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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.

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.

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., 11 Figures, 4 Tables

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., 8 pages, 6 figures

Among different quantum algorithms, PQC for QML show promises on near-term devices. To facilitate the QML and PQC research, a recent python library called TorchQuantum has been released. It can construct, simulate, and train PQC for machine learning tasks with high speed and convenient debugging supports. Besides quantum for ML, we want to raise the community's attention on the reversed direction: ML for quantum. Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency. This paper presents a case study of the ML for quantum part. Since estimating the noise impact on circuit reliability is an essential step toward understanding and mitigating noise, we propose to leverage classical ML to predict noise impact on circuit fidelity. Inspired by the natural graph representation of quantum circuits, we propose to leverage a graph transformer model to predict the noisy circuit fidelity. We firstly collect a large dataset with a variety of quantum circuits and obtain their fidelity on noisy simulators and real machines. Then we embed each circuit into a graph with gate and noise properties as node features, and adopt a graph transformer to predict the fidelity. Evaluated on 5 thousand random and algorithm circuits, the graph transformer predictor can provide accurate fidelity estimation with RMSE error 0.04 and outperform a simple neural network-based model by 0.02 on average. It can achieve 0.99 and 0.95 R$^2$ scores for random and algorithm circuits, respectively. Compared with circuit simulators, the predictor has over 200X speedup for estimating the fidelity., ICCAD 2022; 10 pages, 10 figures; code at https://github.com/mit-han-lab/torchquantum

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., 13 pages, 14 figures

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., 6 pages, 5 figures

Variational quantum algorithms (VQAs) have demonstrated great potentials in the 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. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on transmons. And the control pulses need elaborate calibration 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 PAN, a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed PAN, we are tuning parametric pulses, which are natively supported on NISQ computers. Considering that parameter-shift rules do not hold for native-pulse ansatz, we need to deploy non-gradient optimizers. 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 to validate our methods. When adopted on NISQ machines, PAN obtained improved the performance with decreased latency by an average of 86%. PAN is able to achieve 96.482% and 99.336% accuracy for VQE tasks on H2 and HeH+ respectively, An average accuracy of 97.27% is achieved for medium-size VQE tasks on CO2, H2O, and NaH. PAN also demonstrates advantages on QAOA tasks even with considerable noises in NISQ machines., 13 pages, 13 figures