arXiv subjects

Zhiding Liang

Publications and source records attributed to Zhiding Liang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Tackling Coherent Noise in Quantum Computing via Cross-Layer Compiler Optimization

Quantum computing hardware is affected by quantum noise that undermine the quality of results of an executed quantum program. Amongst other quantum noises, coherent error that caused by parameter drifting and miscalibration, remains critical. While coherent error mitigation has been studied before, studies focused either on gate-level or pulse-level -- missing cross-level optimization opportunities; And most of them only target single-qubit gates -- while multi-qubit gates are also used in practice. To address above limitations, this work proposes a cross-layer approach for coherent error mitigation that considers program-level, gate-level, and pulse-level compiler optimizations, by leveraging the hidden inverse theory, and exploiting the structure inside different quantum programs, while also considering multi-qubit gates. We implemented our approach as compiler optimization passes, and integrated into IBM Qiskit framework. We tested our technique on real quantum computer (IBM-Brisbane), and demonstrated up to 92% fidelity improvements (45% on average), on several benchmarks.

cs.AR↗

arXiv2024

A comparison on constrain encoding methods for quantum approximate optimization algorithm

The Quantum Approximate Optimization Algorithm (QAOA) represents a significant opportunity for practical quantum computing applications, particularly in the era before error correction is fully realized. This algorithm is especially relevant for addressing constraint satisfaction problems (CSPs), which are critical in various fields such as supply chain management, energy distribution, and financial modeling. In our study, we conduct a numerical comparison of three different strategies for incorporating linear constraints into QAOA: transforming them into an unconstrained format, introducing penalty dephasing, and utilizing the quantum Zeno effect. We assess the efficiency and effectiveness of these methods using the knapsack problem as a case study. Our findings provide insights into the potential applicability of different encoding methods for various use cases.

quant-ph↗

arXiv2024

Coqa: Blazing Fast Compiler Optimizations for QAOA

The Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage over classical computers. However, existing compilers lack specialized methods for optimizing QAOA circuits. There are circuit patterns inside the QAOA circuits, and current quantum hardware has specific qubit connectivity topologies. Therefore, we propose Coqa to optimize QAOA circuit compilation tailored to different types of quantum hardware. Our method integrates a linear nearest-neighbor (LNN) topology and efficiently map the patterns of QAOA circuits to the LNN topology by heuristically checking the interaction based on the weight of problem Hamiltonian. This approach allows us to reduce the number of SWAP gates during compilation, which directly impacts the circuit depth and overall fidelity of the quantum computation. By leveraging the inherent patterns in QAOA circuits, our approach achieves more efficient compilation compared to general-purpose compilers. With our proposed method, we are able to achieve an average of 30% reduction in gate count and a 39x acceleration in compilation time across our benchmarks.

quant-ph↗

arXiv2024

Hybrid Quantum Downsampling Networks

Classical max pooling plays a crucial role in reducing data dimensionality among various well-known deep learning models, yet it often leads to the loss of vital information. We proposed a novel hybrid quantum downsampling module (HQD), which is a noise-resilient algorithm. By integrating a substantial number of quantum bits (qubits), our approach ensures the key characteristics of the original image are maximally preserved within the local receptive field. Moreover, HQD provides unique advantages in the context of the noisy intermediate-scale quantum (NISQ) era. We introduce a unique quantum variational circuit in our design, utilizing rotating gates including RX, RY, RZ gates, and the controlled-NOT (CNOT) gate to explore nonlinear characteristics. The results indicate that the network architectures incorporating the HQD module significantly outperform the classical structures with max pooling in CIFAR-10 and CIFAR-100 datasets. The accuracy of all tested models improved by an average of approximately 3%, with a maximum fluctuation of only 0.4% under various quantum noise conditions.

quant-ph↗

arXiv2024

EPOC: A Novel Pulse Generation Framework Incorporating Advanced Synthesis Techniques for Quantum Circuits

In this paper we propose EPOC, an efficient pulse generation framework for quantum circuits that combines ZX-Calculus, circuit partitioning, and circuit synthesis to accelerate pulse generation. Unlike previous works that focus on generating pulses from unitary matrices without exploring equivalent representations, EPOC employs a finer granularity approach by grouping quantum gates and decomposing the resulting unitary matrices into smaller ones using synthesis techniques. This enables increased parallelism and decreased latency in quantum pulses. EPOC also continuously optimizes the circuit by identifying equivalent representations, leading to further reductions in circuit latency while minimizing the computational overhead associated with quantum optimal control. We introduce circuit synthesis into the workflow of quantum optimal control for the first time and achieve a 31.74% reduction in latency compared to previous work and a 76.80% reduction compared to the gate-based method for creating pulses. The approach demonstrates the potential for significant performance improvements in quantum circuits while minimizing computational overhead.

