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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantum computing and quantum information storage

Quantum storage, transmission, and processing is the future of information technology. Here, the promise of quantum hardware stems from the inherent complexity of an entangled quantum system—the size of the wave-function scales exponentially with the number of particles, whether represented in real space or in a parameter space. In contrast, a classical N-body system can be completely represented by only 6N variables (positions and momenta of all particles). This complexity of quantum systems creates a yet-unsolved challenge of modeling quantum systems by means of classical computing—the curse of dimensionality. Indeed, although we can easily write the Schrödinger equation for any system of interacting nuclei and electrons, we can only solve it exactly on classical computers for very small systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Learning energy-based representations of quantum many-body states

Efficient representation of quantum many-body states on classical computers is a problem of practical importance. An ideal representation of a quantum state combines a succinct characterization informed by the structure and symmetries of the system along with the ability to predict the physical observables of interest. Several machine-learning approaches have been recently used to construct such classical representations, which enable predictions of observables and account for physical symmetries. However, the structure of a quantum state typically gets lost unless a specialized is employed based on prior knowledge of the system. Moreover, most such approaches give no information about what states are easier to learn in comparison with others. Here, we propose a generative energy-based representation of quantum many-body states derived from Gibbs distributions used for modeling the thermal states of classical spin systems. Based on the prior information on a family of quantum states, the energy function can be specified by a small number of parameters using an explicit low-degree polynomial or a generic parametric family such as neural nets and can naturally include the known symmetries of the system. Our results show that such a representation can be efficiently learned from data using exact algorithms in a form that enables the prediction of expectation values of physical observables. Importantly, the structure of the learned energy function provides a natural explanation for the difficulty of learning an energy-based representation of a given class of quantum states when measured in a certain basis. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Error statistics and scalability of quantum error mitigation formulas

Quantum computing promises advantages over classical computing in many problems. Nevertheless, noise in quantum devices prevents most quantum algorithms from achieving the quantum advantage. Quantum error mitigation provides a variety of protocols to handle such noise using minimal qubit resources. While some of those protocols have been implemented in experiments for a few qubits, it remains unclear whether error mitigation will be effective in quantum circuits with tens to hundreds of qubits. In this paper, we apply statistics principles to quantum error mitigation and analyse the scaling behaviour of its intrinsic error. We find that the error increases linearly O(ϵN) with the gate number N before mitigation and sublinearly O(ϵ'N γ ) after mitigation, where γ ≈ 0.5, ϵ is the error rate of a quantum gate, and ϵ' is a protocol-dependent factor. The $\sqrt{N}$ scaling is a consequence of the law of large numbers, and it indicates that error mitigation can suppress the error by a larger factor in larger circuits. We propose the importance Clifford sampling as a key technique for error mitigation in large circuits to obtain this result.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Designing Energy-Efficient Quantum Computers Through Prediction and Reduction of Cooling Requirements for Cryogenic Electronics

Quantum computing has been identified as a “wild card” by the International Energy Agency in predicting future global data center energy usage. This is primarily because both uncertainty in the extent to which quantum computing will be adopted, and uncertainty in the power consumption of individual quantum data centers. Unlike the classical counterparts, quantum computers need to be maintained at near absolute zero, requiring energy-intensive cryogenic cooling systems. Therefore, as quantum computers scale up from existing 50 qubit technology demonstrations to the 10,000 to 100,000 qubit systems that will be able to solve complex problems, the energy consumption of both the electronics and the required cooling systems will also increase. To predict this scaling, this work analyzes the energy requirements for both computation and cooling of quantum hardware. We show that the energy requirements for cooling of quantum computers is determined by several computing system parameters, including the number and type of physical qubits, the operating temperature, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. The energy requirements can then be found based on thermal system parameters such as cooling efficiency and cryostat heat transfer. Analysis of these parameters shows that the energy required for cooling is significantly larger than that required for computation, a reversal from energy usage patterns seen in conventional computing. The results and discussions provide a road-map for creating energy efficient quantum computers through the selection of computer architectures and cryogenic system configurations that minimize cooling requirements.

