Search NASA⌕ Search

SEARCH · Search NASA

Results for “Quantum Optimization”

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.

At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Filtered Rayleigh-Ritz is all you need

Recent work has shown that the (block) Lanczos algorithm can be used to extract approximate energy spectra and matrix elements from (matrices of) correlation functions in quantum field theory, and identified exact coincidences between Lanczos analysis methods and others. In this work, we note another coincidence: the Lanczos algorithm is equivalent to the well-known Rayleigh-Ritz method applied to Krylov subspaces. Rayleigh-Ritz provides optimal eigenvalue approximations within subspaces; we find that spurious-state filtering allows these optimality guarantees to be retained in the presence of statistical noise. We explore the relation between Lanczos and Prony's method, their block generalizations, generalized pencil of functions (GPOF), and methods based on the generalized eigenvalue problem (GEVP), and find they all fall into a larger "Prony-Ritz equivalence class", identified as all methods which solve a finite-dimensional spectrum exactly given sufficient correlation function (matrix) data. This equivalence allows simpler and more numerically stable implementations of (block) Lanczos analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quantum limits of superconducting-photonic links and their extension to millimeter waves

Photonic addressing of superconducting circuits has been proposed to overcome wiring complexity and heat-load challenges. However, such superconducting-photonic links suffer from an efficiency-noise trade-off that limits scalability. This trade-off arises because increasing power conversion efficiency entails reducing optical power, which makes the converted signal susceptible to shot noise. We analyze this trade-off and find that the laser-driven qubit gate infidelity scales inversely with the number of photons used. While methods such as nonlinear detection or squeezed light could mitigate this effect, we consider generating higher-frequency electrical signals, such as millimeter waves (100 GHz), using laser light. At these higher frequencies, circuits have higher operating temperatures and cooling power budgets and alleviate constraints posed by the trade-off. Therefore, we demonstrate an optically driven cryogenic millimeter-wave source with a maximum power efficiency of 8 ×10 −5 that can generate a maximum of 0.7 μ⁢W of 80 GHz power, with a 1200-thermal-photon equivalent of added noise at 4K. Using this source, we perform frequency-domain spectroscopy of superconducting NbTiN resonators at 80–90 GHz. Our results show a promising approach to lessen the efficiency-noise constraints on superconducting-photonic links, while leveraging the benefits of photonic signal delivery. Further optimization of power efficiency and noise at high frequencies could make scalable photonic control of superconducting qubits viable at temperatures exceeding 1K.

Optoelectronics↗
DOE OSTI2025

Nanodiamonds in Advancing Biomedical Sciences

Nanodiamonds (NDs), tetrahedral carbon frameworks with size ranging from 1 to 100 nanometers, have gained growing attention in recent years due to their distinct optical, thermal, and mechanical properties compared to other carbon nanomaterials (e.g., graphene, carbon nanotubes, carbon dots). Combined with a high surface-to-volume ratio and tunable and chemically versatile surfaces, these support broad applications across catalysis, electronics, and life sciences. Moreover, the biocompatible characteristics of NDs enable their controllable interfacial interactions with biological systems, positioning them as excellent candidates for advancing cutting-edge biomedical sciences, particularly through the engineering of efficient material-biointerfaces that facilitate optimal interactions with biological systems. Among various forms of NDs, fluorescent nanodiamonds (FNDs) have emerged as some of the most impactful and rapidly advancing materials, demonstrating strong potential in ultrasensitive spin-enhanced bioimaging, high-precision biosensing, traceable drug delivery, and quantum-enabled biomedical technologies. This Perspective introduces the key principles underlying NDs and FNDs, including their structural properties, synthesis methods, and surface functionalization strategies. It also highlights emerging biomedical applications of NDs and FNDs, with particular emphasis on neurological disorders. Last, the article discusses current challenges in advancing NDs as a multifunctional platform for neural therapies with translational potential toward clinical trials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Iodine recombination in xenon solvent: Clusters in the gas to liquid-like state transition

