This paper considers the problem for finding the $(\delta,\epsilon)$-Goldstein stationary point of Lipschitz continuous objective, which is a rich function class to cover a great number of important applications. We construct a zeroth-order quantum estimator for the gradient of the smoothed surrogate. Based on such estimator, we propose a novel quantum algorithm that achieves a query complexity of $\tilde{\mathcal{O}}(d^{3/2}\delta^{-1}\epsilon^{-3})$ on the stochastic function value oracle, where $d$ is the dimension of the problem. We also enhance the query complexity to $\tilde{\mathcal{O}}(d^{3/2}\delta^{-1}\epsilon^{-7/3})$ by introducing a variance reduction variant. Our findings demonstrate the clear advantages of utilizing quantum techniques for non-convex non-smooth optimization, as they outperform the optimal classical methods on the dependency of $\epsilon$ by a factor of $\epsilon^{-2/3}$.
As quantum computers continue to become more capable, the possibilities of their applications increase. For example, quantum techniques are being integrated with classical neural networks to perform machine learning. In order to be used in this way, or for any other widespread use like quantum chemistry simulations or cryptographic applications, classical data must be converted into quantum states through quantum encoding. There are three fundamental encoding methods: basis, amplitude, and rotation, as well as several proposed combinations. This study explores the encoding methods, specifically in the context of hybrid quantum-classical machine learning. Using the QuClassi quantum neural network architecture to perform binary classification of the `3' and `6' digits from the MNIST datasets, this study obtains several metrics such as accuracy, entropy, loss, and resistance to noise, while considering resource usage and computational complexity to compare the three main encoding methods.
Quantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits in various applications, challenges such as large-size data encoding in the input stage and the reliance on quantum resources in the inference stage limit its practicality for tasks like fine-tuning large language models (LLMs). Quantum parameter generation, a novel approach of QML, addresses these limitations by using quantum neural networks (QNNs) to generate classical model weights (parameters) exclusively during training, thereby decoupling inference from quantum hardware. In this work, we introduce Quantum Parameter Adaptation (QPA) in the framework of quantum parameter generation, which integrates QNNs with a classical multi-layer perceptron mapping model to generate parameters for fine-tuning methods. Using Gemma-2 and GPT-2 as case studies, QPA demonstrates significant parameter reduction for parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), while maintaining comparable or improved performance in text generation tasks. Specifically, QPA reduces the number of parameters to $52.06\%$ of the original LoRA for GPT-2 with a slight performance gain of $0.75\%$, and to $16.84\%$ for Gemma-2, with a marginal performance improvement of $0.07\%$. These results highlight QPA's ability to achieve efficient parameter reduction without sacrificing performance in the quantum parameter generation framework. This work showcases the potential of quantum-enhanced parameter reduction, offering a scalable quantum-classical solution for fine-tuning LLMs while preserving the feasibility of inference on classical hardware.
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework designed to enhance the detection of deepfake audio, leveraging the computational power of quantum machine learning (QML). The QT-CNN employs a hybrid quantum-classical approach, integrating Quantum Neural Networks (QNNs) with classical neural architectures to optimize training efficiency while reducing the number of trainable parameters. Our method incorporates a novel quantum-to-classical parameter mapping that effectively utilizes quantum states to enhance the expressive power of the model, achieving up to 70% parameter reduction compared to classical models without compromising accuracy. Data pre-processing involved extracting essential audio features, label encoding, feature scaling, and constructing sequential datasets for robust model evaluation. Experimental results demonstrate that the QT-CNN achieves comparable performance to traditional CNNs, maintaining high accuracy during training and testing phases across varying configurations of QNN blocks. The QT framework's ability to reduce computational overhead while maintaining performance underscores its potential for real-world applications in deepfake detection and other resource-constrained scenarios. This work highlights the practical benefits of integrating quantum computing into artificial intelligence, offering a scalable and efficient approach to advancing deepfake detection technologies.
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features. In recent years, the development of quantum computing has discovered that quantum convolution can improve network accuracy, possibly due to potential quantum advantages. This article proposes a hybrid neural network containing quantum and classical networks for fast magnetic resonance imaging, and conducts experiments on a quantum computer simulation system. The experimental results indicate that the hybrid network has achieved excellent reconstruction results, and also confirm the feasibility of applying hybrid quantum-classical neural networks into the image reconstruction of rapid magnetic resonance imaging.
Chang-Kang Hu, Guixu Xie, Kasper Poulsen, Yuxuan Zhou, Ji Chu, Chilong Liu, Ruiyang Zhou, Haolan Yuan, Yuecheng Shen, Song Liu, Nikolaj T. Zinner, Dian Tan, Alan C. Santos, Dapeng Yu Quantum simulators are ideal platforms to investigate quantum phenomena that are inaccessible through conventional means, such as the limited resources of classical computers to address large quantum systems or due to constraints imposed by fundamental laws of nature. Here, through a digitized adiabatic evolution, we report an experimental simulation of antiferromagnetic (AFM) and ferromagnetic (FM) phase formation induced by spontaneous symmetry breaking (SSB) in a three-generation Cayley tree-like superconducting lattice. We develop a digital quantum annealing algorithm to mimic the system dynamics, and observe the emergence of signatures of SSB-induced phase transition through a connected correlation function. We demonstrate that the signature of phase transition from classical AFM to quantum FM happens in systems undergoing zero-temperature adiabatic evolution with only nearest-neighbor interacting systems, the shortest range of interaction possible. By harnessing properties of the bipartite Renyi entropy as an entanglement witness, we observe the formation of entangled quantum FM and AFM phases. Our results open perspectives for new advances in condensed matter physics and digitized quantum annealing.
Chang-Kang Hu, Chao Wei, Chilong Liu, Liangyu Che, Yuxuan Zhou, Guixu Xie, Haiyang Qin, Guantian Hu, Haolan Yuan, Ruiyang Zhou, Song Liu, Dian Tan, Tao Xin, Dapeng Yu Quantum state tomography (QST) via local measurements on reduced density matrices (LQST) is a promising approach but becomes impractical for large systems. To tackle this challenge, we developed an efficient quantum state tomography method inspired by quantum overlapping tomography [Phys. Rev. Lett. 124, 100401(2020)], which utilizes parallel measurements (PQST). In contrast to LQST, PQST significantly reduces the number of measurements and offers more robustness against shot noise. Experimentally, we demonstrate the feasibility of PQST in a tree-like superconducting qubit chip by designing high-efficiency circuits, preparing W states, ground states of Hamiltonians and random states, and then reconstructing these density matrices using full quantum state tomography (FQST), LQST, and PQST. Our results show that PQST reduces measurement cost, achieving fidelities of 98.68\% and 95.07\% after measuring 75 and 99 observables for 6-qubit and 9-qubit W states, respectively. Furthermore, the reconstruction of the largest density matrix of the 12-qubit W state is achieved with the similarity of 89.23\% after just measuring $243$ parallel observables, while $3^{12}=531441$ complete observables are needed for FQST. Consequently, PQST will be a useful tool for future tasks such as the reconstruction, characterization, benchmarking, and properties learning of states.
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Neuroimaging in Frontotemporal Dementia (NIFD), present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical high-performance computing (HPC) nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ (Noisy Intermediate-Scale Quantum) era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline's superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.
In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a multi-layer perceptron (MLP) for this task, but it struggles with scalability and interpretability. To address these issues, we propose replacing the MLP with a tensor network-based model and introducing a distributed circuit ansatz designed for large-scale quantum machine learning with multiple small quantum processing unit nodes. This approach enhances scalability, efficiently represents high-dimensional data, and maintains a compact model structure. Our enhanced QT framework retains the benefits of reduced parameter count and independence from quantum resources during inference. Experimental results on benchmark datasets demonstrate that the tensor network-based QT framework achieves competitive performance with improved efficiency and generalization, offering a practical solution for scalable hybrid quantum-classical machine learning.
In this work, we introduce the Federated Quantum-Train (QT) framework, which integrates the QT model into federated learning to leverage quantum computing for distributed learning systems. Quantum client nodes employ Quantum Neural Networks (QNNs) and a mapping model to generate local target model parameters, which are updated and aggregated at a central node. Testing with a VGG-like convolutional neural network on the CIFAR-10 dataset, our approach significantly reduces qubit usage from 19 to as low as 8 qubits while reducing generalization error. The QT method mitigates overfitting observed in classical models, aligning training and testing accuracy and improving performance in highly compressed models. Notably, the Federated QT framework does not require a quantum computer during inference, enhancing practicality given current quantum hardware limitations. This work highlights the potential of integrating quantum techniques into federated learning, paving the way for advancements in quantum machine learning and distributed learning systems.
