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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Detecting Quantum Critical Points of Correlated Systems by Quantum Convolutional Neural Network Using Data from Variational Quantum Eigensolver

Machine learning has been applied to a wide variety of models, from classical statistical mechanics to quantum strongly correlated systems, for classifying phase transitions. The recently proposed quantum convolutional neural network (QCNN) provides a new framework for using quantum circuits instead of classical neural networks as the backbone of classification methods. We present the results from training the QCNN by the wavefunctions of the variational quantum eigensolver for the one-dimensional transverse field Ising model (TFIM). We demonstrate that the QCNN identifies wavefunctions corresponding to the paramagnetic and ferromagnetic phases of the TFIM with reasonable accuracy. The QCNN can be trained to predict the corresponding ‘phase’ of wavefunctions around the putative quantum critical point even though it is trained by wavefunctions far away. The paper provides a basis for exploiting the QCNN to identify the quantum critical point.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1972

Geometry of quantum signal detection.

Consideration of a binary quantum signal detection problem in a two-dimensional Hilbert space. The optimum detection problem is reduced to the problem of finding the locus of a vector which has a maximum projection along the fixed a priori probability vector of hypotheses. It is shown that the desired locus can be determined by a geometrical method involving the use of a randomized decision strategy. It is further shown that this geometrical method can be applied to the optimum solution of a binary detection problem described in a product Hilbert space.

Harger, R. O.↗
DOE OSTI2024

Entanglement-Enhanced Neyman-Pearson Target Detection

Quantum illumination (QI) provides entanglement-enabled target-detection enhancement, despite operating in an entanglement-breaking environment. Existing experimental studies of QI have utilized a Bayesian approach, assuming that the target is equally likely to be present or absent before detection, to demonstrate an advantage over classical target detection. However, such a premise breaks down in practical operational scenarios in which the prior probability is unknown, thereby hindering QI's applicability to real-world target-detection scenarios. In this work, we adopt the Neyman-Pearson criterion in lieu of the error probability for equally likely target absence or presence as our figure of merit for QI. We demonstrate an unconditional quantum advantage over the optimal classical-illumination protocol as benchmarked by the receiver operating characteristic, which examines detection probability versus false-alarm probability without resorting to known prior probabilities. Our work represents a critical advancement in adapting quantum-enhanced sensing to practical operational settings.

Ward, William↗
DOE OSTI2023

Quantum anomaly detection for collider physics

We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. This is a representative task where classification needs to be performed using small training datasets - a regime that has been suggested for a quantum advantage. We find that Classical Machine Learning (CML) benchmarks outperform standard QML algorithms and are able to automatically identify the presence of anomalous events injected into otherwise background-only datasets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Intensified Tpx3Cam, a fast data-driven optical camera with nanosecond timing resolution for single photon detection in quantum applications

Here we describe a fast data-driven optical camera, Tpx3Cam, with nanosecond scale timing resolution and 80 Mpixel/sec throughput. After the addition of intensifier, the camera is single photon sensitive with quantum efficiency determined primarily by the intensifier photocathode. The single photon performance of the camera was characterized with results on the gain, timing resolution and afterpulsing reported here. The intensified camera was successfully used for measurements in a variety of applications including quantum applications. As an example of such application, which requires simultaneous detection of multiple photons, we describe registration of photon pairs from the spontaneous parametric down-conversion source in a spectrometer. We measured the photon wavelength and timing with respective precisions of 0.15 nm and 3 ns, and also demonstrated that the two photons are anti-correlated in energy.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Application of signal detection theory to optics

Basic quantum detection and estimation theory, applications to optics, photon counting, and filtering theory are studied. Recent work on the restoration of degraded optical images received at photoelectrically emissive surfaces is also reported, the data used by the method are the numbers of electrons ejected from various parts of the surface.

Helstrom, C. W.↗
DOE OSTI2025

Quantum-enhanced detection of viral cDNA via luminescence resonance energy transfer using upconversion and gold nanoparticles

Abstract The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a sensitive and highly specific detection method for cDNA, leveraging Luminescence Resonance Energy Transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs), and achieves a detection limit of 242 fM for SARS-CoV-2 cDNA. This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5′ and 3′ ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched cDNA sequences and significantly improving detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4 % to 73.6 %, with an optimal midpoint of 42 %, thereby demonstrating the superior sensitivity of our method. Our work uses SARS-CoV-2 cDNA as a model system to demonstrate the potential of our LRET-based detection method. This proof-of-concept study highlights the adaptability of our platform for future diagnostic applications. Instrumental validation confirms the synthesis and formation of AuNPs, addressing the need for experimental verification of the preparation of nanomaterial. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach provides a low detection limit and high specificity for target cDNA sequences, underscoring its potential for targeted COVID-19 diagnostics. This study demonstrates the superior sensitivity and adaptability of using UCNPs and AuNPs for cDNA detection, offering significant advances in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in developing next-generation biosensors for managing current and future viral outbreaks. By adjusting primer sequences, this platform can be tailored to detect other pathogens, contributing to the enhancement of global healthcare responsiveness and infectious disease control.