quant-ph↗

arXiv2024

Towards Advantages of Parameterized Quantum Pulses

The advantages of quantum pulses over quantum gates have attracted increasing attention from researchers. Quantum pulses offer benefits such as flexibility, high fidelity, scalability, and real-time tuning. However, while there are established workflows and processes to evaluate the performance of quantum gates, there has been limited research on profiling parameterized pulses and providing guidance for pulse circuit design. To address this gap, our study proposes a set of design spaces for parameterized pulses, evaluating these pulses based on metrics such as expressivity, entanglement capability, and effective parameter dimension. Using these design spaces, we demonstrate the advantages of parameterized pulses over gate circuits in the aspect of duration and performance at the same time thus enabling high-performance quantum computing. Our proposed design space for parameterized pulse circuits has shown promising results in quantum chemistry benchmarks.

quant-ph↗

arXiv2024

Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm

In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods. Among these, the Quantum Approximate Optimization Algorithm (QAOA) stands out for its potential to efficiently solve the Max-Cut problem, a quintessential example of combinatorial optimization. However, practical application faces challenges due to current limitations on quantum computational resource. Our work optimizes QAOA initialization, using Graph Neural Networks (GNN) as a warm-start technique. This sacrifices affordable computational resource on classical computer to reduce quantum computational resource overhead, enhancing QAOA's effectiveness. Experiments with various GNN architectures demonstrate the adaptability and stability of our framework, highlighting the synergy between quantum algorithms and machine learning. Our findings show GNN's potential in improving QAOA performance, opening new avenues for hybrid quantum-classical approaches in quantum computing and contributing to practical applications.

quant-ph↗

arXiv2024

NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms

Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have seen various efforts to draft better ansatz with less gates. Some works consider the physical meaning of the underlying circuits, while others adopt the ideas of neural architecture search (NAS) for ansatz generator. However, these designs do not exploit the full advantages of VQAs. Because most techniques target gate ansatz, and the parameters are usually rotation angles of the gates. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on superconducting qubits. These control pulses need elaborate calibrations to minimize the errors such as over-rotation and under-rotation. In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters. Therefore, we propose NAPA, a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed NAPA, we are tuning parametric pulses, which are natively supported on NISQ computers. Given the limited availability of gradient-based optimizers for pulse-level quantum programs, we choose to deploy non-gradient optimizers in our framework. To constrain the number of parameters sent to the optimizer, we adopt a progressive way to generate our native-pulse ansatz. Experiments are conducted on both simulators and quantum devices for Variational Quantum Eigensolver (VQE) tasks to evaluate our methods.

quant-ph↗

arXiv2023

VIOLET: Visual Analytics for Explainable Quantum Neural Networks

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET, a novel visual analytics approach to improve the explainability of quantum neural networks. Guided by the design requirements distilled from the interviews with domain experts and the literature survey, we developed three visualization views: the Encoder View unveils the process of converting classical input data into quantum states, the Ansatz View reveals the temporal evolution of quantum states in the training process, and the Feature View displays the features a QNN has learned after the training process. Two novel visual designs, i.e., satellite chart and augmented heatmap, are proposed to visually explain the variational parameters and quantum circuit measurements respectively. We evaluate VIOLET through two case studies and in-depth interviews with 12 domain experts. The results demonstrate the effectiveness and usability of VIOLET in helping QNN users and developers intuitively understand and explore quantum neural networks

cs.HC↗

arXiv2023

SpacePulse: Combining Parameterized Pulses and Contextual Subspace for More Practical VQE

In this paper, we explore the integration of parameterized quantum pulses with the contextual subspace method. The advent of parameterized quantum pulses marks a transition from traditional quantum gates to a more flexible and efficient approach to quantum computing. Working with pulses allows us to potentially access areas of the Hilbert space that are inaccessible with a CNOT-based circuit decomposition. Compared to solving the complete Hamiltonian via the traditional Variational Quantum Eigensolver (VQE), the computation of the contextual correction generally requires fewer qubits and measurements, thus improving computational efficiency. Plus a Pauli grouping strategy, our framework, SpacePulse, can minimize the quantum resource cost for the VQE and enhance the potential for processing larger molecular structures.