In the present paper, a multi-frequency optical non-reciprocal transmission is first realized by using a non-Hermitian multi-mode resonator array.We find that the non-reciprocity can be used to route optical signals, to prevent the reverse flow of noise, and find that the multi-frequency can be used to enhance information processing. In terms of the Scully-Lamb model and gain saturation effect, we accomplish a dual-frequency non-reciprocal transmission by introducing nonlinearity into a linear array of four-mode resonators. For example, a directional cyclic amplifier is constructed with non-reciprocal units. As potential applications, the non-reciprocity optical systems can be employed in dual-frequency control, parallel information processing, photonic integrated circuits, optical devices and so on.
Observable estimation is a core primitive in NISQ-era algorithms targeting quantum chemistry applications. To reduce the state preparation overhead required for accurate estimation, recent works have proposed various simultaneous measurement schemes to lower estimator variance. Two primary grouping schemes have been proposed: fully commutativity (FC) and qubit-wise commutativity (QWC), with no compelling means of interpolation. In this work we propose a generalized framework for designing and analyzing context-aware hybrid FC/QWC commutativity relations. We use our framework to propose a noise-and-connectivity aware grouping strategy: Generalized backend-Aware pauLI Commutation (GALIC). We demonstrate how GALIC interpolates between FC and QWC, maintaining estimator accuracy in Hamiltonian estimation while lowering variance by an average of 20% compared to QWC. We also explore the design space of near-term quantum devices using the GALIC framework, specifically comparing device noise levels and connectivity. We find that error suppression has a more than $13\times$ larger impact on device-aware estimator variance than qubit connectivity with even larger correlation differences in estimator biases.
Yang Li, Wen-Qi Cai, Ji-Gang Ren, Chao-Ze Wang, Meng Yang, Liang Zhang, Hui-Ying Wu, Liang Chang, Jin-Cai Wu, Biao Jin, Hua-Jian Xue, Xue-Jiao Li, Hui Liu, Guang-Wen Yu, Xue-Ying Tao, Ting Chen, Chong-Fei Liu, Wen-Bin Luo, Jie Zhou, Hai-Lin Yong, et al (21) A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guaranteed by the laws of quantum mechanics. A quantum satellite constellation offers a solution to facilitate the quantum network on a global scale. The Micius satellite has verified the feasibility of satellite quantum communications, however, scaling up quantum satellite constellations is challenging, requiring small lightweight satellites, portable ground stations and real-time secure key exchange. Here we tackle these challenges and report the development of a quantum microsatellite capable of performing space-to-ground QKD using portable ground stations. The quantum microsatellite features a payload weighing approximately 23 kg, while the portable ground station weighs about 100 kg. These weights represent reductions by more than an order and two orders of magnitude, respectively, compared to the Micius satellite. Additionally, we multiplex bidirectional satellite-ground optical communication with quantum communication, enabling key distillation and secure communication in real-time. Using the microsatellite and the portable ground stations, we demonstrate satellite-based QKD with multiple ground stations and achieve the sharing of up to 0.59 million bits of secure keys during a single satellite pass. The compact quantum payload can be readily assembled on existing space stations or small satellites, paving the way for a satellite-constellation-based quantum and classical network for widespread real-life applications.
In the ultrastrong-coupling regime, the quantum Rabi model can exhibit quantum phase transition (QPT) when the ratio of the qubit transition frequency to the frequency of the cavity field approaches infinity. However, it is challenging to control the QPT in few-body systems because of the limited coupling strength and the A^2 terms. Here, we propose a practical scheme to manipulate the QPT of quantum Rabi model in the strong-coupling regime. By applying a periodic frequency modulation to the two-level system in a standard quantum Rabi model in the strong-coupling regime, an anisotropic quantum Rabi model with ultrastrong and tunable coupling strengths for rotating and counter-rotating terms is obtained. The ground-state and excitation energy of this model in terms of the modulation parameters are studied. We find that the QPT of quantum Rabi model can be observed in the strong-coupling regime and externally controlled by the modulation.
This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. Leveraging a distributed framework, we address the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, which are hindered by limited qubit counts and high error rates. Our approach decomposes large QAOA problems into smaller subproblems, distributing them across multiple Quantum Processing Units (QPUs) to enhance scalability and performance. The noise-aware strategy incorporates error mitigation techniques to optimize qubit fidelity and gate operations, ensuring reliable quantum computations. We evaluate the efficacy of our framework using the HamilToniQ Benchmarking Toolkit, which quantifies the performance across various quantum hardware configurations. The results demonstrate that our distributed QAOA framework achieves significant improvements in computational speed and accuracy, showcasing its potential to solve complex optimization problems efficiently in the NISQ era. This work sets the stage for advanced algorithmic strategies and practical quantum system enhancements, contributing to the broader goal of achieving quantum advantage.
We present novel path-slicing strategies integrated with quantum local search to optimize solutions for the Traveling Salesman Problem (TSP), addressing the limitations of current Noisy Intermediate-Scale Quantum (NISQ) technologies. Our hybrid quantum-classical approach leverages classical path initialization and quantum optimization to effectively manage the computational challenges posed by the TSP. We explore various path slicing methods, including k-means and anti-k-means clustering, to divide the TSP into manageable subproblems. These are then solved using quantum or classical solvers. Our analysis, performed on multiple TSP instances from the TSPlib, demonstrates the ability of our strategies to achieve near-optimal solutions efficiently, highlighting significant improvements in solving efficiency and resource utilization. This approach paves the way for future applications in larger combinatorial optimization scenarios, advancing the field of quantum optimization.
Flood prediction is a critical challenge in the context of climate change, with significant implications for ecosystem preservation, human safety, and infrastructure protection. In this study, we tackle this problem by applying the Quantum-Train (QT) technique to a forecasting Long Short-Term Memory (LSTM) model trained by Quantum Machine Learning (QML) with significant parameter reduction. The QT technique, originally successful in the A Matter of Taste challenge at QHack 2024, leverages QML to reduce the number of trainable parameters to a polylogarithmic function of the number of parameters in a classical neural network (NN). This innovative framework maps classical NN weights to a Hilbert space, altering quantum state probability distributions to adjust NN parameters. Our approach directly processes classical data without the need for quantum embedding and operates independently of quantum computing resources post-training, making it highly practical and accessible for real-world flood prediction applications. This model aims to improve the efficiency of flood forecasts, ultimately contributing to better disaster preparedness and response.
Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face challenges such as data encoding and the use of quantum computers during the inference stage. We apply the Quantum-Train method to reinforcement learning tasks, called QTRL, training the classical policy network model using a quantum machine learning model with polylogarithmic parameter reduction. This QTRL approach eliminates the data encoding issues of conventional quantum machine learning and reduces the training parameters of the corresponding classical policy network. Most importantly, the training result of the QTRL is a classical model, meaning the inference stage only requires classical computer. This is extremely practical and cost-efficient for reinforcement learning tasks, where low-latency feedback from the policy model is essential.
Ziqiang Xu, Yan Shao, Chun Huang, Genyu Hu, Shihao Hu, Zhi-Lin Li, Xiaoyu Hao, Yanhui Hou, Teng Zhang, Jin-An Shi, Chen Liu, Jia-Ou Wang, Wu Zhou, Jiadong Zhou, Wei Ji, Jingsi Qiao, Xu Wu, Hong-Jun Gao, Yeliang Wang As a fundamental structural feature, the symmetry of materials determines the exotic quantum properties in transition metal dichalcogenides (TMDs) with charge density wave (CDW). Breaking the inversion symmetry, the Janus structure, an artificially constructed lattice, provides an opportunity to tune the CDW states and the related properties. However, limited by the difficulties in atomic-level fabrication and material stability, the experimental visualization of the CDW states in 2D TMDs with Janus structure is still rare. Here, using surface selenization of VTe2, we fabricated monolayer Janus VTeSe. With scanning tunneling microscopy, an unusual root13-root13 CDW state with threefold rotational symmetry breaking was observed and characterized. Combined with theoretical calculations, we find this CDW state can be attributed to the charge modulation in the Janus VTeSe, beyond the conventional electron-phonon coupling. Our findings provide a promising platform for studying the CDW states and artificially tuning the electronic properties toward the applications.
