In the current NISQ era, the performance of QNN models is strictly hindered by the limited qubit number and inevitable noise. A natural idea to improve the robustness of QNN is the implementation of a distributed system. Nevertheless, due to the heterogeneity and instability of quantum chips (e.g., noise, frequent online/offline), training and inference on distributed quantum devices may even destroy the accuracy. In this paper, we propose HeteroQNN, a comprehensive QNN framework designed for efficient and high-accuracy distributed training and inference. The main innovation of HeteroQNN is it decouples the QNN circuit into two uniform representations: model vector and behavioral vector. The model vector specifies the gate parameters in the QNN model, while the behavioral vector captures the hardware features when implementing the QNN circuit. To handle the architectural heterogeneity, we introduce personalized QNN models in each QPU and share the gradient among QPUs with homogeneous behavioral vectors. We propose shot-oriented distributed inference, which is much more fine-grained scheduling that can improve accuracy and balance the workload. Finally, by leveraging the hidden homogeneity in the model vector, we present the maintenance for QPU variability. The experiments show that accelerates the training process by 4.03× with 7.87% loss reduction, compared with the previous distributed QNN framework.

The emergence of quantum computers heralds a new frontier in computational power, empowering quantum algorithms to address challenges that defy classical computation. However, the design of quantum algorithms is challenging as it largely requires the manual efforts of quantum experts to transit mathematical expressions to quantum circuit diagrams. To ease this process, particularly for prototyping, educational, and modular design workflows, we propose to bridge the textual and visual contexts between mathematics and quantum circuits through visual linking and transitions. We contribute a design space for quantum algorithm design, focusing on the textual and visual elements, interactions, and design patterns throughout the quantum algorithm design process. Informed by the design space, we introduce QuRAFT, a visual interface that facilitates a seamless transition from abstract mathematical expressions to concrete quantum circuits. QuRAFT incorporates a suite of eight integrated visual and interaction designs tailored to support users in the formulation, implementation, and validation process of the quantum algorithm design. Through two detailed case studies and a user evaluation, this paper demonstrates the effectiveness of QuRAFT. Feedback from quantum computing experts highlights the practical utility of QuRAFT in algorithm design and provides valuable implications for future advancements in visualization and interaction design within the quantum computing domain.

In the current NISQ era, the performance of QNN models is strictly hindered by the limited qubit number and inevitable noise. A natural idea to improve the robustness of QNN is to involve multiple quantum devices. Nevertheless, due to the heterogeneity and instability of quantum devices (e.g., noise, frequent online/offline), training and inference on distributed quantum devices may even destroy the accuracy. In this paper, we propose ArbiterQ, a comprehensive QNN framework designed for efficient and high-accuracy training and inference on heterogeneous QPUs. The main innovation of ArbiterQ is it applies personalized models for each QPU via two uniform QNN representations: model vector and behavioral vector. The model vector specifies the logical-level parameters in the QNN model, while the behavioral vector captures the hardware-level features when implementing the QNN circuit. In this manner, by sharing the gradient among QPUs with similar behavioral vectors, we can effectively leverage parallelism while considering heterogeneity. We also propose shot-oriented inference scheduling, which is a much more fine-grained scheduling that can improve accuracy and balance the workload. The experiments show that ArbiterQ accelerates the training process by $4.03 \times$ with $7.87 \%$ loss reduction, compared with the previous distributed QNN framework EQC [1].

Quantum computing offers the potential for exponential speedups over classical computing in tackling complex tasks, such as large-number factorization and chemical molecular simulation. However, quantum noise remains a significant challenge, hindering the reliability and scalability of quantum systems. Therefore, effective characterization and calibration of quantum noise are critical to advancing these systems. Quantum calibration is a process that heavily relies on expert knowledge, and there currently is a range of research focused on automatic calibration. However, traditional calibration methods often need an effective error traceback mechanism, leading to repeated calibration attempts without identifying root causes. To address the issue of error traceback in calibration failures, this paper proposes an automatic calibration error traceback algorithm facilitated by a Mixture of Experts (MoE) system inspired by the current large language model technologies. Our approach enables traceability of quantum calibration errors, allowing for the rapid identification and correction of deviations from the calibration state. Extensive experimental results demonstrate that the MoE-based automatic calibration method significantly outperforms traditional error traceability and calibration efficiency techniques. Notably, our approach improved the average visibility of 77 qubits by 25.5%, surpassing the outcomes of fixed calibration processes. This work presents a promising path toward more reliable and scalable quantum computing systems.