Supercritical fluids (SCFs) have attracted significant attention as solvents for chemical reactions due to their unique properties, such as high diffusivity, low viscosity, and tunable solvation properties. These properties profoundly influence reaction kinetics and are often attributed to the formation of molecular clusters within SCFs. To study the effect of supercritical solvent on chemical reactivity and dynamics of reactions, one needs to understand the dynamics of clusters in supercritical fluid. Extensive experiments on the photodissociation and recombination of iodine in supercritical fluids served as a model system for understanding these effects. Experimental studies have been complemented by theoretical and computational investigations, which mostly employ Monte Carlo or empirical molecular dynamics simulations. However, computational studies using non-reactive force fields and ab initio approaches present challenges in capturing reactive processes at larger scales within supercritical fluids. Here, in this work, we developed the ReaxFF parameters by training against quantum mechanics data. ReaxFF reactive force field based molecular dynamics simulations were performed, studying the dynamics of a xenon solvent and cage effect at different thermodynamic conditions for the iodine recombination reaction. We show that the conditions near the critical point are the optimal conditions to study the cage effect. We show that the average lifetime of xenon clusters ranging between 5 and 11 ps is comparable to iodine geminate recombination. Our simulation results of iodine recombination in xenon solvent demonstrate the higher probability of iodine molecule formation in the presence of xenon clusters. Finally, we show that the supercritical condition exhibits the highest recombination rate for iodine atoms.

Cage effect↗
DOE OSTI2025

Strategies for Superconducting Transmon Qubits with Millisecond T$_1$ Relaxation Time

Significant strides have been made in extending qubit lifetimes by mitigating lossy materials at various surfaces and interfaces of superconducting transmon qubits. We have recently demonstrated a five-fold improvement of T$_1$ energy relaxation time by encapsulating the Nb surface to prevent native oxide formation. To further extend qubit coherence to millisecond timescales and beyond, we are actively exploring novel strategies. Such strategies include substrate preparation, alternative materials as low loss platforms, novel non-oxide forming low loss capping layers, optimized qubit designs & qubit packaging, and optimized Josephson junction materials, processing, and design. Qubit relaxation time T$_1$ measurements will be reported with best T$_1$’s in excess of a millisecond. This material is based upon work supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Superconducting Quantum Materials and Systems Center (SQMS) under contract number DE-AC02-07CH11359.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A Quantum Mechanical MP2 Study of the Electronic Effect of Nonplanarity on the Carbon Pyramidalization of Fullerene C 60

Among C 60 ’s diverse functionalities, its potential application in CO 2 sequestration has gained increasing interest. However, the processes involved are sensitive to the molecule’s electronic structure, aspects of which remain debated and require greater precision. To address this, we performed structural optimization of fullerene C 60 using the QM MP2/6–31G* method. The nonplanarity of the optimized icosahedron is characterized by two types of dihedral angles: 138° and 143°. The 120 dihedrals of 138° occur between two hexagons intersecting at C–C bonds of 1.42 Å, while the 60 dihedrals of 143° are observed between hexagons and pentagons at C–C bonds of 1.47 Å. NBO analysis reveals less pyramidal sp 1.78 hybridization for carbons at the 1.42 Å bonds and more pyramidal sp 2.13 hybridization for the 1.47 Å bonds. Electrostatic potential charges range from -0.04 a.u. to 0.04 a.u. on the carbon atoms. Second-order perturbation analysis indicates that delocalization interactions in the C–C bonds of 1.42 Å (143.70 kcal/mol) and 1.47 Å (34.98 kcal/mol) are 22% and 38% higher, respectively, than those in benzene. MP2/Def2SVP calculations yield a correlation energy of 13.49 kcal/mol per electron for C 60 , slightly higher than the 11.68 kcal/mol for benzene. However, the results from HOMO-LUMO calculations should be interpreted with caution. This study may assist in the rational design of fullerene C 60 derivatives for CO 2 reduction systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The right conditions for high-precision dynamic temperature and heat capacity measurement via pyrometry and conductivity