quant-ph↗

arXiv2023

RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training

Quantum state preparation, a crucial subroutine in quantum computing, involves generating a target quantum state from initialized qubits. Arbitrary state preparation algorithms can be broadly categorized into arithmetic decomposition (AD) and variational quantum state preparation (VQSP). AD employs a predefined procedure to decompose the target state into a series of gates, whereas VQSP iteratively tunes ansatz parameters to approximate target state. VQSP is particularly apt for Noisy-Intermediate Scale Quantum (NISQ) machines due to its shorter circuits. However, achieving noise-robust parameter optimization still remains challenging. We present RobustState, a novel VQSP training methodology that combines high robustness with high training efficiency. The core idea involves utilizing measurement outcomes from real machines to perform back-propagation through classical simulators, thus incorporating real quantum noise into gradient calculations. RobustState serves as a versatile, plug-and-play technique applicable for training parameters from scratch or fine-tuning existing parameters to enhance fidelity on target machines. It is adaptable to various ansatzes at both gate and pulse levels and can even benefit other variational algorithms, such as variational unitary synthesis. Comprehensive evaluation of RobustState on state preparation tasks for 4 distinct quantum algorithms using 10 real quantum machines demonstrates a coherent error reduction of up to 7.1 $\times$ and state fidelity improvement of up to 96\% and 81\% for 4-Q and 5-Q states, respectively. On average, RobustState improves fidelity by 50\% and 72\% for 4-Q and 5-Q states compared to baseline approaches.

quant-ph↗

arXiv2023

Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design

Large Language Models (LLMs) contribute significantly to the development of conversational AI and has great potentials to assist the scientific research in various areas. This paper attempts to address the following questions: What opportunities do the current generation of generative pre-trained transformers (GPTs) offer for the developments of noisy intermediate-scale quantum (NISQ) technologies? Additionally, what potentials does the forthcoming generation of GPTs possess to push the frontier of research in fault-tolerant quantum computing (FTQC)? In this paper, we implement a QGAS model, which can rapidly propose promising ansatz architectures and evaluate them with application benchmarks including quantum chemistry and quantum finance tasks. Our results demonstrate that after a limited number of prompt guidelines and iterations, we can obtain a high-performance ansatz which is able to produce comparable results that are achieved by state-of-the-art quantum architecture search methods. This study provides a simple overview of GPT's capabilities in supporting quantum computing research while highlighting the limitations of the current GPT at the same time. Additionally, we discuss futuristic applications for LLM in quantum research.

quant-ph↗

arXiv2023

QuCS: A Lecture Series on Quantum Computer Software and System

In this era of incessant advancements in quantum computing, bridging the gap between quantum algorithms' hardware requisites and available devices has become crucial. A prime focus in this context is the Software and System Level support for quantum computers, which has shown promising potential in significantly decreasing this gap. However, a noteworthy deficit of quantum software and system level-focused courses has been observed in academia worldwide. Addressing this deficiency, this paper proposes the Quantum Computer Systems (QuCS) Lecture Series. The QuCS Lecture Series aims to enhance the visibility of quantum computing software and system level and foster diverse participation in quantum computing research across multiple universities worldwide. It is envisioned as an inclusive platform to bring together individuals of diverse backgrounds, catalyzing cross-cultural collaboration and innovation in this burgeoning field. The lecture series begins with an introductory session elucidating the core concepts and fundamentals of quantum computing. This foundational knowledge will be built upon in subsequent sessions, highlighting cutting-edge research trends and recent findings in quantum software and system level. This paper provides a comprehensive overview of the QuCS Lecture Series, detailing the format, the gamut of topics to be covered, and their significance. It emphasizes the potential impact of the series on accelerating progress towards quantum supremacy and fostering a diverse, global community of quantum computing researchers and practitioners. The QuCS Lecture Series has already hosted nearly 40 lectures with over 40 confirmed speakers from more than eight different countries and from both academia and industry, QuCS also attracted more than 1000 subscribers from all over the world.

physics.ed-ph↗

arXiv2023

Fidelity estimator, randomized benchmarking and ZNE for quantum pulses

Most previous research focused on designing pulse programs without considering the performance of individual elements or the final fidelity. To evaluate the performance of quantum pulses, it is required to know the noiseless results of the pulses. However, quantum pulses can implement unitary matrices that are not analytically known to the user, and pulse simulator usually comes with significant computational overhead. Consequently, determining fidelity of a pulse program is challenging without the knowledge of the ideal results. In this paper, we propose to use reversed pulses to evaluate the performance of quantum pulses, which can provide guidance to design pulse programs. By employing reversed pulses, we can ensure that, in the noiseless situation, the final quantum states are the same as the initial states. This method enables us to evaluate the fidelity of pulse programs by measuring the difference between the final states and the initial states. Such fidelity estimator can tell whether the results are meaningful for quantum pulses on real quantum machines. There are various quantum error correction (QEC) methods available for gate circuits; however, few studies have demonstrated QEC on pulse-level programs. In this paper, we use reversed pulses to implement zero noise extrapolation (ZNE) on pulse programs and demonstrate results for variational quantum eigensolver (VQE) tasks. The deviation from the idea energy value is reduced by an average of 54.1\% with our techniques.

