Hybrid Quantum Research Articles

Macroscopic entanglement between ferrimagnetic magnons and atoms via crossed optical cavities.

We consider a two-dimensional opto-magnomechanical (OMM) system including two optical cavity modes, a magnon mode, a phonon mode, and a collection of two-level atoms. We show how the stationary entanglement between two-level atoms and magnons can be achieved. The presence of two optical cavities leads the atom-magnon entanglement to be achieved in a wide parameter regime. Furthermore, it is shown that one optical cavity can get entangled with magnons, phonons, and the other optical cavity. The entanglement is robust against thermal noise. The work may find applications in building hybrid quantum networks and quantum information processing.

All-optical spin access via a cavity-broadened optical transition in on-chip hybrid quantum photonics

Hybrid quantum photonic systems connect classical photonics to the quantum world and promise to deliver efficient light-matter quantum interfaces while leveraging the advantages of both, the classical and the quantum, subsystems. However, combining efficient, scalable photonics and solid-state quantum systems with desirable optical and spin properties remains a formidable challenge. In particular, the access to individual spin states and coherent mapping to photons remains unsolved for hybrid systems. In this paper, we demonstrate all-optical initialization and readout of the electron spin of a negatively charged silicon-vacancy center in a nanodiamond coupled to a silicon nitride photonic crystal cavity. We characterize relevant parameters of the coupled emitter-cavity system and determine the silicon-vacancy center’s spin-relaxation and spin-decoherence rate. Our results mark a key step towards the realization of a hybrid spin-photon interface based on silicon nitride photonics and the silicon-vacancy center’s electron spin in nanodiamonds with potential use for quantum networks, quantum communication, and distributed quantum computation. Published by the American Physical Society 2024

Coherent optical coupling to surface acoustic wave devices

Surface acoustic waves (SAW) and associated devices are ideal for sensing, metrology, and hybrid quantum devices. While the advances demonstrated to date are largely based on electromechanical coupling, a robust and customizable coherent optical coupling would unlock mature and powerful cavity optomechanical control techniques and an efficient optical pathway for long-distance quantum links. Here we demonstrate direct and robust coherent optical coupling to Gaussian surface acoustic wave cavities with small mode volumes and high quality factors (>105 measured here) through a Brillouin-like optomechanical interaction. High-frequency SAW cavities designed with curved metallic acoustic reflectors deposited on crystalline substrates are efficiently optically accessed along piezo-active directions, as well as non-piezo-active (electromechanically inaccessible) directions. The precise optical technique uniquely enables controlled analysis of dissipation mechanisms as well as detailed transverse spatial mode spectroscopy. These advantages combined with simple fabrication, large power handling, and strong coupling to quantum systems make SAW optomechanical platforms particularly attractive for sensing, material science, and hybrid quantum systems.

Decarboxylation Mechanism of iso-Orotate Decarboxylase Revisited.

iso-Orotate decarboxylase (IDCase), which is involved in the thymidine salvage pathway, has attracted considerable interest owing to its chemical similarity to a hypothetical DNA decarboxylase in mammals. Although valuable insights into the active DNA demethylation of 5-methyl-cytosine can be obtained from the decarboxylation mechanism of 5-carboxyl-uracil (5caU) catalyzed by IDCase, this mechanism remains under debate. In this study, the catalytic mechanism of 5caU decarboxylation by IDCase was studied using hybrid quantum mechanics/molecular mechanics (QM/MM) methodologies and density functional theory (DFT) calculations with a truncated model. The calculations supported a mechanism involving three sequential stages: activation of the 5caU substrate via proton transfer from an arginine (R262') to the carboxyl group of 5caU, formation of a tetrahedral intermediate, and decarboxylation of the tetrahedral intermediate to generate uracil as the product. The reaction pathways and structures obtained using the QM/MM and DFT methods coincided with each other. These simulations provided detailed insights into the unique mechanism of IDCase, clarifying various unresolved issues, such as the critical role of R262'. In addition, aspartate D323 was found to act as a general base in the tetrahedral intermediate formation step and a general acid in the later C-C bond cleavage step.