The pursuit of accurate bulk temperature T under extreme conditions has been a long-standing goal of the high pressure science community, complicated by a lack of data to inform models. To reach these extremely high-pressure, high-temperature (high P − T) conditions, a combination of dynamic and heated static experiments (e.g., diamond or gem anvil cel experiments) are used. For example, in a diamond anvil cell (DAC) experiment, a sample placed in the DAC is first pressurized. Following pressurization, the sample T is increased either by heating the entire DAC (usually using resistive heating, and limited to ∼1000K) or by applying intense laser power to the sample surfaces. In a dynamic experiment, the process of pressurizing the sample also heats it. In the case of shock physics experiments, such heating is substantial, easily reaching thousands of Kelvin; in our work we have seen T ∼17000K. Most methods of measuring temperature at ambient are not compatible with experiments under these high-pressure, high-temperature conditions: thermocouples break, melt, or have conductivity properties that differ from ambient where they are calibrated; thermometers would melt; both are too slow. As a result most methods are based on non-contact techniques such as x-ray diffraction broadening, neutron scattering, or optical methods. Of these, optical methods using the visible and near-infrared region of the spectrum are the most commonly used as the sources and detectors are readily available. In the case of optical methods the optical depth, and therefore the measurement location, is limited to the surface. When a window or anvil material is used, heat flows from the sample into the window/anvil. Likewise, if the sample undergoes a change in thermodynamic state, such as expansion upon release, different T may be expected. As a result, the surface or apparent temperature T app measurement will differ from the bulk or interior temperature that is desired. This surface measurement must be related to the bulk measurement using thermal transport models and material models. While it is tempting to conclude that one should just use x-ray methods that directly probe the interior, even these methods have been shown to depend on thermal transport and material models. Regardless of the method used to create the high P − T condition, therefore, we must understand the role of thermal transport and material models upon our interpretation of the T measurement, as well as the errors and uncertainties associated with the choice of models used in the analysis. This is a substantial area of research and this paper is by no means a complete survey of the relevant sources of uncertainty. For example, we have yet to begin to address alternate transport models in a detailed manner (e.g., Tan-Ahrens), or the many models that use additional layers to approximate melting, turbulence, or epitaxial phenomena). Likewise, we have not explored the impact upon uncertainty of thermal models that use temperature-dependent thermal transport coefficients, or the wide range of material models that can be applied. Instead, this paper focuses on using one simple model, the Urtiew-Grover model, to understand the sources of error in T measurement so that we may identify how best to focus future research efforts to return the best improvements and avoid working on over-optimizing a single type of measurement. To this end, we work through some of the best and worst case scenarios for T measurement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Simplified projection on total spin zero for state preparation on quantum computers

Here, we introduce a simple algorithm for projecting on J = 0 states of a many-body system by performing a series of rotations to remove states with angular momentum projections greater than zero. Existing methods rely on unitary evolution with the two-body operator J 2 , which when expressed in the computational basis contains many complicated Pauli strings requiring Trotterization and leading to very deep quantum circuits. Our approach performs the necessary projections using the one-body operators J x and J z . By leveraging the method of Cartan decomposition, the unitary transformations that perform the projection can be parametrized as a product of a small number of two-qubit rotations, with angles determined by an efficient classical optimization. Given the reduced complexity in terms of gates, this approach can be used to prepare approximate ground states of even-even nuclei by projecting onto the J = 0 component of deformed Hartree-Fock states. We estimate the resource requirements in terms of the universal gate set {H,S, CNOT ,T} and briefly discuss a variant of the algorithm that projects onto J = 1/2 states of a system with an odd number of fermions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Charge-induced atomic strain as a predictor of structural phase transformation in rare-earth intermetallics

