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We review resource-efficiency metrics for transferring classical information with photon, present an overview of the ultimate limits to photon and dimensional efficiency permitted by quantum mechanics, as well as that acheived with structured transmitter-receiver pairs.
We report the first demonstration of using the Quantum Instrumentation and Control Kit (QICK) system on RFSoC FPGA technology to drive an entangled photon pair source and to detect the photon signals. With the QICK system, we achieve high levels of performance metrics including coincidence-to-accidental ratio exceeding 150, and entanglement visibility exceeding 95%, consistent with performance metrics achieved using conventional waveform generators. We also demonstrate simultaneous detector readout using the digitization functional of QICK, achieving internal system synchronization time resolution of 3.2 ps. The work reported in this paper represents an explicit demonstration of the feasibility for replacing commercial waveform generators and time taggers with RFSoC-FPGA technology in the operation of a quantum network, representing a cost reduction of more than an order of magnitude.
Generating all diagnoses is computationally intractable. Therefore, many of the state-of-the-art approaches are incomplete. Quantum computers may however offer a solution. The first commercially available quantum computer is being used to minimize polynomials that are difficult for classical simulated annealing but easy for quantum annealing. All problems in Model-based Diagnosis (MBD) can be transformed into a polynomial minimization problem, allowing one to apply a quantum algorithm called quantum annealing to solve MBD problems. To better understand the need for this quantum approach, we designed two simulated annealingdiagnostic algorithms tailored to run on a polynomial representation of MBD. These algorithms differ on their policy for random neighborhood variable selection. In addition, enhanced metrics were devised to provide more diagnostic coverage. Finally, these two simulated annealing algorithms were analyzed and empirically evaluated and compared against state-of-the-art probabilistic methods for MBD such as SAFARI using ISCAS-85.
Optical tweezer arrays have transformed atomic and molecular physics, now forming the backbone for a range of leading experiments in quantum computing, simulation and metrology. Typical experiments trap tens to hundreds of atomic qubits and, recently, systems with around 1,000 atoms were realized without defining qubits or demonstrating coherent control. However, scaling to thousands of atomic qubits with long coherence times and low-loss and high-fidelity imaging is an outstanding challenge and critical for progress in quantum science, particularly towards quantum error correction (QEC). Here we experimentally realize an array of optical tweezers trapping more than 6,100 neutral atoms in around 12,000 sites, simultaneously surpassing state-of-the-art performance for several metrics that underpin the success of the platform. Specifically, while scaling to such a large number of atoms, we demonstrate a coherence time of 12.6(1) s, a record for hyperfine qubits in an optical tweezer array. We show room-temperature trapping lifetimes of about 23 min, enabling record-high imaging survival of 99.98952(1)% with an imaging fidelity of more than 99.99%. We present a plan for zone-based quantum computing and demonstrate necessary coherence-preserving qubit transport and pick-up/drop-off operations on large spatial scales, characterized through interleaved randomized benchmarking. Our results, along with recent developments, indicate that universal quantum computing and QEC with thousands to tens of thousands of physical qubits could be a near-term prospect.
Consideration of the mechanism of production of gravitons in the empty, anisotropic, spatially inhomogeneous Gowdy three-torus cosmology. The Gowdy cosmology is an exact solution of the vacuum Einstein equations and is obtained as a generalization of the homogeneous empty Bianchi Type I (Kasner) cosmology by permitting the metric components to depend on one of the space variables in addition to time. The Hamiltonian methods of Arnowitt, Deser, and Misner are employed to identify the dynamical variables which are to be quantized. The WKB regime solution is identical to that found by Doroshkevich, Zel'dovich, and Novikov (DZN) for a universe containing collisionless anisotropic radiation. Using a procedure similar to that of Parker (1971) or Zel'dovich and Starobinskii (1971) for defining quantum number, it is found that the DZN large-time radiation consists of quanta (gravitons) created from an initial vacuum. The quantum behavior is much like the semiclassical enhancement of quantum number with the added feature of creation of quanta from vacuum fluctuations.
The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.