Anti-resonant acoustic waveguides enabled tailorable Brillouin scattering on chip

Empowering independent control of optical and acoustic modes and enhancing the photon-phonon interaction, integrated photonics boosts the advancements of on-chip stimulated Brillouin scattering (SBS). However, achieving acoustic waveguides with low loss, tailorability, and easy fabrication remains a challenge. Here, inspired by the optical anti-resonance in hollow-core fibers and acoustic anti-resonance in cylindrical waveguides, we propose suspended anti-resonant acoustic waveguides (SARAWs) with superior confinement and high selectivity of acoustic modes, supporting both forward and backward SBS on chip. Furthermore, this structure streamlines the design and fabrication processes. Leveraging the advantages of SARAWs, we showcase a series of breakthroughs for SBS within a compact footprint on the silicon-on-insulator platform. For forward SBS, a centimeter-scale SARAW supports a large net gain exceeding 6.4 dB. For backward SBS, we observe an unprecedented Brillouin frequency shift of 27.6 GHz and a mechanical quality factor of up to 1960 in silicon waveguides. This paradigm of acoustic waveguide propels SBS into a new era, unlocking new opportunities in the fields of optomechanics, phononic circuits, and hybrid quantum systems.

Axial perturbations in Kantowski-Sachs spacetimes and hybrid quantum cosmology

Axial perturbations in Kantowski-Sachs spacetimes and hybrid quantum cosmology

Magnon-Skyrmion Hybrid Quantum Systems: Tailoring Interactions via Magnons.

Coherent and dissipative interactions between different quantum systems are essential for the construction of hybrid quantum systems and the investigation of novel quantum phenomena. Here, we propose and analyze a magnon-skyrmion hybrid quantum system, consisting of a micromagnet and nearby magnetic skyrmions. We predict a strong-coupling mechanism between the magnonic mode of the micromagnet and the quantized helicity degree of freedom of the skyrmion. We show that with this hybrid setup it is possible to induce magnon-mediated nonreciprocal interactions and responses between distant skyrmion qubits or between skyrmion qubits and other quantum systems like superconducting qubits. This work provides a quantum platform for the investigation of diverse quantum effects and quantum information processing with magnetic microstructures.

A hybrid quantum ensemble learning model for malicious code detection

Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to quantum support vector machine (QSVM), quantum K nearest neighbors (QKNN), variational quantum classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.

Electro-optomechanical entanglement in two coupled optical cavities with an LC microwave circuit

We theoretically investigate entanglement in a hybrid quantum system comprising two optical cavities coupled to a shared microwave cavity via optomechanical interactions. Our results demonstrate that increasing the microwave input power and frequency in the allowed range enhances entanglement between the microwave–optical and optical–optical modes. We also show by change frequency of the one optical cavity in the communication domain that we still have entanglement. This ability to generate robust microwave–optical photon entanglement shows promise for various quantum technologies. Optical photons could enable secure quantum communication in optical fibers, while microwave photons allow transmission to satellites. Overall, this hybrid cavity optomechanical system offers prospects as an efficient source of entangled photon pairs, vital for implementing long-distance quantum communication networks.

Quantum Machine Learning for Credit Scoring

This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges.

A novel intrusion detection system based on a hybrid quantum support vector machine and improved Grey Wolf optimizer

The Internet of Things (IoT) has grown significantly in recent years, allowing devices with sensors to share data via the internet. Despite the growing popularity of IoT devices, they remain vulnerable to cyber-attacks. To address this issue, researchers have proposed the Hybrid Intrusion Detection System (HIDS) as a way to enhance the security of IoT. This paper presents a novel intrusion detection model, namely QSVM-IGWO, for improving the detection capabilities and reducing false positive alarms of HIDS. This model aims to improve the performance of the Quantum Support Vector Machine (QSVM) by incorporating parameters from the Improved Grey Wolf Optimizer (IGWO) algorithm. IGWO is introduced under the hypothesis that the social hierarchy observed in grey wolves enhances the searching procedure and overcomes the limitations of GWO. In addition, the QSVM model is employed for binary classification by selecting the kernel function to obtain an optimal solution. Experimental results show promising performance of QSVM-IGWO in terms of accuracy, Recall, Precision, F1 score, and ROC curve, when compared with recent detection models.

Seamless integration of GEM, a density based-force field, for QM/MM simulations via LICHEM, Psi4, and Tinker-HP.

Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have become an essential tool in computational chemistry, particularly for analyzing complex biological and condensed phase systems. Building on this foundation, our work presents a novel implementation of the Gaussian Electrostatic Model (GEM), a polarizable density-based force field, within the QM/MM framework. This advancement provides seamless integration, enabling efficient and optimized QM/GEM calculations in a single step using the LICHEM Code. We have successfully applied our implementation to water dimers and hexamers, demonstrating the ability to handle water systems with varying numbers of water molecules. Moreover, we have extended the application to describe the double proton transfer of the aspartic acid dimer in a box of water, which highlights the method's proficiency in investigating heterogeneous systems. Our implementation offers the flexibility to perform on-the-fly density fitting or to utilize pre-fitted coefficients to estimate exchange and Coulomb contributions. This flexibility enhances efficiency and accuracy in modeling molecular interactions, especially in systems where polarization effects are significant.