Constrained binary optimization aims to find an optimal assignment to minimize or maximize the objective meanwhile satisfying the constraints, which is a representative NP problem in various domains, including transportation, scheduling, and economy. Quantum approximate optimization algorithms (QAOA) provide a promising methodology for solving this problem by exploiting the parallelism of quantum entanglement. However, existing QAOA approaches based on penalty-term or Hamiltonian simulation fail to thoroughly encode the constraints, leading to extremely low success rate and long searching latency.This paper proposes Choco-Q, a formal and universal framework for constrained binary optimization problems, which comprehensively covers all constraints and exhibits high deployability for current quantum devices. The main innovation of Choco-Q is to embed the commute Hamiltonian as the driver Hamiltonian, resulting in a much more general encoding formulation that can deal with arbitrary linear constraints. Leveraging the arithmetic features of commute Hamiltonian, we propose three optimization techniques to squeeze the overall circuit complexity, including Hamiltonian serialization, equivalent decomposition, and variable elimination. The serialization mechanism transforms the original Hamiltonian into smaller ones. Our decomposition methods only take linear time complexity, achieving end-to-end acceleration. Experiments demonstrate that Choco-Q shows more than 235× algorithmic improvement in successfully finding the optimal solution, and achieves 4.69 × end-to-end acceleration, compared to prior QAOA designs.

Quantum feedback makes the execution of dynamic quantum circuits possible and is widely used in quantum algorithms.However, due to the inherent computation and transmission cost, the latency of the quantum feedback becomes a considerable burden on the current quantum algorithm.The dynamic property of the feedback also makes the gates blocked until the feedback is finished.In this paper, we propose ARTERY, which uses branch prediction to support instruction pre-execution and speed up the feedback.ARTERY integrates historical statistics of branches and a real-time readout pulse analysis to predict the branch.With this idea, we build up a reconciled branch predictor that concatenates the historical statistics of branches and a real-time branch circuit speculation obtained from the readout-pulse trajectory predictor.We further explore the implementation of peripheral hardware for feedback, including a scalable inter-FPGA connection via the backplane, a feedback trigger mechanism for dynamic instruction timing, and an adaptive pulse sampling technique to maximize the hardware bandwidth.ARTERY accelerates quantum feedback process by 2.07× compared to the state-of-the-art method, with over 90% prediction accuracy, achieving 1.24× fidelity improvement.

Unikernel is a promising runtime for serverless computing with its lightweight and isolated architecture. It offers a secure and efficient environment for applications. However, famous serverless frameworks like Knative have introduced heavyweight component sidecars to assist function instance deployment in a non-intrusive manner. But the sidecar not only hinders the throughput of unikernel function services but also consumes excessive memory resources. Moreover, the intricate network communication pathways among various services pose significant challenges for deploying unikernels in production serverless environments. Although shared-memory based communication on the same server can solve the communication bottleneck of unikernel-based function instances. The situation where malicious programs on the server make the shared memory untrustworthy limits the deployment of such technologies.We propose UKFaaS, a lightweight and high-performance serverless framework. UKFaaS leverages the advantages of customized operating systems through unikernel and it non-intrusively integrates sidecar functionality into the unikernel, avoiding the overhead of sidecar request forwarding. Additionally, UKFaaS innovatively implements data communication between unikernels in the same server to eliminate VM-Exit bottlenecks in RPC (remote process call) based on VMFUNC without relying on memory sharing. The preliminary experimental results indicate that UKFaaS can realize 1.8×-3.5× request throughput per second (RPS) compared with the advanced serverless system FaasFlow, UaaF and Nightcore in the Google online boutique microservice benchmark.

We present AdaptDQC, an adaptive compiler framework for optimizing distributed quantum computing (DQC) under diverse performance metrics and inter-chip communication (ICC) architectures. AdaptDQC leverages a novel spatial-temporal graph model to describe quantum circuits, model ICC architectures, and quantify critical performance metrics in DQC systems, yielding a systematic and adaptive approach to constructing circuit-partitioning and chip-mapping strategies that admit hybrid ICC architectures and are optimized against various objectives. Experimental results on a collection of benchmarks show that AdaptDQC outperforms state-of-the-art compiler frameworks: It reduces, on average, the communication cost by up to 35.4% and the latency by up to 38.4%.

