexity analysis for the case when observables factorize across fragments. Then, we numerically show that our divide-and-conquer method outperforms traditional uncut shadow tomography when estimating high-weight observables that act non-trivially on many qubits and discuss the mechanisms for this advantage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Robust implementation of generative modeling with parametrized quantum circuits

Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze, a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer perspective, the primary challenge lies in the solver’s stochastic nature, and their significant variance over the random initialization. Therefore, a robust comparison requires one to perform several training curves for each solver before one can reach conclusions about their typical performance. Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today. In this work, we leverage on Rigetti’s Quantum Cloud Services (QCS™) to overcome this implementation barrier, and we study the on-hardware performance of the data-driven quantum circuit learning (DDQCL) for three different state-of-the-art classical solvers, and on two-different circuit ansätze associated to different entangling connectivity graphs for the same task. Additionally, we assess the gains in performance from varying circuit depths. To evaluate the typical performance associated with each of these settings in this benchmark study, we use at least five independent runs of DDQCL towards the generation of quantum generative models capable of capturing the patterns of the canonical Bars and Stripes dataset. In this experimental benchmarking, the gradient-free optimization algorithms show an outstanding performance compared to the gradient-based solver. In particular, one of them had better performance when handling the unavoidable noisy objective function to be minimized under experimental conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Qutrit circuits and algebraic relations: A pathway to efficient spin-1 Hamiltonian simulation

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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Polynomial-Time Classical Algorithm for Noisy Quantum Circuits

We provide a polynomial-time classical algorithm for noisy quantum circuits. The algorithm computes the expectation value of any observable for any circuit, with a small average error over input states drawn from an ensemble (e.g., the computational basis). Our approach is based upon the intuition that noise exponentially damps nonlocal correlations relative to local correlations. This enables one to classically simulate a noisy quantum circuit by keeping track of only the dynamics of local quantum information. Our algorithm also enables sampling from the output distribution of a circuit in quasipolynomial time, so long as the distribution anticoncentrates. A number of implications are discussed, including a fundamental limit on the efficacy of noise mitigation strategies: For constant noise rates, any quantum circuit for which error mitigation succeeds in polynomial-time on most input states can also be classically simulated in polynomial-time on most input states. Our algorithms scale exponentially in the inverse noise rate, which is fundamental and makes them impractical for current quantum devices.

decoherence↗
DOE OSTI2023

Two Circuits for Directing and Controlling Ballistic Fluxons

Reversible logic schemes using flux solitons (fluxons) on long Josephson junctions (LJJs) have recently been proposed. The attraction of the fluxon is that it propagates ballistically along an LJJ until it encounters a change in the character of the LJJ, often a designed circuit element. Logic gates involve fluxons interacting with circuit elements and with other fluxons. However, testing of ballistic fluxon circuits requires other circuits outside the logic family to direct and control fluxon motion. Here we discuss two such non-reversible fluxon control circuits. First, the polarity filter gate is a simple non-reversible gate that allows one polarity of fluxon to pass, while reflecting the other polarity. In the off state both polarities reflect. Second, the polarity separator generalizes on the polarity filter concept and allows separation of the two fluxon polarities into different LJJs. We discuss simulations of these structures and possible applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A Laplace-Domain Circuit Model for Fault and Stability Analysis Considering Unbalanced Topology

For systems subject to unbalanced faults, analytical model building for stability assessment is a challenging task. This letter presents a straightforward modeling approach. A generalized dynamic circuit representation is achieved by use of the Laplacian transform variable s . Here, we translate the voltage and current relationship at the fault location into the relationship of three subsystems. The final circuit model is an interconnected sequence network with impedances in the Laplace domain. This circuit can be directly converted from a steady-state sequence network. This modeling procedure is illustrated by an example case of an induction motor served by a grid through a series compensated line. Electromagnetic transient simulation results demonstrate that sub-synchronous oscillations can be mitigated when a single-line to ground fault is applied at the motor terminal. Stability analysis results based on the dynamic circuit corroborate the simulation results. What's more, the derived circuit effortlessly reveals why unbalance can enhance stability.