Revisiting Textbook Azide-Clock Reactions: A "Propeller-Crawling" Mechanism Explains Differences in Rates.

An ongoing challenge to chemists is the analysis of pathways and kinetics for chemical reactions in solution, including transient structures between the reactants and products that are difficult to resolve using laboratory experiments. Here, we enabled direct molecular dynamics simulations of a textbook series of chemical reactions on the hundreds of ns to μs time scale using the weighted ensemble (WE) path sampling strategy with hybrid quantum mechanical/molecular mechanical (QM/MM) models. We focused on azide-clock reactions involving addition of an azide anion to each of three long-lived trityl cations in an acetonitrile-water solvent mixture. Results reveal a two-step mechanism: (1) diffusional collision of reactants to form an ion-pair intermediate; (2) "activation" or rearrangement of the intermediate to the product. Our simulations yield not only reaction rates that are within error of experiment but also rates for individual steps, indicating the activation step as rate-limiting for all three cations. Further, the trend in reaction rates is due to dynamical effects, i.e., differing extents of the azide anion "crawling" along the cation's phenyl-ring "propellers" during the activation step. Our study demonstrates the power of analyzing pathways and kinetics to gain insights on reaction mechanisms, underscoring the value of including WE and other related path sampling strategies in the modern toolbox for chemists.

High Color Conversion Efficiency Realized in Graphene‐Connected Nanorod Micro‐LEDs Using Hybrid Ag Nanoparticles and Quantum Dots

AbstractIn this paper, a uniform nanorod (NR) array is etched onto the surface of Micro‐Light‐Emitting‐Diodes (µLEDs) and mix Ag nanoparticles (NPs) with QDs to fill the gaps between the nanorods. Simultaneously, the study utilizes graphene to connect individual nanorods and enhance current spreading. The nanorod array's structure significantly reduces the distance between the QDs and the quantum well (QW), reducing energy loss from the excitation light source through a non‐radiative energy transfer (NRET) mechanism. Additionally, the Ag NPs function as localized surface plasmons (LSPs), further enhancing the CCE of QDs via the absorption resonance. In this study, the effects of two types of Ag NPs are compared with different absorption resonance peaks on device performance. The results demonstrate that Ag NPs with absorption resonance peaks matching the emission wavelength of QDs play a more crucial role in the system. This configuration achieves a CCE of 77.78% for µLEDs with nanorod arrays, operating at a current of 10 mA. Compared to the conventional µLED structure with QDs only on the surface, the proposed method improves the CCE of µLEDs by an impressive 86.5%. This outcome underscores the significant contribution of the NR structure and LSPs in enhancing the CCE of QD‐µLEDs.

Development of flip-chip technology for the optical drive of superconducting circuits

We discuss the flip-chip mounting process of photodiodes and fiber sleeves on silicon substrates to meet the increasing demand for fabrication of highly integrated and hybrid quantum circuits for operation at cryogenic temperatures. To further increase the yield and success rate of the flip-chip procedure, the size of the gold stud bumps, and flip-chip parameters were optimized. Moreover, to connect optical fibers to the photodiodes in an optimal position, the fiber sleeves were aligned with specially fabricated alignment circles before applying thermocompression with the flip-chip machine. The mounted photodiodes were tested at both room temperature and cryogenic temperature, and we find that mechanical imperfections of the sleeve-ferrule combination limit the overall alignment accuracy. The experimental results show that our flip-chip process is very reliable and promising for various optical and electrical applications and, thus, paves the way for fabrication of hybrid chips, multi-chip modules and chip-on-chip solutions, which are operated at cryogenic temperatures.

Correlative Micro‐Photoluminescence Study on Hybrid Quantum‐Well InGaN Red Light‐Emitting Diodes

To investigate nonradiative recombination processes in indium gallium nitride (InGaN)‐based red light‐emitting diodes (LEDs), an InGaN‐based red LED with a hybrid quantum well (QW) structure consisting of red and blue single quantum wells (SQWs) is characterized by micro‐photoluminescence (μ‐PL) spectroscopy. The μ‐PL mapping of the red emission reveals numerous dark spots with various sizes and contrasts. Not only the red and blue (from a blue SQW) but green emission bands are observed at some red dark spots, suggesting that indium (In) segregation is one of the causes of nonradiative recombination in the red emission. Comparing the blue and green emission images to the red emission image reveals that the dark spots in the intensity map of the red emission can be classified into four types. Through this correlative analysis, the red dark spots associated with the dark areas in the intensity map of the blue emission are attributed to the major nonradiative recombination centers in the red emission.