Esmaeili, Shahriar [Institute for Quantum Science ↗
NASA NTRS1989

Improved Si:As BIBIB (Back-Illuminated Blocked-Impurity-Band) hybrid arrays

Results of a program to increase the short wavelength (less than 10 microns) detective quantum efficiency, eta/beta, of Si:As Impurity Band Conduction arrays are presented. The arrays are epitaxially grown Back-Illuminated Blocked (BIB) Impurity-Band (BIBIB) 10x50 detectors bonded to switched-FET multiplexers. It is shown that the 4.7 microns detective quantum efficiency increases proportionately with the thickness of the infrared active layer. A BIB array with a thick active layer, designed for low dark current, exhibits eta/beta = 7 to 9 percent at 4.7 microns for applied bias voltages between 3 and 5 V. The product of quantum efficiency and photoelectric gain, etaG, increases from 0.3 to 2.5 as the voltage increases from 3 to 5 V. Over this voltage range, the dark current increases from 8 to 120 e(-)s(-1) at a device temperature of 4.2 K and is under 70 e(-)s(-1) for all voltages at 2 K. Because of device gain, the effective dark current (equivalent photon rate) is less than 3 e(-)s(-1) under all operating conditions. The effective read noise (equivalent photon noise) is found to be less than 12 electrons under all operating conditions and for integration times between 0.05 and 100 seconds.

Herter, T.↗
DOE OSTI2024

Circuit quantum electrodynamics detection of induced two-fold anisotropic pairing in a hybrid superconductor–ferromagnet bilayer

Abstract Hybrid systems represent one of the frontiers in the study of unconventional superconductivity and are a promising platform to realize topological superconducting states. These materials are challenging to probe using many conventional measurement techniques because of their mesoscopic dimensions, and therefore require new experimental probes so that they can be successfully characterized. Here, we demonstrate a probe that enables us to measure the superfluid density of micrometre-size superconductors using microwave techniques drawn from circuit quantum electrodynamics. We apply this technique to a superconductor–ferromagnet bilayer and find that the proximity-induced superfluid density is two-fold anisotropic within the plane of the sample. It also exhibits power-law temperature scaling that is indicative of a nodal superconducting state. These experimental results are consistent with the theoretically predicted signatures of induced triplet pairing with a nodal p -wave order parameter. Moreover, we observe modifications to the microwave response at frequencies near the ferromagnetic resonance, suggesting a coupling between the spin dynamics and induced superconducting order in the ferromagnetic layer. Our experimental technique can be employed more widely, for example to study fragile unconventional superconductivity in low-dimensional materials such as van der Waals heterostructures.

Bøttcher, C. G. L. (ORCID:0000000194898461)↗
NASA NTRSDate not supplied

Where Lies the Quantumness Behind Detecting Electromagnetic Waves for Frequencies from Infrared and Up?

The synthesis of Newtonian concept of corpuscles during emission with Huygens’ concept of secondary-wavelets during propagation implies that all EM radiations from quantized atoms and molecules are released as discrete amount of energies. However, they propagate out as time-finite Maxwellian light pulses. Huygens also underscored that his secondary wavelets keep propagating as independent pulses in the absence of any interacting medium, or until intercepted by an interface with a medium or a detector. Then we use the Superposition Principle and the coherence theory to derive Einstein’s photoelectric equation by summing innumerable random time-finite pulses. This process driven approach should yield the characteristic statistical variations of photoelectron current pulses, as generated by photodetectors for different kinds of light sources. Lamb & Scully originally proposed this semiclassical approach without assuming that light actually consists of time finite pulses. The quantumness remains confined within the excitation and de-excitations processes in the material particles.