Quantum neural networks (QNNs) hold immense potential for complex tasks by harnessing quantum entanglement and superposition, such as physics simulation, artificial intelligence, and cryptography. However, the presence of quantum noise, stemming from hardware imperfections and environmental interactions, significantly reduces their practical performance. Moreover, the noise varies from different devices and shifts over time, necessitating continuous retraining models to chase and cater to the evolving noise, leading to high-computation costs. In this article, we presentQuST, a novel QNN robust training framework designed to handle the noise in a once-and-for-all manner, which can tackle both spatial and temporal biases to maintain the QNN model accuracy under ever-changing noise conditions. Our approach consists of three key components. First, we propose a metric called circuit sequence correctness (CSC) to characterize QNN circuit reliability in noisy environments. Then, we model CSC as a training weight to incorporate loss integration and utilize KL divergence to align noise inference with noise-free inference, thereby improving anti-noise capabilities. Furthermore, we introduce multiscale noise-aware training to enhance the model’s noise tolerance at various noise magnitudes. We conduct experiments on MNIST and fashion-MNIST datasets, along with 190-day historical noise simulations and one case study on 7 real IBMQ quantum computers. The results demonstrate 8.1%–15.1% and 9.1%–11.45% accuracy improvements in temporal and spatial dimensions, respectively. Additionally, we conduct ablation experiments to validate the effectiveness of theQuST’s key components. The results demonstrate thatQuSTconsistently sustains high accuracy without retraining,even under changing noise conditions, and exhibits minimal loss of accuracy as noise levels increase.

Quantum pulse serves as the machine language of superconducting quantum devices, which needs to be synthesized and calibrated for precise control of quantum operations. However, existing pulse control systems suffer from the dilemma between long synthesis latency and inaccuracy of quantum control systems. compute-in-CPU synthesis frameworks, like IBM Qiskit Pulse, involve massive redundant computation during pulse calculation, suffering from a high computational cost when handling large-scale circuits. On the other hand, field-programmable gate array (FPGA)-based synthesis frameworks, like QuMA, faces inaccurate pulse control problem. In this article, we propose both compute-in-CPU and all-in-FPGA solutions to collaboratively solve the latency and inaccuracy problem. First, we propose QPulseLib, a novel compute-in-CPU library with reusable pulses that can directly provide the pulse of a circuit pattern. To establish this library, we transform the circuit and apply convolutional operators to extract reusable patterns and precalculate their resultant pulses. Then, we develop a matching algorithm to identify such patterns shared by the target circuit. Experiments show that QPulseLib achieves$158.46\times $and$16.03\times $speedup for pulse calculation, compared to Qiskit Pulse and AccQOC. Moreover, we extend the design as a fast and precise all-in-FPGA pulse control approach using near-quantum cache design, SmartQCache. To be specific, we employ a two-level cache to hold reusable pulses of frequently-used circuit patterns. Such a design enables pulse prefetching in near-quantum peripherals, dramatically reducing the end-to-end synthesis latency. To achieve precise pulse control, SmartQCache incorporates duration optimization and pulse sequence calibration to mitigate the execution errors from imperfect hardware, crosstalk, and time shift. Experimental results demonstrate that SmartQCache achieves$294.37\times $and$145.43\times $speedup in pulse synthesis compared to Qiskit Pulse and AccQOC. It also reduces the pulse inaccuracy by$1.27\times $compared to QuMA.

Quantum readout noise turns out to be the most significant source of error, which greatly affects the measurement fidelity. Matrix-based calibration has been demonstrated to be effective in various quantum platforms. However, existing methodologies are fundamentally limited in either scalability or accuracy. Inspired by the classical finite element method (FEM), a formal method to model the complex interaction between elements, we present our calibration framework named QuFEM. First, we apply a divide-and-conquer strategy that formulates the calibration as a series of tensor products with noise matrices. This matrices are iteratively characterized together with the calibrated probability distribution, aiming to capture the inherent locality of qubit interactions. Then, to accelerate the end-to-end calibration, we propose a sparse tensor-product engine to exploit the sparsity in the intermediate values. Our experiments show that QuFEM achieves 2.5×103× speedup in the 136-qubit calibration compared to the state-of-the-art matrix-based calibration technique [50], and provides 1.2× and 1.4× fidelity improvement on the 18-qubit and 36-qubit real-world quantum devices.