quant-ph↗

arXiv2022

Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning

Due to the immense potential of quantum computers and the significant computing overhead required in machine learning applications, the variational quantum classifier (VQC) has received a lot of interest recently for image classification. The performance of VQC is jeopardized by the noise in Noisy Intermediate-Scale Quantum (NISQ) computers, which is a significant hurdle. It is crucial to remember that large error rates occur in quantum algorithms due to quantum decoherence and imprecision of quantum gates. Previous studies have looked towards using ensemble learning in conventional computing to reduce quantum noise. We also point out that the simple average aggregation in classical ensemble learning may not work well for NISQ computers due to the unbalanced confidence distribution in VQC. Therefore, in this study, we suggest that ensemble quantum classifiers be optimized with plurality voting. On the MNIST dataset and IBM quantum computers, experiments are carried out. The results show that the suggested method can outperform state-of-the-art on two- and four-class classifications by up to 16.0% and 6.1% , respectively.

quant-ph↗

arXiv2022

Hybrid Gate-Pulse Model for Variational Quantum Algorithms

Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, however, introduces significant redundancy when quantum gates are eventually transformed into control signals and applied on quantum devices. For superconducting quantum computers, the control signals are microwave pulses. Therefore, pulse-level optimization has gained more attention from researchers due to their advantages in terms of circuit duration. Recent works, however, are limited by their poor scalability brought by the large parameter space of control signals. In addition, the lack of gate-level "knowledge" also affects the performance of pure pulse-level frameworks. We present a hybrid gate-pulse model that can mitigate these problems. We propose to use gate-level compilation and optimization for "fixed" part of the quantum circuits and to use pulse-level methods for problem-agnostic parts. Experimental results demonstrate the efficiency of the proposed framework in discrete optimization tasks. We achieve a performance boost at most 8% with 60% shorter pulse duration in the problem-agnostic layer.

quant-ph↗

arXiv2022

TopGen: Topology-Aware Bottom-Up Generator for Variational Quantum Circuits

Variational Quantum Algorithms (VQA) are promising to demonstrate quantum advantages on near-term devices. Designing ansatz, a variational circuit with parameterized gates, is of paramount importance for VQA as it lays the foundation for parameter optimizations. Due to the large noise on Noisy-Intermediate Scale Quantum (NISQ) machines, considering circuit size and real device noise in the ansatz design process is necessary. Unfortunately, recent works on ansatz design either consider no noise impact or only treat the real device as a black box with no specific noise information. In this work, we propose to open the black box by designing specific ansatz tailored for the qubit topology on target machines. Specifically, we propose a bottom-up approach to generate topology-specific ansatz. Firstly, we generate topology-compatible sub-circuits with desirable properties such as high expressibility and entangling capability. Then, the sub-circuits are combined together to form an initial ansatz. We further propose circuits stitching to solve the sparse connectivity issue between sub-circuits, and dynamic circuit growing to improve the accuracy. The ansatz constructed with this method is highly flexible and thus we can explore a much larger design space than previous state-of-the-art method in which all ansatz candidates are strict subsets of a pre-defined large ansatz. We use a popular VQA algorithm - Quantum Neural Networks (QNN) for Machine Learning (ML) task as the benchmarks. Experiments on 14 ML tasks show that under the same performance, the TopGen-searched ansatz can reduce the circuit depth and the number of CNOT gates by up to 2 * and 4 * respectively. Experiments on three real quantum machines demonstrate on average 17% accuracy improvements over baselines.

quant-ph↗

arXiv2022

Variational Quantum Pulse Learning

Quantum computing is among the most promising emerging techniques to solve problems that are computationally intractable on classical hardware. A large body of existing works focus on using variational quantum algorithms on the gate level for machine learning tasks, such as the variational quantum circuit (VQC). However, VQC has limited flexibility and expressibility due to limited number of parameters, e.g. only one parameter can be trained in one rotation gate. On the other hand, we observe that quantum pulses are lower than quantum gates in the stack of quantum computing and offers more control parameters. Inspired by the promising performance of VQC, in this paper we propose variational quantum pulses (VQP), a novel paradigm to directly train quantum pulses for learning tasks. The proposed method manipulates variational quantum pulses by pulling and pushing the amplitudes of pulses in an optimization framework. Similar to variational quantum algorithms, our framework to train pulses maintains the robustness to noise on Noisy Intermediate-Scale Quantum (NISQ) computers. In an example task of binary classification, VQP learning achieves up to 11% and 9% higher accuracy compared with VQC learning on the qiskit noise simulators (with noise model from real machine) and ibmq-jarkata, respectively, demonstrating its effectiveness and feasibility. Stability for VQP to obtain reliable results has also been verified in the presence of noise.

quant-ph↗