We present a descriptor based on charge-induced atomic strain in crystalline lattices for predicting structural phase transformations in rare-earth intermetallic compounds containing lanthanides and transition metals. The charge-induced local atomic strain was obtained from structural optimization of experimentally known crystalline phases using state of the art density-functional theory methods. The predictive power of the descriptor was evaluated on 𝑅⁢𝐸 2 ⁢In (𝑅𝐸 = rare earth) compounds, a class known for diverse phase transformations. We show that incorporating quantum-mechanical effects—such as local charge distribution, bonding, symmetry, and electronic structure—enhances the robustness of the descriptor. To gain further insight, we analyzed phononic and electronic behavior in Y 2 ⁢In and demonstrated that experimental phase transformations are captured only when atomic strain effects are included. The descriptor was further used to predict structural phase changes in (Y⁢b 1–𝑥 ⁢E⁢r 𝑥 ) 2 ⁢In and G⁡d 2 ⁡(I⁢n 1–𝑥⁢ A⁢l 𝑥 ), with predictions confirmed by x-ray powder diffraction. Here, while the current study is focused on lanthanide-based intermetallics, the underlying principles of the descriptor suggest potential applicability to other closely related classes of rare-earth intermetallics.

Density functional theory↗
DOE OSTI2025

Studies as a function of different ALD capping layers on cavity losses for QIS and accelerators

Niobium-based bulk SRF cavities have demonstrated exceptional performance. To further improve niobium cavity performance, we present studies involving a novel surface engineering process designed to prevent the formation of amorphous niobium oxides on the surface. This is achieved by encapsulating the niobium surface using thermal Atomic-Layer-Deposition (ALD). This technique has been shown to enhance the properties of niobium cavities and 2D resonators. This study not only aims to improve SRF performance at high fields but also has the potential to enhance the quality factor in low-field regimes, particularly for quantum applications. For the method to be effective, it is essential to have a clean interface between the encapsulating layer and the bulk niobium. Achieving this requires a uniform coating across the entire cavity surface and efficient removal of the underlying niobium oxides. To optimize the process, a variety of material characterization tools have been utilized to refine parameters such as thin film thickness and annealing conditions. Cavity RF measurements were performed at the vertical test facilities at FNAL to assess the Q vs. Eacc curves for accelerator applications, as well as at the SQMS dilution refrigerators to investigate the full two-level system (TLS) losses at milliKelvin and single-photon levels. Based on the results, we characterized the loss of different oxides in various regimes and applications, comparing them to natural niobium pentoxide.

Grassellino, L. [Fermilab]↗
DOE OSTI2024

GDB-9-Ex_EOM-CCSD: Dataset containing Equation of Motion Coupled Cluster (EOM-CCSD) calculations for organic molecules of the GDB-9-Ex dataset.

This dataset contains data-intensive quantum chemical electronic structure calculations for 80,593 organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Equation of Motion Coupled Cluster (EOM-CCSD) first principles method using the ORCA software. It provides UV-vis spectra calculations of molecules with a high level of accuracy. The optical spectra behavior was collected based on the optimized molecular geometries in the DFTB method with 3ob parameters. All calculations utilized the def2-TZVP basis sets with the auxiliary def2/J and def2-TZVP/C basis sets. The similarity-transformed EOM-CCSD method that used domain-based local pair natural orbitals (DLPNO) approximation which constitutes the STEOM-DLPNO-CCSD method was used. This method is based on the STEOM approach and was found to make accurate predictions of transition energies for organic molecules. For the excitation energy calculations, the lowest 50 excitation states were calculated.