Electromagnetic Waves↗
DOE OSTI2024

Noise robust detection of quantum phase transitions

Quantum computing allows for the manipulation of highly correlated states whose properties quickly go beyond the capacity of any classical method to calculate. Thus one natural problem which could lend itself to quantum advantage is the study of ground-states of condensed matter models, and the transitions between them. However, current levels of hardware noise can require extensive application of error-mitigation techniques to achieve reliable computations. In this work, we use several IBM devices to explore a finite-size spin model with multiple “phaselike” regions characterized by distinct ground-state configurations. Using preoptimized Variational Quantum Eigensolver (VQE) solutions, we demonstrate that in contrast to calculating the energy, where zero-noise extrapolation is required in order to obtain qualitatively accurate yet still unreliable results, calculations of the energy derivative, two-site spin correlation functions, and the fidelity susceptibility yield accurate behavior across multiple regions, even with minimal or no application of error-mitigation approaches. Taken together, these sets of observables could be used to identify level crossings in a simple, noise-robust manner which is agnostic to the method of ground state preparation. This work shows promising potential for near-term application to identifying quantum phase transitions, including avoided crossings and nonadiabatic conical intersections in electronic structure calculations. Published by the American Physical Society 2024

Lively, Kevin (ORCID:0000000320981494)↗
NASA NTRS1993

Germanium blocked-impurity-band detector arrays - Unpassivated devices with bulk substrates

We have fabricated and characterized six-element monolithic arrays of Ge:Ga blocked-impurity-band detectors, with threshold wavelength 220 microns, peak quantum efficiency 14 percent, detective quantum efficiency 9 percent, dark current 300 e(-)/s, and response uniformity better than 4 percent. The devices are described very well by the standard model of blocked-impurity-band detectors and appear to satisfy many of the requirements of low-background astronomical instruments.

Watson, Dan M.↗
DOE OSTI2025

Quantum AI Based Enhanced Detection of Dementia

Quantum computing has the potential to significantly improve the early detection of Alzheimer's Disease and Related Dementias (ADRD). Quantum-enhanced machine learning can be used to perform an early screening of Alzheimer's disease using brain imaging data based on dataset of MRI scans from both healthy individuals and those diagnosed with Alzheimer's. This study aims to demonstrate the potential of quantum transfer learning to enhance the performance of the classical deep learning model for dementia detection. Using the MRI sagittal images available in the OASIS-2 (64 demented and 72 non-demented subjects between 60 and 96 years), we show how quantum techniques can transform a suboptimal classical model into a more effective solution for dementia detection, highlighting their potential impact on advancing healthcare technology. We begin with a simple classical deep learning model with a significantly smaller number of parameters, which gives suboptimal performance on the problem. Then, we apply different configurations of quantum transfer learning based on the pre-trained weak classifier (Figure 1). We fix the weak classifier's initial convolutional layers at their fixed pre-trained parameters and replace the last set of dense layers with a dressed quantum circuit (DQN), which we train to enhance performance. We performed 4-fold cross-validation for both the classical and the hybrid quantum models and trained them using Pennylane's `default.qubit' simulator and IonQ's Aria-1 simulator (noisy simulation). We showed that with significantly fewer parameters, the quantum transfer learning-based hybrid models showed significant performance enhancement over the base weak classical deep learning model for dementia detection. To classify between a demented and non-demented subject, the accuracy of quantum-based AI methods improved by 6 to 14% compared to classical methods. The sensitivity of the models improved by 4 to 17%. This shows that there are fewer chances of misclassifying demented patients. Figure 2 compares the performance of the hybrid quantum models and their base classical model, and Table 1 summarizes the results. We illustrated that with assistance from quantum machine learning, it is possible to enhance detection for dementia based on brain images. This shows the potential for practical utility of quantum computing in ADRD research.

Bhowmik, Sounak [University of Tennessee, Knoxvill↗
DOE OSTI2022

Resolution of 100 photons and quantum generation of unbiased random numbers

Macroscopic quantum phenomena, such as observed in superfluids and superconductors, have led to promising technological advancements and some of the most important tests of fundamental physics. At present, quantum detection of light is mostly relegated to the microscale, where avalanche photodiodes are very sensitive to distinguishing single-photon events from vacuum but cannot differentiate between larger photon-number events. Beyond this, the ability to perform measurements to resolve photon numbers is highly desirable for a variety of quantum information applications including computation, sensing, and cryptography. True photon-number resolving detectors do exist, but they are currently limited to the ability to resolve on the order of 10 photons, which is too small for certain proposals. In this work, we extend photon measurement into the mesoscopic regime by implementing a detection scheme based on multiplexing highly quantum-efficient transition-edge sensors to accurately resolve photon numbers between zero and 100. Further, we then demonstrate the use of our system by implementing a quantum random number generator with no inherent bias. This method is based on sampling a coherent state in the photon-number basis and is robust against environmental noise, phase and amplitude fluctuations in the laser, loss and detector inefficiency as well as eavesdropping. Beyond true random number generation, our detection scheme serves as a means to implement quantum measurement and engineering techniques valuable for photonic quantum information processing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