energy efficiency↗
DOE OSTI2023

Numerical evidence against advantage with quantum fidelity kernels on classical data

Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able to learn certain classically intractable functions efficiently if the kernel is well aligned with the target function. In the more general case, quantum kernels are known to suffer from exponential “flattening” of the spectrum as the number of qubits grows, preventing generalization and necessitating the control of the inductive bias by hyperparameters. We show that the general-purpose hyperparameter-tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well approximated by a classical kernel, removing the possibility of quantum advantage. We provide extensive numerical evidence for this phenomenon utilizing multiple previously studied quantum feature maps and both synthetic and real data. Our results show that unless novel techniques are developed to control the inductive bias of quantum kernels, they are unlikely to provide a quantum advantage on classical data that lacks special structure.

Quantum Information, Science & Technology↗
DOE OSTI2022

Quantum time dynamics employing the Yang-Baxter equation for circuit compression

Quantum time dynamics (QTD) is considered a promising problem for quantum supremacy on near-term quantum computers. However, QTD quantum circuits grow with increasing time simulations. This study focuses on simulating the time dynamics of one-dimensional (1D) integrable spin chains with nearest-neighbor interactions. We have proved the existence of a reflection symmetry in the quantum circuit employed for simulating the time evolution of certain classes of 1D Heisenberg model Hamiltonians by virtue of the quantum Yang-Baxter equation, and how this symmetry can be exploited to compress and produce a shallow quantum circuit. With this compression scheme, the depth of the quantum circuit becomes independent of step size and only depends on the number of spins. We show that the depth of the compressed circuit is rigorously a linear function of the system size for the studied Heisenberg model Hamiltonians in the present work. As a consequence, the number of CNOT gates in the compressed circuit only scales quadratically with the system size, which allows for the simulations of time dynamics of very large 1D spin chains. We derive the compressed circuit representations for different special cases of the Heisenberg Hamiltonian. We compare and demonstrate the effectiveness of this approach by performing simulations on quantum computers.

1-dimensional spin chains↗
DOE OSTI2020

Efficient Scalable Tomography of Many-Qubit Quantum Processors

Quantum computing has the potential to realize powerful and revolutionary applications. A quantum computer can, in theory, solve certain problems exponentially faster than its classical counterparts. The current state of the art devices, however, are too small and noisy to practically realize this goal. An important tool for the advancement of quantum hardware, called model-based characterization, seeks to learn what types of noise are exhibited in a quantum processor. This technique, however, is notoriously difficult to scale up to even modest numbers of qubit, and has been limited to just 2 qubits until now. In this report, we present a novel method for performing model-based characterization, or tomography, on a many-qubit quantum processor. We consider up to 10 qubits, but the technique is expected to scale to even larger systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fast decay of classification error in variational quantum circuits

Variational quantum circuits (VQCs) have shown great potential in near-term applications. However, the discriminative power of a VQC, in connection to its circuit architecture and depth, is not understood. To unleash the genuine discriminative power of a VQC, we propose a VQC system with the optimal classical post-processing—maximum-likelihood estimation on measuring all VQC output qubits. Via extensive numerical simulations, we find that the error of VQC quantum data classification typically decays exponentially with the circuit depth, when the VQC architecture is extensive—the number of gates does not shrink with the circuit depth. This fast error suppression ends at the saturation towards the ultimate Helstrom limit of quantum state discrimination. On the other hand, non-extensive VQCs such as quantum convolutional neural networks are sub-optimal and fail to achieve the Helstrom limit, demonstrating a trade-off between ansatz complexity and classification performance in general. To achieve the best performance for a given VQC, the optimal classical post-processing is crucial even for a binary classification problem. To simplify VQCs for near-term implementations, we find that utilizing the symmetry of the input properly can improve the performance, while oversimplification can lead to degradation.

