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At least 1,495 records · Page 83

A variational master equation approach to quantum dynamics with off-diagonal coupling in a sub-Ohmic environment

A master equation approach based on an optimized polaron transformation is adopted for dynamics simulation with simultaneous diagonal and off-diagonal spin-boson coupling. Two types of bath spectral density functions are considered, the Ohmic and the sub-Ohmic. The off-diagonal coupling leads asymptotically to a thermal equilibrium with a nonzero population difference P{sub z}(t → ∞) ≠ 0, which implies localization of the system, and it also plays a role in restraining coherent dynamics for the sub-Ohmic case. Since the new method can extend to the stronger coupling regime, we can investigate the coherent-incoherent transition in the sub-Ohmic environment. Relevant phase diagrams are obtained for different temperatures. It is found that the sub-Ohmic environment allows coherent dynamics at a higher temperature than the Ohmic environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Demonstration of a 1820 channel multiplexer for transition-edge sensor bolometers

The scalability of most transition-edge sensor arrays is limited by the multiplexing technology, which combines their signals over a reduced number of wires and amplifiers. Here, in this Letter, we present and demonstrate a multiplexer design optimized for transition-edge sensor bolometers with 1820 sensors per readout unit, a factor of two more than the previous state-of-the-art. The design is optimized for cosmic microwave background imaging applications, and it builds on previous microwave superconducting quantum interference device multiplexers by doubling the available readout bandwidth to the full 4–8 GHz octave. Evaluating the key performance metrics of yield, sensitivity, and crosstalk through laboratory testing, we find an end-to-end operable detector yield of 78%, a typical nearest-neighbor crosstalk amplitude of ∼0.4%, and a median white noise level of 83 pA/$\sqrt{\textrm{Hz}}$ due to the multiplexer, corresponding to an estimated contribution of 4% to the total system noise for a ground-based cosmic microwave background telescope. Additionally, we identify a possible path toward reducing resonator loss for future designs with reduced noise. We expect these developments to alleviate the system complexity, cryogenic requirements, and cost of future large arrays of low temperature detectors.

cosmic microwave background↗

Detection of the metal–insulator transition in disordered systems of magnetic nanoislands

We have studied the conductivity and permittivity of a series of nanoisland-type FeNi films with an effective thickness of up to 3.2 nm on different substrates. It has been observed that the quantity Re ε changes its sign at effective thickness d{sup *} ≈ 1.5–1.8 nm, because of the metal–insulator transition. Analysis of the temperature dependences of the conductivity has confirmed the existence of the metal–insulator transition at the same thickness d{sup *}. It has been concluded that the introduced effective permittivity can serve as a characteristic of island metal systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

OpenQudit v1.0

OpenQudit is a library that packages a quantum circuit representation using a domain-specific language for gates with a highly optimized simulator for very small circuits. No other package is well optimized for small circuits, which is something another DOE-packaged tool, BQSKit, will benefit from.

Younis, Ed↗

ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries

High throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and envi-ronmental samples will revolutionize our understanding of the role of these intriguing and chemically diverse molecules in biological systems. Despite recent technological advances, a metabolomics study still results in the detection of a dispropor-tionate number of features than cannot be confidently assigned to a chemical structure. This inadequacy is driven by the sin-gle most significant limitation in metabolomics: the reliance on reference libraries constructed through the analysis of au-thentic reference chemicals and the limited commercial availability of these compounds. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e. conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The ap-proach employs state-of-the-art first-principles simulation, distinguished by use of molecular dynamics, quantum chemistry, and ion mobility calculations to generate predictions, all without training data. Importantly, optimization of ISiCLE includ-ed a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational effi-ciency by over two orders of magnitude. Calculated CCS values were validated against 1,983 experimental CCS values and compared to previously reported CCS calculation approaches. Average CCS calculation absolute error is 3.2% with standard deviation 3.1%. An online database is introduced for sharing both calculated and experimental CCS values (metabolom-ics.pnnl.gov), initially including a CCS library with over 0.8M entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including the identification of an environmental degradation product, the separation of molecular isomers, and the decoding of complex blinded mixtures of exposure chemicals. This work represents a promising method to address the limitations of small molecule identification, and offers alternatives to standards-based chemical identification amenable to high-performance computation.

Colby, Sean M↗

Quantum State Engineering Via Coherent-State Superpositions

The quantum interference between the two parts of the optical Schrodinger-cat state makes possible to construct a wide class of quantum states via discrete superpositions of coherent states. Even a small number of coherent states can approximate the given quantum states at a high accuracy when the distance between the coherent states is optimized, e. g. nearly perfect Fock state can be constructed by discrete superpositions of n + 1 coherent states lying in the vicinity of the vacuum state.