Kerr optical frequency combs are generated by pumping a high-Q integrated microresonator with a resonant laser. Below threshold, the pump laser field mediates the phenomenon of spontaneous four-wave mixing, where two pump photons are symmetrically up- and down-converted as twin photons that can be entangled across up to tens of eigenmodes in the spectral domain. While these room-temperature integrated photonic circuits are expected to play a central role in quantum technology, their high dimensionality and dissipative nature are a challenge for their theoretical description, therefore hindering the understanding of their properties and potential of performance. In this article, we develop a framework that permits to obtain an explicit solution for the density operator of quantum microcombs below threshold. Furthermore, this self-consistent theoretical description allows for their complete characterization, as well as for the analytical determination of various performance metrics such as fidelity, purity, and entropy.
To harness the potential of radially polarized photons for classical and quantum communication applications, we demonstrate an on-chip, room-temperature device, which generates highly directional radially polarized photons at very high rates. The photons are emitted from a giant CdSe/CdS colloidal quantum dot (gQD) accurately positioned at the tip of a metal nanocone centered inside a hybrid metal-dielectric bullseye antenna. We show that due to the large and selective Purcell enhancement specifically for the out-of-plane optical dipole of the gQD, the emitted photons can have a very high degree of radial polarization (>93%), based on a quantitative metric. Our study emphasizes the importance of accurate gQD positioning for optimal radial polarization purity through extensive experiments and simulations, which contribute to the fundamental understanding of radial polarization in nanostructured devices and pave the way for implementation of such systems in practical applications using structured quantum light.
We propose a precise duality between pure de Sitter quantum gravity in 2+1 2 + 1 dimensions and a double-scaled matrix integral. This duality unfolds in two distinct aspects. First, by carefully quantizing the gravitational phase space, we arrive at a novel proposal for the quantum state of the universe at future infinity. We compute cosmological correlators of massive particles in the universe specified by this wavefunction. Integrating these correlators over the metric at future infinity yields gauge-invariant observables, which are identified with the string amplitudes of the complex Liouville string [S. Collier et al., arXiv: 2409.17246]. This establishes a direct connection between integrated cosmological correlators and the resolvents of the matrix integral dual to the complex Liouville string, thereby demonstrating one aspect of the dS _3 3 /matrix integral duality. The second aspect concerns the cosmological horizon of the dS static patch and the Gibbons-Hawking entropy it is conjectured to encode. We show that this entropy can be reproduced exactly by counting the entries of the matrix.
Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.
The Euclidean path integral for gravity is enriched by the addition of boundaries, which provide useful probes of thermodynamic properties. Common boundary conditions include Dirichlet conditions on the boundary induced metric; microcanonical conditions, which refers to fixing some components of the Brown-York boundary stress tensor; and conformal conditions, in which the conformal structure of the induced metric and the trace of the extrinsic curvature are fixed. Boundaries also present interesting problems of consistency. The Dirichlet problem is known, under various (and generally different) conditions, to be inconsistent with perturbative quantization of graviton fluctuations, to exhibit thermodynamic instability, or to require infinite fine-tuning in the presence of matter fluctuations. We extend some of these results to other boundary conditions. We find that similarly to the Dirichlet problem, the graviton fluctuation operator is not elliptic with microcanonical boundaries, and the nonelliptic modes correspond to “boundary-moving diffeomorphisms.” However, we argue that microcanonical factorization of path integrals—essentially, the insertion of microcanonical constraints on two-sided surfaces in the bulk—is not affected by the same issues of ellipticity. We also show that for a variety of matter field boundary conditions, matter fluctuations renormalize the gravitational bulk and boundary terms differently, so that the classical microcanonical or conformal variational problems are not preserved unless an infinite fine-tuning is performed.