42 ENGINEERING↗
DOE OSTI2023

Determining circuit model parameters from operation data for PV system degradation analysis: $\mathrm{PVPRO}$

Physics-based circuit parameters like series and shunt resistance are essential to provide insights into the degradation status of photovoltaic (PV) arrays. However, calculating these parameters typically requires a full current-voltage characteristic (I-V curve), the acquisition of which involves specific measurement devices and costly methods. Thus, I-V curves of the PV system level are often not available. Here this paper proposes a methodology (PVPRO) to estimate these I-V curve parameters using only operation (string-level DC voltage and current) and weather data (irradiance and temperature). PVPRO first performs multi-stage data pre-processing to remove noisy data. Next, the time-series DC data are used to fit an equivalent circuit single-diode model (SDM) to estimate the circuit parameters by minimizing the differences between the measured and estimated values. In this way, the time evolutions of the SDM parameters are obtained. We evaluate PVPRO on synthetic datasets and find an excellent estimation of both SDM and the key I-V parameters (e.g., open-circuit voltage, short-circuit current, maximum power, etc.) with an average relative error of 0.55%. The performance, especially the extracted degradation rate of parameters, is robust to various measurement noises and the presence of faults. In addition, PVPRO is applied to a 271 kW PV field system. The relative error between the real and estimated operation voltage and current is less than 1%, suggesting that degradation trends are well captured. PVPRO represents a promising open-source tool to extract the time-series degradation trends of key PV parameters from routine operation data.

14 SOLAR ENERGY↗
DOE OSTI2022

Estimating the randomness of quantum circuit ensembles up to 50 qubits

Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. To study this property of random circuits, we develop numerical protocols for estimating the frame potential, the distance between a given ensemble and the exact randomness. Our tensor-network-based algorithm has polynomial complexity for shallow circuits and is high-performing using CPU and GPU parallelism. We study 1. local and parallel random circuits to verify the linear growth in complexity as stated by the Brown–Susskind conjecture, and; 2. hardware-efficient ansätze to shed light on its expressibility and the barren plateau problem in the context of variational algorithms. Our work shows that large-scale tensor network simulations could provide important hints toward open problems in quantum information science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Integrative Circuit-Host Modeling of a Genetic Switch in Varying Environments

Synthetic biology is advancing into a new phase where real-world applications are emphasized. There is hence an urgent need for mathematical modeling that can quantitatively describe the behaviors of genetic devices in natural, fluctuating environments. We utilize an integrative circuit-host modeling framework to examine the dynamics of a genetic switch and its host cell in varying environments. For both steady-state and transient cases, we find increasing nutrient reduces the bistability region of the phase space and eventually drives the switch from bistability to monostability. In response, cellular growth and proteome partitioning experience the same transition. Antibiotic perturbations cause the similar circuit and host responses as nutrient variations. However, one difference is the trend of growth rate, which augments with nutrient but declines with antibiotic levels. The framework provides a mechanistic scheme to account for both the dynamic and static characteristics of the circuit-host system upon environmental perturbations, underscoring the intimacy of gene circuits and their hosts and elucidating the complexity of circuit behaviors arising from environmental variations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Design considerations of series type hybrid circuit breaker (S‐HCB)

Abstract The series‐type direct current (DC) hybrid circuit breaker (S‐HCB) concept was previously reported to offer better performance than solid‐state circuit breakers (SSCB) and hybrid circuit breakers (HCB). S‐HCB offers low conduction power loss like an HCB and ‐scale interruption time, which is even faster than an SSCB. It uses a pulse transformer to isolate the lower‐voltage high‐inductance power electronic circuit from the high‐voltage, low‐inductance main power loop. This paper provides analysis of the impact of the S‐HCB circuit components on the overall system performance and a scalable S‐HCB design guide for different DC system voltage and current ratings. In addition, system energy flow analysis is performed in the time domain to provide an understanding of how energy is delivered, dissipated, and released throughout the entire fault interruption process. The S‐HCB prototype was experimentally tested at 3 kV/30 A and 6 kV/150A with the results showing the interruption of the low fault current of 30 A and the high fault current of 150 A within 8 and maintaining the fault current at a near zero value for 300 to enable an arcless opening of a series mechanical switch. The key design challenges of S‐HCB at high voltage and high current ratings were discussed and possible solutions to mitigate those challenges were introduced.