Utilizing nonlinear elements, SU(1,1) interferometers demonstrate superior phase sensitivity compared to passive interferometers. However, the precision is significantly impacted by photon losses, particularly internal losses. We propose a theoretical scheme to improve the precision of phase measurement using homodyne detection by implementing number-conserving operations (PA-then-PS and PS-then-PA) within the SU(1,1) interferometer, with the coherent state and the vacuum state as the input states. We analyze the effects of number-conserving operations on the phase sensitivity, the quantum Fisher information, and the quantum Cramer-Rao bound under both ideal and photon losses scenarios. Our findings reveal that the internal non-Gaussian operations can enhance the phase sensitivity and the quantum Fisher information, and effectively improve the robustness of the SU(1,1) interferometer against internal photon losses. Notably, the PS-then-PA scheme exhibits superior improvement in both ideal and photon losses cases in terms of phase sensitivity. Moreover, in the ideal case, PA-then-PS scheme slightly outperforms PS-then-PA scheme in terms of the quantum Fisher information and the Quantum Cramer-Rao. However, in the presence of photon losses, PS-then-PA scheme demonstrates a greater advantage.
We propose an innovative Parallel Quantum Local Search (PQLS) methodology that leverages the capabilities of small-scale quantum computers to efficiently address complex combinatorial optimization problems. Traditional Quantum Local Search (QLS) methods face limitations due to the sequential nature of solving sub-problems, which arises from dependencies between their solutions. Our approach transcends this constraint by simultaneously executing multiple QLS pathways and aggregating their most effective outcomes at certain intervals to establish a ``generation''. Each subsequent generation commences with the optimal solution from its predecessor, thereby significantly accelerating the convergence towards an optimal solution. Our findings demonstrate the profound impact of parallel quantum computing in enhancing the resolution of Ising problems, which are synonymous with combinatorial optimization challenges.
We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model, which significantly reduces the parameter count from $M$ to $O(\text{polylog} (M))$ during training. Our experiments demonstrate QT's effectiveness in classification tasks, offering insights into its potential to revolutionize machine learning by leveraging quantum computational advantages. This approach not only improves model efficiency but also reduces generalization errors, showcasing QT's potential across various machine learning applications.
Zichen Lian, Yongchao Wang, Yongqian Wang, Yang Feng, Zehao Dong, Shuai Yang, Liangcai Xu, Yaoxin Li, Bohan Fu, Yuetan Li, Wanjun Jiang, Chang Liu, Jinsong Zhang, Yayu Wang The interplay between nontrivial band topology and layered antiferromagnetism in MnBi2Te4 has opened up a new avenue for exploring topological phases of matter. Representative examples include the quantum anomalous Hall effect and axion insulator state observed in odd and even number layers of MnBi2Te4, when the top and bottom surfaces have parallel and antiparallel spin alignments respectively. The rich and complex spin dynamics associated with the van der Waals antiferromagnetic order is expected to generate novel topological phases and phase transitions that are unique to MnBi2Te4. Here we fabricate a device of 7-septuple-layer MnBi2Te4 covered with AlOx capping layer, which enables the investigation of antiferromagnetic quantum anomalous Hall effect over wide parameter spaces. By tuning the gate voltage and perpendicular magnetic field, we uncover a cascade of quantum phase transitions that can be attributed to the influence of spin configurations on charge transport. Furthermore, we find that an in-plane magnetic field enhances both the coercive field and exchange gap of the surface state, in sharp contrast to that in ferromagnetic quantum anomalous Hall state. We propose that these peculiar features arise from the spin flip and flop transitions inherent to van der Waals antiferromagnet. The versatile tunability of the quantum anomalous Hall effect in MnBi2Te4 paves the way for potential applications in topological antiferromagnetic spintronics.
We uncover emergent universality arising in the equilibration dynamics of multimode continuous-variable systems. Specifically, we study the ensemble of pure states supported on a small subsystem of a few modes, generated by Gaussian measurements on the remaining modes of a globally pure bosonic Gaussian state. We find that beginning from sufficiently complex global states, such as random Gaussian states and product squeezed states coupled via a deep array of linear optical elements, the induced ensemble attains a universal form, independent of the choice of measurement basis: it is composed of unsqueezed coherent states whose displacements are distributed normally and isotropically, with variance depending on only the particle-number density of the system. We further show that the emergence of such a universal form is consistent with a generalized maximum entropy principle, which endows the limiting ensemble, which we call the "Gaussian Scrooge distribution", with a special quantum information-theoretic property of having minimal accessible information. Our results represent a conceptual generalization of the recently introduced notion of "deep thermalization" in discrete-variable quantum many-body systems -- a novel form of equilibration going beyond thermalization of local observables -- to the realm of continuous-variable quantum systems. Moreover, it demonstrates how quantum information-theoretic perspectives can unveil new physical phenomena and principles in quantum dynamics and statistical mechanics.
Quantum state preparation involves preparing a target state from an initial system, a process integral to applications such as quantum machine learning and solving systems of linear equations. Recently, there has been a growing interest in qumodes due to advancements in the field and their potential applications. However there is a notable gap in the literature specifically addressing this area. This paper aims to bridge this gap by providing performance benchmarks of various optimizers used in state preparation with Variational Quantum Algorithms. We conducted extensive testing across multiple scenarios, including different target states, both ideal and sampling simulations, and varying numbers of basis gate layers. Our evaluations offer insights into the complexity of learning each type of target state and demonstrate that some optimizers perform better than others in this context. Notably, the Powell optimizer was found to be exceptionally robust against sampling errors, making it a preferred choice in scenarios prone to such inaccuracies. Additionally, the Simultaneous Perturbation Stochastic Approximation optimizer was distinguished for its efficiency and ability to handle increased parameter dimensionality effectively.
Despite quantum computing's rapid development, current systems remain limited in practical applications due to their limited qubit count and quality. Various technologies, such as superconducting, trapped ions, and neutral atom quantum computing technologies are progressing towards a fault tolerant era, however they all face a diverse set of challenges in scalability and control. Recent efforts have focused on multi-node quantum systems that connect multiple smaller quantum devices to execute larger circuits. Future demonstrations hope to use quantum channels to couple systems, however current demonstrations can leverage classical communication with circuit cutting techniques. This involves cutting large circuits into smaller subcircuits and reconstructing them post-execution. However, existing cutting methods are hindered by lengthy search times as the number of qubits and gates increases. Additionally, they often fail to effectively utilize the resources of various worker configurations in a multi-node system. To address these challenges, we introduce FitCut, a novel approach that transforms quantum circuits into weighted graphs and utilizes a community-based, bottom-up approach to cut circuits according to resource constraints, e.g., qubit counts, on each worker. FitCut also includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms the Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 3.88 times on the worker side, achieving a system-wide improvement of 2.86 times.
Entanglement purification protocols, designed to improve the fidelity of Bell states over quantum networks for inter-node communications, have attracted significant attention over the last few decades. These protocols have great potential to resolve a core challenge in quantum networking of generating high-fidelity Bell states. However, previous studies focused on the theoretical discussion with limited consideration of realistic errors. Studies of dynamically selecting the right purification protocol under various realistic errors that populate in practice have yet to be performed. In this work, we study the performance of various purification protocols under realistic errors by conducting density matrix simulations over a large suite of error models. Based on our findings of how specific error channels affect the performance of purification protocols, we propose a module that can be embedded in the quantum network. This module determines and selects the appropriate purification protocol, considering not only expected specifications from the network layer but also the capabilities of the physical layer. Finally, the performance of our proposed module is verified using two benchmark categories. Compared with the default approach and exhaustive search approach, we show a success rate approaching 90% in identifying the optimal purification protocol for our target applications.
Dissipation and decoherence of quantum systems in thermal environments is important to various spectroscopies. It is generally believed that dissipation can broaden the line shape of spectroscopies, and thus stronger system-bath interaction can result in more significant homogeneous broadening of two-dimensional electronic spectroscopy (2DES). Here we show that the case can be the opposite in the regime of electromagnetically induced transparency (EIT). We predict that assisted by EIT, the homogeneous broadening of the 2DES at a higher temperature can be significantly reduced due to the detailed balance. This anomalous effect is due to the long-lasting off-diagonal peaks in 2DES.