Topics covered include: Fiber-Optic Sensor Would Monitor Growth of Polymer Film; Sensors for Pointing Moving Instruments Toward Each Other; Pd/CeO2/SiC Chemical Sensors; Microparticle Flow Sensor; Scattering-Type Surface-Plasmon-Resonance Biosensors; Diode-Laser-Based Spectrometer for Sensing Gases; Improved Cathode Structure for a Direct Methanol Fuel Cell; X-Band, 17-Watt Solid-State Power Amplifier; Improved Anode for a Direct Methanol Fuel Cell; Tools for Designing and Analyzing Structures; Interactive Display of Scenes with Annotations; Solving Common Mathematical Problems; Tools for Basic Statistical Analysis; Program Calculates Forces in Bolted Structural Joints; Integrated Structural Analysis and Test Program; Molybdate Coatings for Protecting Aluminum Against Corrosion; Synthesizing Diamond from Liquid Feedstock; Modifying Silicates for Better Dispersion in Nanocomposites; Powder-Collection System for Ultrasonic/Sonic Drill/Corer; Semiautomated, Reproducible Batch Processing of Soy; Hydrogen Peroxide Enhances Removal of NOx from Flue Gases; Subsurface Ice Probe; Real-Time Simulation of Aeroheating of the Hyper-X Airplane; Using Laser-Induced Incandescence To Measure Soot in Exhaust; Method of Real-Time Principal-Component Analysis; Insect-Inspired Flight Control for Unmanned Aerial Vehicles; Domain Compilation for Embedded Real-Time Planning; Semantic Metrics for Analysis of Software; Simulation of Laser Cooling and Trapping in Engineering Applications; Large Fluvial Fans and Exploration for Hydrocarbons; Doping-Induced Interband Gain in InAs/AlSb Quantum Wells; Development of Software for a Lidar-Altimeter Processor; Upgrading the Space Shuttle Caution and Warning System; and Fractal Reference Signals in Pulse-Width Modulation.
The Multi-angle Imaging SpectroRadiometer (MISR) was launched as part of NASA’s Terra satellite suite of instruments. Still operational, it makes use of observations from multiple view angles to retrieve aerosol abundance and particle properties. The Multi-Angle Imager for Aerosols (MAIA) instrument improves on this heritage by incorporating polarimetry and an expanded spectral range. Combining these data with surface measurements, the relationship between pollution and human health will be explored. MAIA has just completed camera testing, building on the experience from MISR. Spectral calibration now makes use of a double subtractive monochromator, built with the intent to allow the exit slit output to be uniform in spectral content. For radiometric testing, hardware upgrades have included adding UV lamps to the 1.65 cm (65") integrating sphere, use of a NIST-traceable sphere to establish absolute radiances, and the addition of a UV transfer spectrometer to support characterization of the sphere output from 300 to 2500 nm. During camera build, Newton’s rings were observed in the detector Quantum Efficiency (QE) measurements. This is due to etaloning within the detector itself. Etaloning was also evident in the spectral and radiometric characterizations performed on the completed camera. Spectral metrics, including center wavelength and width, are presented here using a moment’s analysis. This better represents the band properties, particularly in bands where fringing is observed, as compared to a full-width at half-maximum determination. It is believed that the MAIA camera has been carefully characterized, and meet its spectral and radiometric requirements.
Motivated by near term quantum computing hardware limitations, combinatorial optimization problems that can be addressed by current quantum algorithms and noisy hardware with little or no overhead are used to probe capabilities of quantum algorithms such as the quantum approximate optimization algorithm. In this study, a specific class of near term quantum computing hardware defined combinatorial optimization problems, Ising models on heavy-hex graphs both with and without geometrically local cubic terms, are examined for their classical computational hardness via empirical computation time scaling quantification. Specifically the time-to-solution (TTS) metric using the classical heuristic simulated annealing is measured for finding optimal variable assignments (ground states), as well as the time required for the optimization software Gurobi to find an optimal variable assignment. Because of the sparsity of these Ising models, the classical algorithms are able to find optimal solutions efficiently even for large instances (i.e. 100 000 spin variables). The Ising models both with and without geometrically local cubic terms exhibit average-case linear-time or weakly quadratic scaling when solved exactly using Gurobi, and the Ising models with no cubic terms show evidence of exponential-time TTS scaling when sampled using simulated annealing. These findings point to the necessity of developing and testing more complex, namely more densely connected, optimization problems in order for quantum computing to ever have a practical advantage over classical computing. Our results are another illustration that different classical algorithms can indeed have exponentially different running times, thus making the identification of the best practical classical technique important in any quantum computing vs. classical computing comparison.