Alashi, Mahmoud↗
DOE OSTI2023

A novel approach to interface high-Q Fabry–Pérot resonators with photonic circuits

The unique benefits of Fabry–Pérot resonators as frequency-stable reference cavities and as an efficient interface between atoms and photons make them an indispensable resource for emerging photonic technologies. To bring these performance benefits to next-generation communications, computation, and time-keeping systems, it will be necessary to develop strategies to integrate compact Fabry–Pérot resonators with photonic integrated circuits. In this paper, we demonstrate a novel reflection cancellation circuit that utilizes a numerically optimized multi-port polarization-splitting grating coupler to efficiently interface high-finesse Fabry–Pérot resonators with a silicon photonic circuit. This circuit interface produces a spatial separation of the incident and reflected waves, as required for on-chip Pound–Drever–Hall frequency locking, while also suppressing unwanted back reflections from the Fabry–Pérot resonator. Using inverse design principles, we design and fabricate a polarization-splitting grating coupler that achieves 55% coupling efficiency. This design realizes an insertion loss of 5.8 dB for the circuit interface and more than 9 dB of back reflection suppression, and we demonstrate the versatility of this system by using it to interface several reflective off-chip devices.

Optics↗
DOE OSTI2023

Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions

Abstract The ability to efficiently load functions on quantum computers with high fidelity is essential for many quantum algorithms, including those for solving partial differential equations and Monte Carlo estimation. In this work, we introduce the Fourier series loader (FSL) method for preparing quantum states that exactly encode multi-dimensional Fourier series using linear-depth quantum circuits. Specifically, the FSL method prepares a (Dn)-qubit state encoding the 2 Dn -point uniform discretization of aD-dimensional function specified by aD-dimensional Fourier series. A free parameter,m, which must be less thann, determines the number of Fourier coefficients, 2 D ( m + 1 ) , used to represent the function. The FSL method uses a quantum circuit of depth at most 2 ( n − 2 ) + ⌈ log 2 ( n − m ) ⌉ + 2 D ( m + 1 ) + 2 − 2 D ( m + 1 ) , which is linear in the number of Fourier coefficients, and linear in the number of qubits (Dn) despite the fact that the loaded function’s discretization is over exponentially many (2 Dn ) points. The FSL circuit consists of at most D n + 2 D ( m + 1 ) + 1 − 1 single-qubit and D n ( n + 1 ) / 2 + 2 D ( m + 1 ) + 1 − 3 D ( m + 1 ) − 2 two-qubit gates; we present a classical compilation algorithm with runtime O ( 2 3 D ( m + 1 ) ) to determine the FSL circuit for a given Fourier series. The FSL method allows for the highly accurate loading of complex-valued functions that are well-approximated by a Fourier series with finitely many terms. We report results from noiseless quantum circuit simulations, illustrating the capability of the FSL method to load various continuous 1D functions, and a discontinuous 1D function, on 20 qubits with infidelities of less than 10 −6 and 10 −3 , respectively. We also demonstrate the practicality of the FSL method for near-term quantum computers by presenting experiments performed on the Quantinuum H1-1 and H1-2 trapped-ion quantum computers: we loaded a complex-valued function on 3 qubits with a fidelity of over 95 % , as well as various 1D real-valued functions on up to 6 qubits with classical fidelities ≈99%, and a 2D function on 10 qubits with a classical fidelity ≈94%.

Physics↗
DOE OSTI2021

Probabilistic simulation of quantum circuits using a deep-learning architecture

The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasistochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits using an attention network based on a powerful neural network ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits that build Greenberger-Horne-Zeilinger and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on several system sizes. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