In this paper, we explore using the Harrow-Hassidim-Lloyd (HHL) algorithm to address scientific and engineering problems through quantum computing utilizing the NWQSim simulation package on high-performance computing. Focusing on domains such as power-grid management and heat transfer problems, we demonstrate the correlations of the precision of quantum phase estimation, along with various properties of coefficient matrices, on the final solution and quantum resource cost in iterative and non-iterative numerical methods such as Newton-Raphson method and finite difference method, as well as their impacts on quantum error correction costs using Microsoft Azure Quantum resource estimator. We conclude the exponential resource cost from quantum phase estimation before and after quantum error correction and illustrate a potential way to reduce the demands on physical qubits. This work lays down a preliminary step for future investigations, urging a closer examination of quantum algorithms' scalability and efficiency in domain applications.
Although there have been remarkable advances in quantum computing (QC), it remains crucial to simulate quantum programs using classical large-scale parallel computing systems to validate quantum algorithms, comprehend the impact of noise, and develop resilient quantum applications. This is particularly important for bridging the gap between near-term noisy-intermediate-scale-quantum (NISQ) computing and future fault-tolerant quantum computing (FTQC). Nevertheless, current simulation methods either lack the capability to simulate noise, or simulate with excessive computational costs, or do not scale out effectively. In this paper, we propose TANQ-Sim, a full-scale density matrix based simulator designed to simulate practical deep circuits with both coherent and non-coherent noise. To address the significant computational cost associated with such simulations, we propose a new density-matrix simulation approach that enables TANQ-Sim to leverage the latest double-precision tensorcores (DPTCs) in NVIDIA Ampere and Hopper GPUs. To the best of our knowledge, this is the first application of double-precision tensorcores for non-AI/ML workloads. To optimize performance, we also propose specific gate fusion techniques for density matrix simulation. For scaling, we rely on the advanced GPU-side communication library NVSHMEM and propose effective optimization methods for enhancing communication efficiency. Evaluations on the NERSC Perlmutter supercomputer demonstrate the functionality, performance, and scalability of the simulator. We also present three case studies to showcase the practical usage of TANQ-Sim, including teleportation, entanglement distillation, and Ising simulation. TANQ-Sim will be released on GitHub.
In recent years, significant progress has been made in utilizing the divergence of spectrum response rate at the exceptional point (EP) for sensing in classical systems, while the use and characterization of quantum EPs for sensing have been largely unexplored. For a quantum EP sensor, an important issue is the relation between the order of the quantum EP and the scaling of quantum Fisher information (QFI), an essential quantity for characterizing quantum sensors. Here we investigate multi-mode quadratic bosonic systems, which exhibit higher-order EP dynamics, but possess Hermitian Hamiltonians without Langevin noise, thus can be utilized for quantum sensing. We derive an exact analytic formula for the QFI, from which we establish a scaling relation between the QFI and the order of the EP. We apply the formula to study a three-mode EP sensor and a multi-mode bosonic Kitaev chain and show that the EP physics can significantly enhance the sensing sensitivity. Our work establishes the connection between two important fields: non-Hermitian EP dynamics and quantum sensing, and may find important applications in quantum information and quantum non-Hermitian physics.
The research focused on enhancing the measurement accuracy through the use of non-Gaussian states has garnered increasing attention. In this study, we propose a scheme to input the coherent state mixed with photon-catalyzed squeezed vacuum state into the Mach-Zender interferometer to enhance phase measurement accuracy. The findings demonstrate that photon catalysis, particularly multi-photon catalysis, can effectively improve the phase sensitivity of parity detection and the quantum Fisher information. Moreover, the situation of photon losses in practical measurement was studied. The results indicate that external dissipation has a greater influence on phase sensitivity than the internal dissipation. Compared to input coherent state mixed with squeezed vacuum state, the utilization of coherent state mixed photon-catalyzed squeezed vacuum state, particularly the mixed multi-photon catalyzed squeezed vacuum state as input, can enhance the phase sensitivity and quantum Fisher information. Furthermore, the phase measurement accuracy can exceed the standard quantum limit, and even surpass the Heisenberg limit. This research is expected to significantly contribute to quantum precision measurement.
Qubits are the fundamental building blocks of quantum information science and applications, whose concept is widely utilized in both quantum physics and quantum computation. While the significance of qubits and their implementation in physical devices have been extensively examined, now is the right time to revisit this understanding. In this paper, we introduce an abstract qubit model (AQM), offering a mathematical framework for higher-level algorithms and applications, and setting forth criteria for lower-level physical devices to enable quantum computation. We first provide a comprehensive definition of "qubits", regarded as the foundational principle for quantum computing algorithms (bottom-up support), and examine their requisites for devices (top-down demand). We then investigate the feasibility of relaxing specific requirements, thereby broadening device support while considering techniques that tradeoff extra costs to counterbalance this relaxation. Lastly, we delve into the quantum applications that only require partial support of "qubits", and discuss the physical systems with limited support of the AQM but remain valuable in quantum applications. AQM may serve as an intermediate interface between quantum algorithms and devices, facilitating quantum algorithm-device co-design.
Achieving high-performance computation on quantum systems presents a formidable challenge that necessitates bridging the capabilities between quantum hardware and classical computing resources. This study introduces an innovative distribution-aware Quantum-Classical-Quantum (QCQ) architecture, which integrates cutting-edge quantum software framework works with high-performance classical computing resources to address challenges in quantum simulation for materials and condensed matter physics. At the heart of this architecture is the seamless integration of VQE algorithms running on QPUs for efficient quantum state preparation, Tensor Network states, and QCNNs for classifying quantum states on classical hardware. For benchmarking quantum simulators, the QCQ architecture utilizes the cuQuantum SDK to leverage multi-GPU acceleration, integrated with PennyLane's Lightning plugin, demonstrating up to tenfold increases in computational speed for complex phase transition classification tasks compared to traditional CPU-based methods. This significant acceleration enables models such as the transverse field Ising and XXZ systems to accurately predict phase transitions with a 99.5% accuracy. The architecture's ability to distribute computation between QPUs and classical resources addresses critical bottlenecks in Quantum-HPC, paving the way for scalable quantum simulation. The QCQ framework embodies a synergistic combination of quantum algorithms, machine learning, and Quantum-HPC capabilities, enhancing its potential to provide transformative insights into the behavior of quantum systems across different scales. As quantum hardware continues to improve, this hybrid distribution-aware framework will play a crucial role in realizing the full potential of quantum computing by seamlessly integrating distributed quantum resources with the state-of-the-art classical computing infrastructure.
High-fidelity quantum non-demolition qubit measurement is critical to error correction and rapid qubit feedback in large-scale quantum computing. High-fidelity readout requires passing a short and strong pulse through the qubit's readout resonator, which is then processed by a sufficiently high bandwidth, high saturation power, and quantum-limited amplifier. We have developed a design pipeline that combines time-domain simulation of the un-truncated device Hamiltonian, fabrication constraints, and maximization of saturation power. We have realized an amplifier based on a modified NIST tri-layer Nb fabrication suite which utilizes an array of 25 radio frequency Superconducting QUantum Interference Devices (rf SQUIDs) embedded within a low-Q resonator powered by a high-power voltage pump delivered via a diplexer on the signal port. We show that, despite the intensity of the pump, the device is quantum-efficient and capable of high-fidelity measurement limited by state transitions in the transmon. We present experimental data demonstrating up to -91.2 dBm input saturation power with 20 dB gain, up to 28 MHz instantaneous bandwidth, and phase-preserving qubit measurements with 62% quantum efficiency.
In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with $M$ parameters to a quantum neural network (QNN) with $O(\text{polylog} (M))$ rotational gate angles, we can significantly reduce the number of parameters. These gate angles can be updated to train the classical NN. Unlike existing quantum machine learning (QML) methods, the results obtained from quantum computers using our approach can be directly used on classical computers. Numerical results on the MNIST and Iris datasets are presented to demonstrate the effectiveness of our approach. Additionally, we investigate the effects of deeper QNNs and the number of measurement shots for the QNN, followed by the theoretical perspective of the proposed method. This work opens a new branch of QML and offers a practical tool that can greatly enhance the influence of QML, as the trained QML results can benefit classical computing in our daily lives.