Hybrid quantum neural network structures for image multi-classification

Image classification is a fundamental problem in computer vision, and neural networks provide an effective solution. With the advancement of quantum technology, quantum neural networks have attracted a lot of attention. However, they are only suitable for low-dimensional data and require dimensionality reduction and quantum encoding. Two image classification methods have recently emerged: one employs PCA for dimensionality reduction and angular encoding, and the other integrates QNN into a CNN to improve performance. Despite numerous algorithms, the differences between them remain unclear. This study explores these algorithms’ performance in multi-class image classification and proposes an optimized hybrid quantum neural network suitable for the current environment. As the number of classes increases, research on PCA-based quantum algorithms reveals the barren plateau problem of QNN, which is not suitable for multiple classes in a hybrid setting. Our proposed model combining traditional CNN with QNN addresses QNN’s multi-class training difficulties to some extent and achieves satisfactory classification results. Nevertheless, its accuracy remains below that of the top-performing CNN models. Furthermore, we investigate transfer learning in hybrid quantum neural network models and assess the performance of our models on the quantum hardware from IBM. In conclusion, quantum neural networks show promise but require further research and optimization, facing challenges ahead.

Carbon-silicon based hybrid quantum dot short wave infrared photodetector

Recently infrared photodetectors based on low-dimensional semiconductors have developed rapidly. However, due to its poor light absorption and incompatibility with traditional silicon-based readout circuit processes, the sensitivity and integration of such photodetectors are limited. In this work, we proposes a 64 × 64 quantum dot short-wavelength infrared (SWIR) photodetector composed of carbon nanotube thin film transistor (CNT TFT) and silicon-based ROIC. CNT TFT's gate is constructed by PbS colloidal quantum dots (PbS CQDs) which improve the absorption rate of infrared light. The generated photovoltage is amplified and converted in situ by CNT TFT. Notably, under infrared radiation of 1300 nm, the noise equivalent current reach up to 1.25*10−13A/Hz1/2. At a drain-source bias (Vds)= -0.1 V. The device exhibits detectivity of 5.6*1013 Jones and a fast response of 0.57 ms. The silicon-based ROIC is implemented by CMOS 0.18um process, with a power supply voltage of 1.8 V. It mainly includes a programmable integrator, a sampling and holding circuit, and a 10bit/2.5 MHz successive approximation analog-to-digital converter (SAR ADC). The programmable integrator has four levels of integral gain to meet the application requirements of different infrared light intensities. The experiment results show that the imaging function of the overall photodetector is correct, laying the foundation for the development of carbon-silicon based heterojunction integrated photodetector in the future.

Correlation between two distant quasiparticles in separate superconducting islands mediated by a single spin

Controlled coupling between distant particles is a key requirement for the implementation of quantum information technologies. A promising platform are hybrid systems of semiconducting quantum dots coupled to superconducting islands, where the tunability of the dots is combined with the macroscopic coherence of the islands to produce states with non-local correlations, e.g. in Cooper pair splitters. Electrons in hybrid quantum dots are typically not amenable to long-distance spin alignment as they tend to be screened into a localized singlet state by bound superconducting quasiparticles. However, two quasiparticles coming from different superconductors can overscreen the quantum dot into a doublet state, leading to ferromagnetic correlations between the superconducting islands. We present experimental evidence of a stabilized overscreened state, implying correlated quasiparticles over a micrometer distance. We propose alternating chains of quantum dots and superconducting islands as a novel platform for controllable large-scale spin coupling.

Quantum sensing of magnetic fields with molecular spins

Spins are prototypical systems with the potential to probe magnetic fields down to the atomic scale limit. Exploiting their quantum nature through appropriate sensing protocols allows to enlarge their applicability to fields not always accessible by classical sensors. Here we first show that quantum sensing protocols for AC magnetic fields can be implemented with molecular spin ensembles embedded into hybrid quantum circuits. We then show that, using only echo detection at microwave frequency and no optical readout, Dynamical Decoupling protocols synchronized with the AC magnetic fields can enhance sensitivity up to S ≈ 10−10 − 10−9 T Hz−1/2 with a low (4-5) number of applied pulses. These results paves the way for the development of strategies to exploit molecular spins as quantum sensors.