AI dataset↗
DOE OSTI2025

Slow Electron Spin Relaxation at Ambient Temperatures with Copper Coordinated by a Rigid Macrocyclic Ligand

Paramagnetic transition metal complexes can serve as quantum bits, storing phase information through unpaired electrons. Despite their promise, these systems often require low temperatures and tend to rapidly decohere. Recent efforts have sought to improve longitudinal relaxation (T 1 ), which provides an upper limit for phase coherence (T m ), by investigating existing literature compounds with reduced vibrational coupling and orbital angular momentum. However, synthetic strategies for improving T 1 through novel ligand design have remained scant. Here, we disclose the synthesis of a new modular macrocyclic ligand framework with four nitrogen donors (N 4 ) derived from phenanthroline that supports room-temperature coherent Cu(II) spin centers. The optimized complex more than doubles the T 1 over the next best Cu(II)-N 4 compound and exhibits a room temperature coherence time (T m ) of 0.28 μs, close to previously reported values. This performance enhancement arises from a tight binding site with short Cu–N distances, resulting in a stronger ligand field and reduced thermal accessibility of symmetric vibrational modes. This work demonstrates a practical approach to enabling spin coherence at room temperature, a factor critical to accessing relevant quantum bits and biological sensors, through a designer macrocyclic ligand platform.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Heterovalent Substitution of K 2 SrP 2 O 7 :Cr 3+ to Achieve Anti-Thermal-Quenching Broadband Near-Infrared Luminescence

Broadband near-infrared (NIR) light sources based on phosphor-converted light-emitting diodes are highly desirable for biochemical analysis and medical diagnosis applications. However, thermal quenching remains a demanding challenge for developing efficient NIR phosphors. Herein, we report the enhancement of both quantum efficiency and thermal stability in Cr 3+ -activated K 2 SrP 2 O 7 phosphors through a heterovalent substitution strategy by replacing Sr 2+ with Al 3+ in K 2 Sr 1–x Al x P 2 O 7 (0.05 ≤ x ≤ 0.2) to obtain optimized broadband NIR emission. Structural modulation via Al 3+ substitution leads to the optimized composition, K 2 Sr 0.88 Al 0.1 P 2 O 7 :0.02Cr 3+ , which emits across a broad NIR range of 650–1100 nm peaking at 807 nm with a full width at half-maximum of ∼130 nm under 448 nm excitation. Remarkably, its emission intensity at 150 °C remains 120% of the initial value at room temperature, demonstrating a rare antithermal-quenching behavior. Temperature-dependent XRD studies further reveal that Al 3+ substitution effectively suppresses lattice expansion at elevated temperatures, indicating enhanced lattice stability under thermal excitation. Detailed structural and spectral analyses show that the substitution enhances local site symmetry, reduces electron–phonon coupling, increases thermally induced absorption probability, and fortifies energetic barriers against nonradiative transitions. These synergistic effects collectively endow this NIR phosphor with a superior thermal stability. Furthermore, NIR light-emitting diodes fabricated with this phosphor exhibit strong potential for applications in information identification, nondestructive detection, and night vision technologies. This study demonstrates a local structure engineering strategy for designing thermally robust Cr 3+ -activated NIR phosphors, offering valuable insights into material discovery and NIR spectroscopy device development.

Cr3+↗
DOE OSTI2024

Advanced CO 2 Capture Solvent Systems for Dynamic Power Generation: Quarterly Research Performance Progress Report, QR4 (Q4FY24)

We developed an integrated Computational Fluid Dynamics (CFD) model to simulate the multi-physics coupled cooling process of mixed gas by cold water within a Direct Contact Cooler (DCC) equipped with a rotating packing bed (RPB). The model captures the interactions between fluid dynamics, heat transfer, mass transport, and phase transitions, while accounting for key operational variables such as RPB rotational speed and the mass flow rates of both liquid and gas. The CFD model has been validated using experimental data, specifically by comparing predicted outflow gas and liquid temperatures to measured results. Our findings demonstrate the significant effects of RPB rotational speed and mass flow rates on cooling performance, providing valuable insights for optimizing DCC efficiency in industrial applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Photosynthetic responses of switchgrass to light and CO 2 under different precipitation treatments