Janszky, Jozsef↗

Influence of phonon reservoir on photon blockade in a driven quantum dot-cavity system

We theoretically investigate the influence of the phonon bath on photon blockade in a simultaneously driven dot-cavity system. An optimal condition for avoiding two-photon excitation of a cavity field is put forward which can be achieved by modulating the phase difference and the strengths of the driving fields. The second-order correlation function and the mean photon number of the cavity field are discussed. In the absence of phonon effect, the strong photon blockade in a moderate quantum dot (QD)-cavity coupling regime occurs, which can be attributed to the destructive quantum interference arisen from different transition paths induced by simultaneously driving the dressed QD-cavity system. The participation of acoustic-phonon reservoir produces new transition channels for the QD-cavity system, which leads to the damage of destructive interference. As a result, the photon blockade effect is hindered when taking the electron-phonon interaction into account. It is also found that the temperature of the phonon reservoir is disadvantageous for the generation of photon blockade.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots

Defining quantum dots in semiconductor-based heterostructures is an essential step in initializing solid-state qubits. With growing device complexity and increasing number of functional devices required for measurements, a manual approach to finding suitable gate voltages to confine electrons electrostatically is impractical. Here, we implement a two-stage device characterization and dot-tuning process, which first determines whether devices are functional and then attempts to tune the functional devices to the single or double quantum-dot regime. We show that automating well-established manual-tuning procedures and replacing the experimenter’s decisions by supervised machine learning is sufficient to tune double quantum dots in multiple devices without premeasured input or manual intervention. The quality of measurement results and charge states are assessed by four binary classifiers trained with experimental data, reflecting real device behavior. We compare and optimize eight models and different data preprocessing techniques for each of the classifiers to achieve reliable autonomous tuning, an essential step towards scalable quantum systems in quantum-dot-based qubit architectures.

Darulova, J↗

Broadband three-photon near-infrared quantum cutting in Tm{sup 3+} singly doped YVO{sub 4}

An efficient three-photon near-infrared (NIR) quantum cutting (QC) is reported in Tm{sup 3+} singly doped YVO{sub 4} polycrystalline phosphors, where an optimized content of Tm{sup 3+} is determined to be 1.0 mol. %. Upon the absorption of a visible photon around 473 nm, three NIR photons emitting at 1180, 1479, and 1800 nm can be obtained efficiently by the sequential three-step radiative transitions of Tm{sup 3+}. The underlying mechanisms are analyzed in terms of the steady and dynamic fluorescence spectra measurements. Internal quantum yield is calculated to be 161.8% as a theoretical value when luminescence quenching due to defect species can be overcome. In addition, the broadband ultraviolet (UV)-excited [VO{sub 4}]{sup 3−} can strongly sensitize the {sup 1}G{sub 4} level of Tm{sup 3+} in the wavelength range likely from 250 to 360 nm, greatly increasing the UV photo-response and NIR fluorescent intensity of Tm{sup 3+}. The further development of this broadband three-photon NIR QC material would explore the new route to improve the photo-response of novel photoelectronic devices, particularly in 250–360 nm.

36 MATERIALS SCIENCE↗

Locally purified maximally mixed states at scale: Entanglement pruning and symmetries

Locally Purified Density Operators (LPDOs) are state-of-the-art tensor network ansatze candidates that efficiently represent mixed quantum states at scale. However, given their non-uniqueness, their representational complexity is generally sub-optimal in practical computations. Here, in this work we perform a comprehensive numerical and analytical analysis and resolve this issue in the experimentally relevant limit where noise depolarizes the density operator into a maximally mixed state. To resolve the sub-optimality issue, we analyze two numerical tools, one analytic method, and detail the relations between them. The numerical tools used are fidelity-preserving truncations and isometric gauge transformations leveraging Riemannian optimizations over entropic objective functions. In addition, by invoking the injectivity and symmetry constraints of the maximally mixed LPDO, we also present analytical closed-form expressions for the disentangler and discuss their relation to numerical optimizers. Further, away from the maximally mixed state, our simulations highlight how the truncation threshold smoothly interpolate, as a function of depolarization, between established matrix product results and our new results. Our work shows how, by minimizing the resources required to represent key states of practical interest in experiment, the efficiency of tensor network algorithms can be substantially increased. This paves the path for uncovering tensor network’s fundamental scalability limits and latent potential in representing the wide locus of mixed quantum states that are accessible on near-term quantum devices.

Gangapuram, Amit Jamadagni [Oak Ridge National Lab↗

SU-D-BRB-05: Quantum Learning for Knowledge-Based Response-Adaptive Radiotherapy

Purpose: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient’s characteristics and optimize outcomes. Methods: We studied 88 liver SBRT patients with 35 on non-adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split-course of 3+2 fractions with a month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5-variable state represented by patient’s clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision-making to adapt at one month was considered. The MDP objective was defined by the complication-free tumor control (P{sup +}=TCPx(1-NTCP)). A simple regression model represented state-action mapping. Single bit in classical MDP and a qubit of 2-superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q-learning and quantum searching was performed using Grover’s algorithm, which applies uniform superposition over possible states and yields quadratic speed-up. Results: Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous-courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while the quantum approach averaged a more optimistic 0.57±0.4, but with high phase fluctuations. Conclusion: Our results demonstrate that quantum machine learning approaches provide a feasible and promising framework for real-time and sequential clinical decision-making in adaptive radiotherapy.