We study a correspondence between gravitational shockwave geometry and its fluid description near a Rindler horizon in Minkowski spacetime. Utilizing the Petrov classification that describes algebraic symmetries for Lorentzian spaces, we establish an explicit mapping between a potential fluid and the shockwave metric perturbation, where the Einstein equation for the shockwave geometry is equivalent to the incompressibility condition of the fluid, augmented by a shockwave source. Then we consider an Ansatz of a stochastic quantum source for the potential fluid, which has the physical interpretation of shockwaves created by vacuum energy fluctuations. Under such circumstance, the Einstein equation, or equivalently, the incompressibility condition for the fluid, becomes a stochastic differential equation. By smearing the quantum source on a stretched horizon in a Lorentz invariant manner with a Planckian width (similarly to the membrane paradigm), we integrate fluctuations near the Rindler horizon to find an accumulated effect of the variance in the round-trip time of a photon traversing the horizon of a causal diamond.
Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.
Chirality-induced spin selectivity (CISS) describes how chiral molecules and materials generate spin polarization even at thermal equilibrium. This observation has challenged established principles of microscopic reversibility and Onsager reciprocity. We resolve this paradox by formulating a pseudo-Hermitian quantum framework that separates thermodynamic equilibrium from time-reversal symmetry. Within this approach, structural chirality and electron correlations, irrespective of their microscopic origin, are sufficient to produce CISS observables. Chirality enters through a non-local metric η that couples spin and spatial motion, leading to real spectra, unitary evolution, and thermodynamic consistency. The framework predicts a chirality-induced spin magnetic ordering characterized by a spin-displacement order, which reconciles equilibrium spin polarization with detailed balance and explains the persistence of CISS in materials composed of light elements. We derive generalized Onsager-Casimir relations that respect the observed CISS symmetry, i.e., parity $(\mathscr{P})$-odd and time-reversal $(\mathscr{T})$-odd, but exhibiting $\mathscr{PT}$-even symmetry. This approach establishes a coherent foundation for equilibrium CISS and provides a route to link chemical chirality with measurable spin-to-charge conversion effects.
This project developed Cd-free quantum dot (QD) downconverters for efficacious human-centric lighting (HCL) light emitting diode (LED) devices. Solid state HCL devices address the lack of light in the cyan wavelength region (460-490 nm) in white LEDs by enhancing the melanopic daylight efficacy ratio (MDER) value. MDER is a metric that indicates the degree to which artificial lighting stimulates nonvisual biological processes in the retina responsible for regulating circadian rhythms compared with natural lighting. The peak sensitivity of melanopsin in the retina is at 479 nm, therefore artificial lighting which can fill the well-known “cyan gap” may improve human health. Due to the Restriction of Hazardous Substances (RoHS) regulations, cadmium-free core/shell/shell QDs are the primary targets for low toxicity, tunable downconverters. Cyan- and red-emitting core/shell/shell QDs were implemented in LED devices toward achieving a brightness of 210 lm/W at 4000 K, CRI 90, and MDER > 0.7. Synthetic development of Cd-free materials yielded cyan InP/ZnS QDs with a photoluminescence quantum yield (PLQY) of ~60% between 480-490 nm, and red InP/ZnSe/(ZnSeS)/ZnS QDs reaching ~80% PLQY at 620-630 nm. We successfully demonstrated that the inclusion of cyan QDs increased the MDER value to ≥0.7, but the brightness of the HCL LED devices was hindered due to low PLQY values. However, upon substitution of the Cd-free cyan QD with a low-Cd QD with >90% PLQY, brightness improved by 25% over the Cd-free device to 179 lm/W. Despite the technical obstacles remaining toward improving emission characteristics and stability of QDs under high flux, we have confirmed the promise of narrow, tunable QD emitters in SSL packages toward the goal of healthy, human-centric lighting.