Unlike classical computing, quantum program verification (QPV) is much more challenging due to the non-duplicability of quantum states that collapse after measurement. Prior approaches rely on deductive verification that shows poor scalability. Or they require exhaustive assertions that cannot ensure the program is correct for all inputs. In this paper, we propose MorphQPV, a confident assertion-based verification methodology. Our key insight is to leverage the isomorphism in quantum programs, which implies a structure-preserve relation between the program runtime states. In the assertion statement, we define a tracepoint pragma to label the verified quantum state and an assume-guarantee primitive to specify the expected relation between states. Then, we characterize the ground-truth relation between states using an isomorphism-based approximation, which can effectively obtain the program states under various inputs while avoiding repeated executions. Finally, the verification is formulated as a constraint optimization problem with a confidence estimation model to enable rigorous analysis. Experiments suggest that MorphQPV reduces the number of program executions by 107.9× when verifying the 27-qubit quantum lock algorithm and improves the probability of success by 3.3×-9.9× when debugging five benchmarks.

The current Noisy Intermediate-Scale Quantum (NISQ) era suffers from high quantum readout error that severely reduces the measurement fidelity. Matrix-based error mitigation has been demonstrated as a promising software-level technique, which performs matrix-vector multiplication to calibrate the probability distribution with noise. However, this approach shows poor scalability and limited fidelity improvement as the matrix size exponentially increases with the number of qubits. In this paper, we propose SpREM to exploit the inherent sparsity in the mitigation matrix. Inspired by the interaction mechanism between qubits, we identify structured sparsity patterns using Hamming distance. With this insight, we propose the Hamming-Distance Sparse Row (HDSR) compression method and its format, which can achieve higher sparsity than threshold-based pruning meanwhile exhibiting great fidelity improvement. Finally, we propose the computational dataflow of the HDSR format and implement it on hardware. Experiments demonstrate that SpREM achieves 98.9% sparsity and a 27.3× reduction in fidelity loss on the real-world quantum device, compared to threshold-based pruning. It achieves an average 11.2× ~ 36.4× speedup compared to Xilinx Vitis SPARSE library and NVIDIA A100 GPU implementations.

Recent years have witnessed remarkable advances in graph representation learning using Graph Neural Networks (GNNs). To fully exploit the unlabeled graphs, researchers pre-train GNNs on large-scale graph databases and then fine-tune these pre-trained G raph M odels (GMs) for better performance in downstream tasks. Because different GMs are developed with diverse pre-training tasks or datasets, they can be complementary to each other for a more complete knowledge base. Naturally, a compelling question is emerging: How can we exploit the diverse knowledge captured by different GMs simultaneously in downstream tasks? In this paper, we make one of the first attempts to exploit multiple GMs to advance the performance in the downstream tasks. More specifically, for homogeneous GMs that share the same model architecture but are obtained with different pre-training tasks or datasets, we align each layer of these GMs and then aggregate them adaptively on a per-sample basis with a tailored Recurrent Aggregation Policy Network (RAPNet). For heterogeneous GMs with different model architectures, we design an alignment module to align the output of diverse GMs and a meta-learner to decide the importance of each GM conditioned on each sample automatically before aggregating the GMs. Extensive experiments in various downstream tasks from 3 domains reveal our dominance over each single GM. Additionally, our methods (UniGM) can achieve better performance with moderate computational overhead compared to alternative approaches including ensemble and model fusion. Also, we verify that our methods are not limited to graph data but could be flexibly applied to multiple modalities. The codes are available at https://github.com/monica309673/UniGM.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

Propositional satisfiability problem (SAT) is represented in a conjunctive normal form with multiple clauses, which is an important non-deterministic polynomial-time (NP) complete problem that plays a major role in various applications including artificial intelligence, graph colouring, and circuit analysis. Quantum annealing (QA) is a promising methodology for solving complex SAT problems by exploiting the parallelism of quantum entanglement, where the SAT variables are embedded to the qubits. However, the long embedding time fundamentally limits existing QA-based methods, leading to inefficient hardware implementation and poor scalability.In this paper, we propose HyQSAT, a hybrid approach that integrates QA with the classical Conflict-Driven Clause Learning (CDCL) algorithm to enable end-to-end acceleration for solving SAT problems. Instead of embedding all clauses to QA hardware, we quantitatively estimate the conflict frequency of clauses and apply breadth-first traversal to choose their embedding order. We also consider the hardware topology to maximize the utilization of physical qubits in embedding to QA hardware. Besides, we adjust the embedding coefficients to improve the computation accuracy under qubit noise. Finally, we present how to interpret the satisfaction probability based on QA energy distribution and use this information to guide the CDCL search. Our experiments demonstrate that HyQSAT can effectively support larger-scale SAT problems that are beyond the capability of existing QA approaches, achieve up to 12.62X end-to-end speedup using D-Wave 2000Q compared to the classic CDCL algorithm on Intel E5 CPU, and considerably reduce the QA embedding time from 17.2s to 15.7µs compared to the D-Wave Minorminer algorithm [11].