Zhang, Bingzhi↗
DOE OSTI2022

Coherent Many-Body Oscillations Induced by a Superposition of Broken Symmetry States in the Wake of a Quantum Phase Transition

It is now widely accepted that quenches through the critical region of quantum phase transitions result in post-transition states populated with topological defects—analogs of the classical topological defects. However, consequences of the very nonclassical fact that the state after a quench is a superposition of distinct, broken-symmetry vacua with different numbers and locations of defects have remained largely unexplored. Here, we identify coherent quantum oscillations induced by such superpositions in observables complementary to the one involved in symmetry breaking. These oscillations satisfy Kibble-Zurek dynamical scaling laws with the quench rate, with an instantaneous oscillation frequency set primarily by the gap of the system. In addition to the obvious fundamental significance of a superposition of different broken symmetry states, quantum coherent oscillations can be used to verify unitarity and test for imperfections of the experimental implementations of quantum simulators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Neutrinoless double beta decay via light neutralinos in R-parity violating supersymmetry

We perform a study of neutrinoless double beta (0νββ) decay mediated by the lightest neutralino of arbitrary mass in the minimal supersymmetric Standard Model (MSSM) under the presence of R-parity violating trilinear interactions. In this scenario, the exchange of the lightest neutralino can result in 0νββ decay of either long-range or short-range behaviour, depending on the neutralino mass. Using nuclear matrix elements calculated in the interacting boson model, we use an interpolation between the long- and short-range behaviours with an approximate formula. The non-observation of 0νββ decay is then used to place constraints on the supersymmetry parameter space, compatible with constraints from collider experiments. We compare these constraints to bounds from pion decays, CKM unitarity and big bang nucleosynthesis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Neutrino masses and magnetic moments of electron and muon in the Zee Model

We explore parameter space in the Zee Model to resolve the long-standing tension of the electron and muon anomalous magnetic moment (AMM). The model comprises a second Higgs doublet and a charged singlet at electroweak scale and generates Majorana neutrino masses at one-loop level; the neutral partner of the SU(2) L doublet contributes to the AMM of electron and muon via one loop and two-loop corrections. We propose two minimal flavor structures that can explain these anomalies while fitting the neutrino oscillation data. We find that the neutral Higgs resides in the mass range of roughly 10–300 GeV or 1–30 GeV, depending on the flavor structures. The model is consistent with constraints from colliders, electroweak precision data, and lepton flavor violation. To be comprehensive, we examine the constraints from the electric dipole moment (EDM) and find a region of parameter space that gives a sizable contribution to muon EDM while simultaneously giving corrections to muon AMM. In addition to the light scalar, the two charged scalars with masses as low as 100 GeV can induce nonstandard neutrino interactions ε ee as large as 8%, potentially hinting at new physics. We also investigate the projected capability of future lepton colliders to probe the currently allowed parameter space consistent with both electron and muon AMMs via direct searches in the ℓ + ℓ – → ℓ + ℓ – (H→ℓ + ℓ – )channel.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

High-resolution absorption spectroscopy of room-temperature and jet-cooled ammonia between 59,000 and 93,000 cm -1

Here, we present new high-resolution photoabsorption spectra of ammonia spanning the region between 59,000 cm -1 and 93,000 cm -1 that were recorded by using the Fourier Transform Spectrometer at the Synchrotron SOLEIL. This region extends from just above the Franck-Condon envelope for the $\tilde{A}$ 1 $A$ 2 " ← $\tilde{X}$ 1 $A$ 1 ' transition to well above the NH 3 + $\tilde{X}$ + 2 $A$ 2 " ionization threshold. The spectra were recorded at a measured resolution of 0.23 cm -1 in both a room-temperature cell (293 K) and in a slit-jet supersonic expansion (~70 K). The absolute photoabsorption cross section with an uncertainty of ±5% is also reported for the room-temperature spectrum. The present resolution is a factor of 10 – 100 times higher than in other recently reported broad band spectra of ammonia, and many of the observed bands show partially resolved rotational structure. We have attempted to assign this structure for a number of these bands. The oscillator strengths extracted from the data are in good agreement with previous measurements but, in the case of structured bands, the present higher resolution measurements return higher peak absorption cross sections, that increase further when the sample is cooled. The present higher resolution spectra suggest that a number of previous vibronic band assignments that were based on quantum defect considerations may require some revision. Finally, we discuss the substantial differences between the photoabsorption and photoionization data just above the first ionization threshold.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Fluorescence Enhancement in the Solid State by Isolating Perylene Fluorophores in Metal–Organic Frameworks