Switchgrass ( Panicum virgatum L .) is a prominent bioenergy crop with robust resilience to environmental stresses. However, our knowledge regarding how precipitation changes affect switchgrass photosynthesis and its responses to light and CO 2 remains limited. To address this knowledge gap, we conducted a field precipitation experiment with five different treatments, including −50%, −33%, 0%, +33%, and +50% of ambient precipitation. To determine the responses of leaf photosynthesis to CO 2 concentration and light, we measured leaf net photosynthesis of switchgrass under different CO 2 concentrations and light levels in 2020 and 2021 for each of the five precipitation treatments. We first evaluated four light and CO 2 response models (i.e., rectangular hyperbola model, nonrectangular hyperbola model, exponential model, and the modified rectangular hyperbola model) using the measurements in the ambient precipitation treatment. Based on the fitting criteria, we selected the nonrectangular hyperbola model as the optimal model and applied it to all precipitation treatments, and estimated model parameters. Overall, the model fit field measurements well for the light and CO 2 response curves. Precipitation change did not influence the maximum net photosynthetic rate ( P max ) but influenced other model parameters including quantum yield ( α ), convexity ( θ ), dark respiration ( Rd ), light compensation point ( LCP ), and saturated light point ( LSP ). Specifically, the mean P max of five precipitation treatments was 17.6 μmol CO 2 m −2 s −1 , and the ambient treatment tended to have a higher P max . The +33% treatment had the highest α , and the ambient treatment had lower θ and LCP , higher Rd , and relatively lower LSP . Furthermore, precipitation significantly influenced all model parameters of CO 2 response. The ambient treatment had the highest P max , largest α , and lowest θ , R d , and CO 2 compensation point LCP . Overall, this study improved our understanding of how switchgrass leaf photosynthesis responds to diverse environmental factors, providing valuable insights for accurately modeling switchgrass ecophysiology and productivity.

09 BIOMASS FUELS↗
DOE OSTI2024

Harnessing Quantum Capacitance in 2D Material/Molecular Layer Junctions for Novel Electronic Device Functionality

Two-dimensional (2D) materials promise advances in electronic devices beyond Moore’s scaling law through extended functionality, such as non-monotonic dependence of device parameters on input parameters. However, the robustness and performance of effects like negative differential resistance (NDR) and anti-ambipolar behavior have been limited in scale and robustness by relying on atomic defects and complex heterojunctions. In this paper, we introduce a novel device concept that utilizes the quantum capacitance of junctions between 2D materials and molecular layers. We realized a variable capacitance 2D molecular junction (vc2Dmj) diode through the scalable integration of graphene and single layers of stearic acid. The vc2Dmj exhibits NDR with a substantial peak-to-valley ratio even at room temperature and an active negative resistance region. The origin of this unique behavior was identified through thermoelectric measurements and ab initio calculations to be a hybridization effect between graphene and the molecular layer. The enhancement of device parameters through morphology optimization highlights the potential of our approach toward new functionalities that advance the landscape of future electronics.

2D materials↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Programmable Cryogenic Memory in a Ge/GeSi Heterostructure

Programmable memory components that operate optimally at cryogenic temperatures are essential for cryogenic computing architectures that seek to implement computing-in-memory. In this work, we demonstrate highly programmable memory in a Ge/GeSi heterostructure field-effect transistor (HFET). To operate, the HFET is gated to introduce positive carriers within the Ge quantum well, creating a high-conductance state. We show that this device can be set to a low-conductance state by sweeping a negative bias on the device drain, and reset it to its high-conductance state by sweeping a more positive bias on the device gate, thereby creating memory. We then determine that the device can be programmed within a 103 range of conductances using either the SET or the RESET operation. We propose that memory is achieved through charge trapping as carriers tunnel out of the quantum well, and that altering the density and spatial distribution of carriers modulates the device conductance. This mechanism exhibits endurance over 1000 cycles at temperatures ≤ 25 K, suggesting that the carrier traps are located at the oxide-semiconductor interface. As a first demonstration of programmable conductance in a Ge/GeSi HFET, this work highlights the potential of group-IV HFETs to perform as analog cryogenic memory components.

cryogenic memory↗