60 APPLIED LIFE SCIENCES↗

Design and optimization of microbolometer multilayer optical cavity

Microbolometers are the most widely used detectors in long-wave infrared uncooled thermal imagers. An optical cavity is required within a microbolometer structure to increase its optical absorption. In this work we present a detailed study on the design and optimization of a microbolometer optical cavity using Essential-Macleod package. In the simulations, the cavity is considered as thin film multi-layers that form cascaded Fabry-Perot optical cavities. In the design phase, the layers structures are selected including materials and initial thickness. The absorbing layers are chosen to be vanadium-pentoxide (V{sub 2}O{sub 5}) and titanium (Ti). In the optimization phase, the designed layer thicknesses are varied to maximize optical absorption within the absorbing layers. The simulations show that Ti layer absorption dominates over V{sub 2}O{sub 5} layer. Also, the optimization proves that the air-gap cavity thickness is not simply quarter-wavelength because of the complex cascaded Fabry-Perot structure. The optimized air-gap thickness here is ≈3.5 µm at 10.6µm wavelength.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

High-power terahertz quantum cascade lasers with ∼0.23 W in continuous wave mode

Terahertz quantum cascade lasers with a record output power up to ∼0.23 W in continuous wave mode were obtained. We show that the optimal 2.9-mm-long device operating at 3.11 THz has a low threshold current density of 270 A/cm{sup 2} at ∼15 K. The maximum operating temperature arrived at ∼65 K in continuous wave mode and the internal quantum efficiencies decreased from 0.53 to 0.19 for the devices with different cavity lengths. By using one convex lens with the effective focal length of 13 mm, the beam profile was collimated to be a quasi Gaussian distribution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Hierarchical optimization for neutron scattering problems

We present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Phenomenological model of photoluminescence degradation and photoinduced defect formation in silicon nanocrystal ensembles under singlet oxygen generation

We propose a phenomenological model to explain photoluminescence degradation of silicon nanocrystals under singlet oxygen generation in gaseous and liquid systems. The model considers coupled rate equations, which take into account the exciton radiative recombination in silicon nanocrystals, photosensitization of singlet oxygen generation, defect formation on the surface of silicon nanocrystals as well as quenching processes for both excitons and singlet oxygen molecules. The model describes well the experimentally observed power law dependences of the photoluminescence intensity, singlet oxygen concentration, and lifetime versus photoexcitation time. The defect concentration in silicon nanocrystals increases by power law with a fractional exponent, which depends on the singlet oxygen concentration and ambient conditions. The obtained results are discussed in a view of optimization of the photosensitized singlet oxygen generation for biomedical applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Data-centric machine learning in quantum information science

Abstract We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a ‘toy model’ demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Simulating quantum-classical interfaces via the Lindblad master equation

In hybrid quantum systems, the interface between quantum and classical domains is essential for the generation, control, and measurement of quantum states. Quantum-classical interfaces (QCIs) are ubiquitous in devices such as optical modulators, quantum sensors, and signal processors, where classical signals influence quantum dynamics. In this paper, we employ the Lindblad master equation to simulate the evolution of a quantum system interacting with a classical control system. Our model captures both linear and nonlinear interactions by incorporating first- and second-order susceptibilities, and it quantifies the influence of externally applied control parameters on decoherence and state evolution. As an illustrative example, we analyze an optical modulator and demonstrate how variations in material response and drive conditions affect photon statistics, coherence, and phase-space distributions. In conclusion, the findings offer a path to an all-encompassing model for understanding and optimizing QCIs, with wide-ranging implications for the performance, design, and robustness of next-generation quantum devices.

Quantum engineering↗

A Deep Quench Approach to the Optimal Control of an Allen–Cahn Equation with Dynamic Boundary Conditions and Double Obstacles

In this paper, we investigate optimal control problems for Allen-Cahn variational inequalities with a dynamic boundary condition involving double obstacle potentials and the Laplace-Beltrami operator. The approach covers both the cases of distributed controls and of boundary controls. The cost functional is of standard tracking type, and box constraints for the controls are prescribed. We prove existence of optimal controls and derive first-order necessary conditions of optimality. The general strategy is the following: we use the results that were recently established by two of the authors for the case of (differentiable) logarithmic potentials and perform a so-called “deep quench limit”. Using compactness and monotonicity arguments, it is shown that this strategy leads to the desired first-order necessary optimality conditions for the case of (non-differentiable) double obstacle potentials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