In this paper, how to modulate entanglement dynamics of two V-type atoms in dissipative cavity by detuning, weak measurement and weak measurement reversal is studied. The analytical solution of this model is obtained by solving Schrodinger Equation after diagonalizing Hamiltonian of dissipative cavity. It is discussed in detail how the entanglement dynamics is influenced by cavity--environment coupling, spontaneously generated interference (SGI) parameter, detuning between cavity with environment and weak measurement reversal. The results show that the entanglement dynamics of different initial states obviously depends on coupling, SGI parameter, detuning and reversing measurement strength. The stronger coupling, the smaller SGI parameter, the larger detuning and the bigger reversing measurement strength can all not only protect but also generate the entanglement, and the detuning is more effectively in tne strong coupling regime than the weak measurement reversal, which is more effectively than the SGI parameter. We also give corresponding physical interpretations.
We investigate the amplification of the genuine tripartite nonlocality(GTN) and the genuine tripartite entanglement(GTE) of Dirac particles in the background of a Schwarzschild black hole by a local filtering operation under decoherence. It is shown that the physically accessible GTN will be completely destroyed by decoherence, which means that the physically accessible GTN will not exist in the system. Particularly, the local filtering operation can make the physically accessible GTN appear within a certain range of Hawking temperature, namely, the local filtering operation can cause the physically accessible GTN to be generated in the system coupled with the environment, which is not discovered before and is benefit for the quantum information processing. Furthermore, we also find that the physically accessible GTE approaches a stable value in the limit of infinite Hawking temperature for most cases, but if the decoherence parameter $p$ is less than 1, the ``sudden death'' of GTE will take place when the decoherence strength is large enough. It is worth noting that the nonzero stable value of GTE can be increased by performing the local filtering operation, even in the presence of decoherence. Finally, we explore the generation of physically inaccessible GTN and GTE of other tripartite subsystems under decoherence, it is shown that the physically inaccessible GTN cannot be produced, but the physically inaccessible GTE can be produced. In addition, we can see that the generated physically inaccessible GTE can be increased by applying the local filtering operation.
To unequivocally distinguish the genuine quantumness from classicality, a widely adopted approach appeals to the negativity within a join quasi-distribution representation as a compelling evidence for the nonclassical essence. However, to construct a joint quasi-distribution with negativity from experimental data typically proves to be highly cumbersome. Here we propose a computational approach utilizing a deep generative model integrated with color mapping to construct the bivariate joint quasi-distribution functions by processing three marginals. We first apply our model to predict the Wigner functions subject to thermal noises. Our model successfully predicts the Wigner functions with a prominent accuracy by processing three marginals of probability distributions. We also tackle a challenging problem of the canonical Hamiltonian ensemble representation (CHER), which is developed for characterizing the dynamical process nonclassicality. Furthermore, we also design optimal synthetic datasets to train the model for overcoming the ground-truth deficiency of the CHER problem. While trained with synthetic data, the physics-informed optimization enables our model to capture the detrimental effect of the thermal fluctuations on nonclassicality. Our approach also provides a significant reduction of the experimental efforts of constructing the Wigner functions of quantum states.
Simultaneous ground-state cooling of two levitated nanoparticles is a crucial prerequisite for investigation of macroscopic quantum effects such as quantum entanglement and quantum correlation involving translational motion of particles. Here we consider a coupled cavity-levitated-particle system and present a detailed derivation of its Hamiltonian. We find that the $y$-direction motions of the two particles are decoupled from the cavity field and both the $x$- and $z$-direction motions, and that the $z$-direction motions can be further decoupled from the cavity field and the $x$-direction motions by choosing proper locations of the particles. We study the simultaneous cooling of these mechanical modes in both the three-mode and five-mode cavity-levitated optomechanical models. It is found that there exists the dark-mode effect when the two tweezers have the same powers, which suppress the simultaneous ground-state cooling. Nevertheless, the simultaneous ground-state cooling of these modes can be realized by breaking the dark-mode effect under proper parameters. Our system provides a versatile platform to study quantum effects and applications in cavity-levitated optomechanical systems.
We study the non-equilibrium dynamics of kicked Ising models in $1+1$ dimensions which have interactions alternating between odd and even bonds in time. These models give rise to time-evolution equivalent to quantum circuits having both the global property of tri-unitarity (three 'arrows of time') and also the local property of second-level dual-unitarity, which constrains the behavior of pairs of local gates underlying the circuit under a space-time rotation. We identify a broad class of initial product states wherein the effect of the environment on a small subsystem can be exactly represented by influence matrices with simple Markovian structures, resulting in the subsystem's full dynamics being efficiently computable. We further find additional conditions under which the dynamics of entanglement can be solved for all times, yielding rich phenomenology ranging from linear growth at half the maximal speed allowed by locality, followed by saturation to maximum entropy (i.e., thermalization to infinite temperature); to entanglement growth with saturation to extensive but sub-maximal entropy. Our findings extend our knowledge of interacting quantum systems whose thermalizing dynamics can be efficiently and analytically computed, going beyond the well-known examples of integrable models, Clifford circuits, and dual-unitary circuits.
We propose and prove two theorems for determining the number of dark modes in linear two-component quantum networks composed of two types of bosonic modes. This is achieved by diagonalizing the two sub-networks of the same type of modes, mapping the networks to either a standard or a thick arrowhead matrix, and analyzing the linear dependence and independence between the column vectors associated with degenerate normal modes in the coupling matrix. We confirm the two theorems by checking the simultaneous ground-state cooling of the mechanical modes in linearized optomechanical networks. These results also work for linear fermionic networks and other networks described by quadratic coupled-mode Hamiltonian. The present method can be extended to study the dark-state effect in driven atom systems and to construct large decoherence-free subspaces for processing quantum information. This work will initiate the studies on dynamical, transport, and statistical properties of linear networks with decoupled subspaces.
Facilitating the ability to achieve logical qubit error rates below physical qubit error rates, error correction is anticipated to play an important role in scaling quantum computers. While many algorithms require millions of physical qubits to be executed with error correction, current superconducting qubit systems contain only hundreds of physical qubits. One of the most promising codes on the superconducting qubit platform is the surface code, requiring a realistically attainable error threshold and the ability to perform universal fault-tolerant quantum computing with local operations via lattice surgery and magic state injection. Surface code architectures easily generalize to single-chip planar layouts, however space and control hardware constraints point to limits on the number of qubits that can fit on one chip. Additionally, the planar routing on single-chip architectures leads to serialization of commuting gates and strain on classical decoding caused by large ancilla patches. A distributed multi-chip architecture utilizing the surface code can potentially solve these problems if one can optimize inter-chip gates, manage collisions in networking between chips, and minimize routing hardware costs. We propose QuIRC, a superconducting Quantum Interface Routing Card for Lattice Surgery between surface code modules inside of a single dilution refrigerator. QuIRC improves scaling by allowing connection of many modules, increases ancilla connectivity of surface code lattices, and offers improved transpilation of Pauli-based surface code circuits. QuIRC employs in-situ Entangled Pair (EP) generation protocols for communication. We explore potential topological layouts of QuIRC based on superconducting hardware fabrication constraints, and demonstrate reductions in ancilla patch size by up to 77.8%, and in layer transpilation size by 51.9% when compared to the single-chip case.
Quantum metrology explores quantum effects to improve the measurement accuracy of some physical quantities beyond the classical limit. However, due to the interaction between the system and the environment, the decoherence can significantly reduce the accuracy of the measurement. Many methods have been proposed to restore the accuracy of the measurement in the long-time limit. Recently, it has been found that the bound state can assist the error-free measurement and recover the $t^{-1}$ scaling [K. Bai, Z. Peng, H. G. Luo, and J. H. An, Phys. Rev. Lett. 123, 040402 (2019)]. Here, by using $N$-qubits, we propose a method to simulate the open quantum dynamics of the hybrid system including one atom and coupled resonators. We find that the error of the measurement can vanish as the time increases due to the existence of the bound state. By both analytical and numerical simulations, we prove the $t^{-1}$ scaling of the measurement error can be recovered when there is a bound state in the hybrid system. Interestingly, we observe that there are perfect oscillations which can be used for the evaluation of the atomic transition frequency. For a finite-$N$, the duration of the perfect oscillations doubles as one more qubit is involved.