Polycyclic aromatic hydrocarbons such as perylene, pyrene, and their derivatives are highly emissive fluorophores in solution. Furthermore, the practical applications of these materials in the field of molecular electronic and light emitting devices are often hindered by self-quenching effects due to the formation of non-fluorescent aggregates in concentrated solutions or in the solid state. Herein, we demonstrate that aggregation caused quenching of perylenes can be minimalized by molecular incorporation into metalorganic frameworks (MOFs). This study utilized a stable Zr 6 cluster-based MOF, UiO-67, as a matrix. Linear linkers containing the photo-responsive moieties were designed and incorporated into the parent UiO-67 scaffold through the partially replacement of the non-fluorescent linkers of a similar length, forming mixed-linker MOFs. Here, the average distance between perylene moieties was tuned by changing the linker ratios, thus controlling the fluorescence intensity, emission wavelength, and quantum yield. Molecular modeling was further adopted to correlate the number of isolated perylene linkers within the framework to the ratio between the two linkers, therefore rationalizing the change in the observed fluorescent properties. Taking advantage of the tunable fluorescence, inherent porosity, and high chemical stability of this MOF platform, it was applied as a fluorescent sensor for oxygen detection in the gas phase, a model reaction, showing fast response and good recyclability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Strongly correlated and topological states in [111] grown transition metal oxide thin films and heterostructures

We highlight recent advances in the theory, materials fabrication, and experimental characterization of strongly correlated and topological states in [111] oriented transition metal oxide thin films and heterostructures, which are notoriously difficult to realize compared to their [001] oriented counterparts. We focus on two classes of complex oxides, with the chemical formulas ABO 3 and A 2 B 2 O 7 , where the B sites are occupied by an open-shell transition metal ion with a local moment and the A sites are typically a rare earth element. The [111] oriented quasi-two-dimensional lattices derived from these parent compound lattices can exhibit peculiar geometries and symmetries, namely, a buckled honeycomb lattice, as well as kagome and triangular lattices. These lattice motifs form the basis for emergent strongly correlated and topological states expressed in exotic magnetism, various forms of orbital ordering, topological insulators, topological semimetals, quantum anomalous Hall insulators, and quantum spin liquids. For transition metal ions with high atomic number, spin–orbit coupling plays a significant role and may give rise to additional topological features in the electronic band structure and in the spectrum of magnetic excitations. We conclude this perspective by articulating open challenges and opportunities in this actively developing field.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Hamiltonian learning using machine-learning models trained with continuous measurements

Here, we build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Origin of the arrow of time in quantum mechanics

We point out that time’s arrow is naturally induced by quantum mechanical evolution, whenever the systems have a very large number [Formula: see text] of nondegenerate states and a Hamiltonian bounded from below. When [Formula: see text] is finite, the arrow is imperfect, since evolution can resurrect past states. In the limit [Formula: see text] the arrow is fixed by the “tooth of time”: the decay of excited states induced by spontaneous emission to the ground state, mediated by interactions and a large number of decay products which carry energy and information to infinity. This applies to individual isolated atoms, and does not require a coupling to a separate large heath bath.

Astronomy & Astrophysics↗
DOE OSTI2025

Few Electron Transfer using Helium-Channel CCDs

This presentation discusses the movement of a small number of electrons suspended on superfluid helium, towards applications for quantum computing applications.

D'Imperio, Luke Anthony [Sandia National Laborator↗