Quantum circuit serves as a popular programming model that describes the computation using a set of quantum gates, which requires generating a sequence of pulses that collect the operation of each gate for superconducting quantum devices. However, existing quantum synthesis frameworks, like IBM OpenPulse [1], involve massive redundant computation during pulse generation, suffering from a high computational cost when handling large-scale circuits. In this paper, we propose QPulseLib, a novel library with reusable pulses that can directly provide the pulse of a circuit block. To establish this library, we transform the circuit and apply convolutional operators to extract reusable patterns and pre-calculate their resultant pulses. Then, we develop a matching algorithm to identify such patterns shared by the target circuit. Experiments show that QPulseLib achieves 158.46 × and 16.03 × speedup for pulse generation, compared to OpenPulse and AccQOC [2].

In the current Noisy Intermediate-Scale Quantum era, quantum circuit analysis is an essential technique for designing high-performance quantum programs. Current analysis methods exhibit either accuracy limitations or high computational complexity for obtaining precise results. To reduce this tradeoff, we propose QuCT, a unified framework for extracting, analyzing, and optimizing quantum circuits. The main innovation of QuCT is to vectorize each gate with each element, quantitatively describing the degree of the interaction with neighboring gates. Extending from the vectorization model, we propose two representative downstream models for fidelity prediction and unitary decomposition. The fidelity prediction model performs a linear transformation on all gate vectors and aggregates the results to estimate the overall circuit fidelity. By identifying critical weights in the transformation matrix, we propose two optimizations to improve the circuit fidelity. In the unitary decomposition model, we significantly reduce the search space by bridging the gap between unitary and circuit via gate vectors. Experiments show that QuCT improves the accuracy of fidelity prediction by 4.2 × on 5-qubit and 18-qubit quantum devices and achieves 2.5 × fidelity improvement compared to existing quantum compilers [19, 55]. In unitary decomposition, QuCT achieves 46.3 × speedup for 5-qubit unitary and more than hundreds of speedup for 8-qubit unitary, compared to the state-of-the-art method [87].

The development of digitized humanity information provides a new perspective on data-oriented studies of history. Many previous studies have ignored uncertainty in the exploration of historical figures and events, which has limited the capability of researchers to capture complex processes associated with historical phenomena. We propose a visual reasoning system to support visual reasoning of uncertainty associated with spatio-temporal events of historical figures based on data from the China Biographical Database Project. We build a knowledge graph of entities extracted from a historical database to capture uncertainty generated by missing data and error. The proposed system uses an overview of chronology, a map view, and an interpersonal relation matrix to describe and analyse heterogeneous information of events. The system also includes uncertainty visualization to identify uncertain events with missing or imprecise spatio-temporal information. Results from case studies and expert evaluations suggest that the visual reasoning system is able to quantify and reduce uncertainty generated by the data.

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.

The convergence of the Internet and traditional industries gives the birth to the crossover services, which break through the boundaries of domains, enterprises, and businesses. The design of the service pattern is one of the key points to the success of crossover services. Existing works are mainly aimed at resource and process convergence, unable to guide the design of whole crossover service. To address this issue, we propose a rule-based service pattern convergence framework for crossover service, which consists of participant convergence, resource convergence and service process convergence. In addition to summarizing the general rules of convergence, we introduce semantic similarity to promote deep pattern convergence. Finally, a case study is presented to prove the operability of this framework.

The real-time processing of instantaneous bell target signal puts forward a higher demand in real-time processing ability and algorithm complexity. According to the high computational load of the point-wise sliding DFT method used in multiple points-wise sliding algorithm, a novel multiple points-wise sliding DFT algorithm is proposed in this paper based on sliding window model with a wide application in data streams processing. The calculation structure of the algorithm is modified by using DIT algorithm. The simulation results show that the algorithm could calculate the instantaneous bell target signal amplitude in real-time with less operations and has an ability of abnormal points tolerance in data sampling.