To improve the phase sensitivity, multi-photon subtraction schemes within the SU(1,1) interferometer are proposed. The input states are the coherent state and the vacuum state, and the detection method is homodyne detection. The effects of multi-photon subtraction on phase sensitivity, quantum Fisher information, and quantum Cramer-Rao bound are analyzed under both ideal and photon losses situations. It is shown that the internal subtraction operation can improve the phase sensitivity, which becomes better performance by increasing subtraction number. It can also efficiently improve the robustness of the SU(1,1) interferometer against internal photon losses. By comparing separatively arbitrary photon subtraction on the two-mode inside SU(1,1) interferometer, the performance differences under different conditions are analyzed, including the asymmetric properties of non-Gaussian operations on the phase precision and the quantum Fisher information. Our proposed scheme represents a valuable method for achieving quantum precision measurements.
In this study, we introduce an innovative Quantum-enhanced Support Vector Machine (QSVM) approach for stellar classification, leveraging the power of quantum computing and GPU acceleration. Our QSVM algorithm significantly surpasses traditional methods such as K-Nearest Neighbors (KNN) and Logistic Regression (LR), particularly in handling complex binary and multi-class scenarios within the Harvard stellar classification system. The integration of quantum principles notably enhances classification accuracy, while GPU acceleration using the cuQuantum SDK ensures computational efficiency and scalability for large datasets in quantum simulators. This synergy not only accelerates the processing process but also improves the accuracy of classifying diverse stellar types, setting a new benchmark in astronomical data analysis. Our findings underscore the transformative potential of quantum machine learning in astronomical research, marking a significant leap forward in both precision and processing speed for stellar classification. This advancement has broader implications for astrophysical and related scientific fields
Haibo Hu, Yu Zhou, Ailun Yi, Tongyuan Bao, Chengying Liu, Qi Luo, Yao Zhang, Zi Wang, Zhengtong Liu, Shuming Xiao, Xin Ou, Qinghai Song The rise of the 4H-silicon-carbide-on-insulator (SiCOI) platform marks a promising pathway towards the realization of monolithic quantum photonic networks. However, the challenge of establishing room-temperature entangled registers on these integrated photonics platforms remains unresolved. Herein, we demonstrate the first entangled processor on the SiCOI platform. We show that both deterministic generation of single divacancy electron spins and near-unity spin initialization of a single $^{13}$C nuclear spin can be achieved on SiCOI at room temperature. Besides coherently manipulating the single nuclear spin, a maximally entangled state with a fidelity of 0.89 has been prepared on this CMOS-compatible semiconductor-integrated photonics system. This work establishes the foundation for compact and on-chip solutions within existing defect-based computing and sensing protocols, positioning the SiCOI platform as the most promising candidate for integrated monolithic quantum photonic networks.
Quantum Phase Estimation (QPE) stands as a pivotal quantum computing subroutine that necessitates an inverse Quantum Fourier Transform (QFT). However, it is imperative to recognize that enhancing the precision of the estimation inevitably results in a significantly deeper circuit. We developed a variational quantum circuit (VQC) approximation to reduce the depth of the QPE circuit, yielding enhanced performance in noisy simulations and real hardware. Our experiments demonstrated that the VQC outperformed both Noisy QPE and standard QPE on real hardware by reducing circuit noise. This VQC integration into quantum compilers as an intermediate step between input and transpiled circuits holds significant promise for quantum algorithms with deep circuits. Future research will explore its potential applicability across various quantum computing hardware architectures.
Francesco Zatelli, David van Driel, Di Xu, Guanzhong Wang, Chun-Xiao Liu, Alberto Bordin, Bart Roovers, Grzegorz P. Mazur, Nick van Loo, Jan Cornelis Wolff, A. Mert Bozkurt, Ghada Badawy, Sasa Gazibegovic, Erik P. A. M. Bakkers, Michael Wimmer, Leo P. Kouwenhoven, Tom Dvir The recent realization of a two-site Kitaev chain featuring "poor man's Majorana" states demonstrates a path forward in the field of topological superconductivity. Harnessing the potential of these states for quantum information processing, however, requires increasing their robustness to external perturbations. Here, we form a two-site Kitaev chain using proximitized quantum dots hosting Yu-Shiba-Rusinov states. The strong hybridization between such states and the superconductor enables the creation of poor man's Majorana states with a gap larger than $70 \mathrm{~\mu eV}$. It also greatly reduces the charge dispersion compared to Kitaev chains made with non-proximitized quantum dots. The large gap and reduced sensitivity to charge fluctuations will benefit qubit manipulation and demonstration of non-abelian physics using poor man's Majorana states.
The saturation of a recently proposed universal bound on the Lyapunov exponent has been conjectured to signal the existence of a gravity dual. This saturation occurs in the low temperature limit of the dense Sachdev-Ye-Kitaev (SYK) model, $N$ Majorana fermions with $q$-body ($q>2$) infinite-range interactions. We calculate certain Out of Time Order Correlators (OTOC) for $N\le 64$ fermions for a highly sparse SYK model and find no significant dependence of the Lyapunov exponent on sparsity up to near the percolation limit where the Hamiltonian breaks up into blocks. This suggests that in the sparse case, the Lyapunov exponent also saturates the low-temperature bound. A key ingredient to reaching $N = 64$ is the development of a novel quantum spin model simulation library that implements highly-optimized matrix-free Krylov subspace methods on Graphical Processing Units (GPUs). This leads to a significantly lower simulation time as well as vastly reduced memory usage over previous approaches, while using modest computational resources. Strong sparsity-driven statistical fluctuations require both the use of a vastly larger number of disorder realizations with respect to the dense limit and a careful finite size scaling analysis. Our results potentially broadens the landscape of theories that may have a gravity analogue.
In this paper, we propose a novel quantum classifier utilizing dissipative engineering. Unlike standard quantum circuit models, the classifier consists of a central spin-qubit model. By subjecting the auxiliary qubits to carefully tailored strong dissipations, we establish a one-to-one mapping between classical data and dissipative modes. This mapping enables the encoding of classical data within a decoherence-free subspace, where the central qubit undergoes evolution. The dynamics of the central qubit are governed by an effective Lindblad master equation, resulting in relaxation towards a steady state. We first demonstrate the capability of our model to prepare arbitrary single-qubit states by training the inter-coupling of the system and the external dissipations. By elucidating the underlying classification rule, we subsequently derive a quantum classifier. Leveraging a training set with labeled data, we train the dissipative central spin-qubit system to perform specific classification tasks akin to classical neural networks. Our study illuminates the untapped potential of the dissipative system for efficient and effective classification tasks in the realm of quantum machine learning.
Near-term quantum computing technologies grapple with huge complexity overheads, hindering their ability to induce algorithms, necessitating engineering and scientific innovations. One class of problems of interest is Quantum Simulation, whereby quantum systems are simulated using a quantum computer. However, current devices are yet to surpass classical tensor network techniques. For problems of interest, where classical simulation techniques fail, large degrees of entanglement are required. Another challenge of implementing quantum simulation problems is that qubits sit idle whilst alternating simulation terms are implemented, exposing the system to decoherence. In the near term, 2D planar superconducting lattices of circuit-QED elements such as the transmon continue to draw substantial attention, but they are hindered by their nearest neighbor topology and relatively short lifespan, two problems that are problematic for quantum simulation. One technology of particular interest is the multi-mode superconducting resonator capable of storing multiple qubits in one device. We observe that these cavities have a natural virtual topology that aligns particularly well with quantum simulation problems, and exhibit much longer lifespans in comparison to other planar superconducting hardware. In this paper we present MUCIC, we discuss the simple integration of these devices into the current landscape and their implications to quantum simulation, motivated by their alignment to the quantum simulation problem, and potential as a quantum memory candidate. We report the development of MUCICs transpiler, leading to reductions of up to 82% in quantum simulation circuit depths. Additionally, our investigation demonstrates improvements of up to 19.4% in converged results from Variational Quantum Algorithms.
Memory is an indispensable component in classical computing systems. While the development of quantum computing is still in its early stages, current quantum processing units mainly function as quantum registers. Consequently, the actual role of quantum memory in future advanced quantum computing architectures remains unclear. With the rapid scaling of qubits, it is opportune to explore the potential and feasibility of quantum memory across different substrate device technologies and application scenarios. In this paper, we provide a full design stack view of quantum memory. We start from the elementary component of a quantum memory device, quantum memory cells. We provide an abstraction to a quantum memory cell and define metrics to measure the performance of physical platforms. Combined with addressing functionality, we then review two types of quantum memory devices: random access quantum memory (RAQM) and quantum random access memory (QRAM). Building on top of these devices, quantum memory units in the computing architecture, including building a quantum memory unit, quantum cache, quantum buffer, and using QRAM for the quantum input-output module, are discussed. We further propose the programming model for the quantum memory units and discuss their possible applications. By presenting this work, we aim to attract more researchers from both the Quantum Information Science (QIS) and classical memory communities to enter this emerging and exciting area.
We propose an extended compass model that hosts subsystem symmetries and has potential experimental relevance with 3d transition metal compounds. The subsystem symmetries strongly constrain the mobility of spin excitations and lead to profound consequences. At the quantum critical point we find the presence of "critical Bose surface" along the entire $k_x$ and $k_y$ axis. Across which we find a nodal-line spin liquid that undergoes nematic instability at low temperatures. In the ferro-quadrupole phase, we find that one excitation is immobile individually analogous to "fractons".
This paper presents a nonperturbative method for solving eigenproblems. This method applies to almost all potentials and provides nonperturbative approximations for any energy level. The method converts an eigenproblem into a perturbation problem, obtains perturbation solutions through standard perturbation theory, and then analytically continues the perturbative solution into a nonperturbative solution. Concretely, we follow three main steps: (1) Introduce an auxiliary potential that can be solved exactly and treat the potential to be solved as a perturbation on this auxiliary system. (2) Use perturbation theory to obtain an approximate polynomial of the eigenproblem. (3) Use a rational approximation to analytically continue this approximate polynomial into the nonperturbative region.
Photonic parity projection plays a significant role in photonic quantum information processing. Non-destructive parity projections normally require high-fidelity Controlled-Z gates between photonic and matter qubits, which can be experimentally demanding. In this paper, we propose a nearly deterministic parity projection protocol on two photonic qubits which only requires stable matter-photon Controlled-Phase gates. The fact that our protocol does not require perfect Controlled-Z gates makes it more amenable to experimental implementation.
Fei Hua, Meng Wang, Gushu Li, Bo Peng, Chenxu Liu, Muqing Zheng, Samuel Stein, Yufei Ding, Eddy Z. Zhang, Travis S. Humble, Ang Li The success of a quantum algorithm hinges on the ability to orchestrate a successful application induction. Detrimental overheads in mapping general quantum circuits to physically implementable routines can be the deciding factor between a successful and erroneous circuit induction. In QASMTrans, we focus on the problem of rapid circuit transpilation. Transpilation plays a crucial role in converting high-level, machine-agnostic circuits into machine-specific circuits constrained by physical topology and supported gate sets. The efficiency of transpilation continues to be a substantial bottleneck, especially when dealing with larger circuits requiring high degrees of inter-qubit interaction. QASMTrans is a high-performance C++ quantum transpiler framework that demonstrates up to 369X speedups compared to the commonly used Qiskit transpiler. We observe speedups on large dense circuits such as uccsd_n24 and qft_n320 which require O(10^6) gates. QASMTrans successfully transpiles the aforementioned circuits in 69s and 31s, whilst Qiskit exceeded an hour of transpilation time. With QASMTrans providing transpiled circuits in a fraction of the time of prior transpilers, potential design space exploration, and heuristic-based transpiler design becomes substantially more tractable. QASMTrans is released at http://github.com/pnnl/qasmtrans.
We study the closed expressions of the convex roof coherence measures for one-qubit states in this paper. We present the analytical expressions for the convex roof coherence measures, $C_f(\rho)$, of one-qubit states with $C_f(\varphi):=f(\abs{c_0}^2,\abs{c_1}^2)$ (where $\ket{\varphi}=c_0\ket{0}+c_1\ket{1}$) being convex with respect to the $l_1$ norm of coherence of $\varphi$ (i.e., $C_{l_1}(\varphi)$), such coherence measures including the coherence of formation, the geometric measure of coherence, the coherence concurrence, and the coherence rank. We further present the operational interpretations of these measures. Finally, we present the usefulness of the convex roof coherence measures $C_f(\varphi)$ being non-convex with respect to $C_{l_1}(\varphi)$ by giving the necessary and sufficient conditions for the transformations $p\varphi_1\oplus(1-p)\varphi_2\to q\phi_1\oplus(1-q)\phi_2$ via incoherent operations, where $\varphi_i$, $\phi_j$ $(i, j=1, 2)$ are one-qubit pure states and $0\leq p, q\leq 1$.
Herein, we propose an experimentally feasible scheme to show the quantum phase transition of the Jaynes-Cummings (JC) model by modulating the transition frequency of a two-level system in a quantum Rabi model with strong coupling. By tuning the modulation frequency and amplitude, the ratio of the effective coupling strength of the rotating terms to the effective cavity (atomic transition) frequency can enter the deep-strong coupling regime, while the counter-rotating terms can be neglected. Thus, a deep-strong JC model is obtained. The ratio of the coupling strength to resonance frequencies in the deep-strong JC model is two orders of magnitude larger than the corresponding ratio in the original quantum Rabi model. Our scheme can be employed in atom-cavity resonance and off-resonance cases, and it is valid over a broad range. The nonzero average cavity photons of the ground state indicate the emergence of a quantum phase transition. Further, we demonstrate the dependence of the phase diagram on the atom-cavity detuning and modulation parameters. All the parameters used in our scheme are within the reach of current experimental technology. Our scheme provides a new mechanism for investigating the critical phenomena of finite-sized systems without requiring classical field limits, thereby opening a door for studying fundamental quantum phenomena occurring in the ultrastrong and even deep-strong coupling regimes.
Recent discoveries have highlighted the significance of replica wormholes in resolving the information paradox and establishing the unitarity of black hole evaporation. In this letter, we propose the dissipative Sachdev-Ye-Kitaev model (SYK) as a minimal quantum model that exhibits entanglement dynamics with features qualitatively similar to replica wormholes. As a demonstration, we investigate the entanglement growth of a pair of dissipative SYK models initialized in a thermofield double state (TFD). In the regime of large $N$ with weak dissipation, we observe a first-order entanglement transition characterized by a switch of the dominant saddle point: from replica diagonal solutions for short times to replica wormhole-like off-diagonal solutions for long times. Furthermore, we show that signature of replica wormholes persists even at moderate $N \lesssim 30$ by using the Monte Carlo quantum trajectory method. Our work paves the way for explorations of replica wormhole physics in quantum simulators.
Mingkang Xia, Chao Zhou, Chenxu Liu, Param Patel, Xi Cao, Pinlei Lu, Boris Mesits, Maria Mucci, David Gorski, David Pekker, Michael Hatridge Increasing the fidelity of single-qubit gates requires a combination of faster pulses and increased qubit coherence. However, with resonant qubit drive via a capacitively coupled port, these two objectives are mutually contradictory, as higher qubit quality factor requires a weaker coupling, necessitating longer pulses for the same applied power. Increasing drive power, on the other hand, can heat the qubit's environment and degrade coherence. In this work, by using the inherent non-linearity of the transmon qubit, we circumvent this issue by introducing a new parametric driving scheme to perform single-qubit control. Specifically, we achieve rapid gate speed by pumping the transmon's native Kerr term at approximately one third of the qubit's resonant frequency. Given that transmons typically operate within a fairly narrow range of anharmonicity, this technique is applicable to all transmons. In both theory and experiment, we show that the Rabi rate of the process is proportional to applied drive amplitude cubed, allowing for rapid gate speed with only modest increases in applied power. In addition, we demonstrate that filtering can be used to protect the qubit's coherence while performing rapid gates, and present theoretical calculations indicating that decay due to multi-photon losses, even in very strongly coupled drive lines, will not limit qubit lifetime. We demonstrate $\pi/2$ pulses as short as tens of nanoseconds with fidelity as high as 99.7\%, limited by the modest coherence of our transmon. We also present calculations indicating that this technique could reduce cryostat heating for fast gates, a vital requirement for large-scale quantum computers.
We study the task of coherence filtration under strictly incoherent operations in this paper. The aim of this task is to transform a given state $\rho$ into another one $\rho^\prime$ whose fidelity with the maximally coherent state is maximal by using stochastic strictly incoherent operations. We find that the maximal fidelity between $\rho^\prime$ and the maximally coherent state is given by a multiple of the $\Delta$ robustness of coherence $R(\rho\|\Delta\rho):=\min\{\uplambda|\rho\leq\uplambda\Delta\rho\}$, which provides $R(\rho\|\Delta\rho)$ an operational interpretation. Finally, we provide a coherence measure based on the task of coherence filtration.
Quantum Local Search (QLS) is a promising approach that employs small-scale quantum computers to tackle large combinatorial optimization problems through local search on quantum hardware, starting from an initial point. However, the random selection of the sub-problem to solve in QLS may not be efficient. In this study, we propose a reinforcement learning (RL) based approach to train an agent for improved subproblem selection in QLS, beyond random selection. Our results demonstrate that the RL agent effectively enhances the average approximation ratio of QLS on fully-connected random Ising problems, indicating the potential of combining RL techniques with Noisy Intermediate-scale Quantum (NISQ) algorithms. This research opens a promising direction for integrating RL into quantum computing to enhance the performance of optimization tasks.
Grover search is a renowned quantum search algorithm that leverages quantum superposition to find a marked item with quadratic speedup. However, when implemented on Noisy Intermediate-scale Quantum (NISQ) hardware, the required repeated iterations of the oracle and diffusion operators increase exponentially with the number of qubits, resulting in significant noise accumulation. To address this, we propose a hybrid quantum-classical architecture that replaces quantum iterations with updates from a classical optimizer. This optimizer minimizes the expectation value of an oracle Hamiltonian with respect to a parameterized quantum state representing the target bit string. Our parameterized quantum circuit is much shallower than Grover search circuit, and we found that it outperforms Grover search on noisy simulators and NISQ hardware. When the number of qubits is greater than 5, our approach still maintains usable success probability, while the success probability of Grover search is at the same level as random guessing.
We study the Markovian and Non-Markovian dynamics in a giant atom system which couples to a coupled resonator waveguide (CRW) via two distant sites. Under certain conditions, we find that the giant atom population can exhibit an oscillating behavior and the photon can be trapped in the giant atom regime. These phenomena are induced by the interference effect among the bound states both in and outside the continuum. As an application of the photon trapping, we theoretically propose a magic cavity model where the giant atom serve as either a perfect or leaky cavity, depending on the distance between the coupling sites. The controllability of the magic cavity from perfect to leaky one can not be realized in the traditional cavity or circuit QED setup. The predicted effects can be probed in state-of-the-art waveguide QED experiments and provide a striking example of how the different kinds of bound states modify the dynamics of quantum open system in a structured environment.
The non-Hermitian skin effect under open boundary conditions is widely believed to originate from the intrinsic spectral topology under periodic boundary conditions. If the eigenspectra under periodic boundary conditions have no spectral windings (e.g., piecewise arcs) or a finite area on the complex plane, there will be no non-Hermitian skin effect with open boundaries. In this article, we demonstrate another scenario beyond this perception by introducing a two-dimensional periodically driven model. The effective Floquet Hamiltonian lacks intrinsic spectral topology and is proportional to the identity matrix (representing a single point on the complex plane) under periodic boundary conditions. Yet, the Floquet Hamiltonian exhibits a second-order skin effect that is robust against perturbations and disorder under open boundary conditions. We further reveal the dynamical origin of these second-order skin modes and illustrate that they are characterized by a dynamical topological invariant of the full time-evolution operator.
Chuan-Hong Liu, Andrew Ballard, David Olaya, Daniel R. Schmidt, John Biesecker, Tammy Lucas, Joel Ullom, Shravan Patel, Owen Rafferty, Alexander Opremcak, Kenneth Dodge, Vito Iaia, Tianna McBroom, Jonathan L. Dubois, Pete F. Hopkins, Samuel P. Benz, Britton L. T. Plourde, Robert McDermott The single flux quantum (SFQ) digital superconducting logic family has been proposed for the scalable control of next-generation superconducting qubit arrays. In the initial implementation, SFQ-based gate fidelity was limited by quasiparticle (QP) poisoning induced by the dissipative on-chip SFQ driver circuit. In this work, we introduce a multi-chip module architecture to suppress phonon-mediated QP poisoning. Here, the SFQ elements and qubits are fabricated on separate chips that are joined with In bump bonds. We use interleaved randomized benchmarking to characterize the fidelity of SFQ-based gates, and we demonstrate an error per Clifford gate of 1.2(1)%, an order-of-magnitude reduction over the gate error achieved in the initial realization of SFQ-based qubit control. We use purity benchmarking to quantify the contribution of incoherent error at 0.96(2)%; we attribute this error to photon-mediated QP poisoning mediated by the resonant mm-wave antenna modes of the qubit and SFQ-qubit coupler. We anticipate that a straightforward redesign of the SFQ driver circuit to limit the bandwidth of the SFQ pulses will eliminate this source of infidelity, allowing SFQ-based gates with fidelity approaching theoretical limits, namely 99.9% for resonant sequences and 99.99% for more complex pulse sequences involving variable pulse-to-pulse separation.
We study the localization and topological transitions of the generalized non-Hermitian SSH models, where the non-Hermiticities are introduced by the complex quasiperiodic hopping and the nonreciprocal hopping. We elucidate the universality of the models and how many models can be mapped to them. Under the open boundary condition, two delocalization transitions are found due to the competition between the Anderson localization and the boundary localization from the nontrivial edge states and the non-Hermitian skin effect. Under the periodic boundary condition, only one delocalization transition is found due to the disappearance of the non-Hermitian skin effect. The winding numbers of energy and the Lyapunov exponents in analytical form are obtained to exactly characterize the two deloaclizateon transitions. It finds that the delocalization transitions don't accompany the topological transition. Furthermore, the large on-site non-Hermiticity and the large nonreciprocal hopping are all detrimental to the topological transitions. However, the large nonreciprocal hopping enhances the Anderson localizations. The above analyses are verified by calculating the energy gap and the inverse of the participation ratio numerically.
Simulating $\mathrm{U(1)}$ quantum gauge theories with spatial dimension greater than one is of great physical significance yet has not been achieved experimentally. Here we propose a simple realization of $\mathrm{U(1)}$ gauge theory on triangular lattice Rydberg atom arrays. Within experimentally accessible range, we find that the effective model well simulates various aspects of the $\mathrm{U(1)}$ gauge theory, such as emergence of topological sectors, incommensurability, and the deconfined Rokhsar-Kivelson point. Our proposal is easy to implement experimentally and exhibits pronounced quantum dynamics compared with previous proposals realizing $\mathrm{U(1)}$ and $\mathbb Z_2$ gauge theories.
James Ang, Gabriella Carini, Yanzhu Chen, Isaac Chuang, Michael Austin DeMarco, Sophia E. Economou, Alec Eickbusch, Andrei Faraon, Kai-Mei Fu, Steven M. Girvin, Michael Hatridge, Andrew Houck, Paul Hilaire, Kevin Krsulich, Ang Li, Chenxu Liu, Yuan Liu, Margaret Martonosi, David C. McKay, James Misewich, et al (13) Many proposals to scale quantum technology rely on modular or distributed designs where individual quantum processors, called nodes, are linked together to form one large multinode quantum computer (MNQC). One scalable method to construct an MNQC is using superconducting quantum systems with optical interconnects. However, a limiting factor of these machines will be internode gates, which may be two to three orders of magnitude noisier and slower than local operations. Surmounting the limitations of internode gates will require a range of techniques, including improvements in entanglement generation, the use of entanglement distillation, and optimized software and compilers, and it remains unclear how improvements to these components interact to affect overall system performance, what performance from each is required, or even how to quantify the performance of each. In this paper, we employ a `co-design' inspired approach to quantify overall MNQC performance in terms of hardware models of internode links, entanglement distillation, and local architecture. In the case of superconducting MNQCs with microwave-to-optical links, we uncover a tradeoff between entanglement generation and distillation that threatens to degrade performance. We show how to navigate this tradeoff, lay out how compilers should optimize between local and internode gates, and discuss when noisy quantum links have an advantage over purely classical links. Using these results, we introduce a roadmap for the realization of early MNQCs which illustrates potential improvements to the hardware and software of MNQCs and outlines criteria for evaluating the landscape, from progress in entanglement generation and quantum memory to dedicated algorithms such as distributed quantum phase estimation. While we focus on superconducting devices with optical interconnects, our approach is general across MNQC implementations.
Braneshop, and
the US National Science Foundation.
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