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International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

Quantum circuit output prediction based on time-series neural network integration

Xiang Li, Xueyun Cheng, Xinyu Chen, Zhijin Guan, Pengcheng Zhu, Hui Gu

Published Jan 19, 2024• 2 citations
Publisher

Abstract

In the context of current Noisy Intermediate-Scale Quantum (NISQ) computers, the application of quantum circuits is hampered by various sources of noise, resulting in errors between the actual output and the ideal output. To mitigate these errors, this study investigates a method for predicting the expected output of quantum circuits. In order to enhance the precision of the experiments, a fixed-scale quantum circuit dataset is randomly collected, and one-hot encoding is applied to represent the data. To eliminate redundant noise in the dataset, a Quantum auto-encoder (QAE) reconstruction encoding model is designed to separate and reconstruct the descriptions of quantum circuits in the dataset. The operation process of a quantum circuit is a sequence of quantum gate operations, and gate operations are gradually realized to change the state of qubits. We regard the process of quantum circuit implementation as a process of evolution with time, so it has the nature of timing. We build an additive fusion prediction model based on temporal neural network, the model can be trained to learn the relationship between sequences to predict the output of quantum circuit, and a loss function based on maximum likelihood estimation is employed to optimize the model parameters. Additionally, a dual approach is verified to optimize the computational indicators in the ensemble model. To validate the effectiveness of the proposed method, experiments are conducted on quantum circuits of varying scales, and the results are compared with the quantum system framework provided by IBM Quantum Experience. The experimental results show that the time series neural network integration can better highlight timing information and noise information in quantum circuits, the proposed method outperforms the IBM Quantum Experience framework in terms of circuit depth and network depth for quantum circuits of different scales, exhibiting robustness and stability.

DOI
10.1145/3673277.3673369

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In the context of current Noisy Intermediate-Scale Quantum (NISQ) computers, the application of quantum circuits is hampered by various sources of noise, resulting in errors between the actual output and the ideal output. To mitigate these errors, this study investigates a method for predicting the expected output of quantum circuits. In order to enhance the precision of the experiments, a fixed-scale quantum circuit dataset is randomly collected, and one-hot encoding is applied to represent the data. To eliminate redundant noise in the dataset, a Quantum auto-encoder (QAE) reconstruction encoding model is designed to separate and reconstruct the descriptions of quantum circuits in the dataset. The operation process of a quantum circuit is a sequence of quantum gate operations, and gate operations are gradually realized to change the state of qubits. We regard the process of quantum circuit implementation as a process of evolution with time, so it has the nature of timing. We build an additive fusion prediction model based on temporal neural network, the model can be trained to learn the relationship between sequences to predict the output of quantum circuit, and a loss function based on maximum likelihood estimation is employed to optimize the model parameters. Additionally, a dual approach is verified to optimize the computational indicators in the ensemble model. To validate the effectiveness of the proposed method, experiments are conducted on quantum circuits of varying scales, and the results are compared with the quantum system framework provided by IBM Quantum Experience. The experimental results show that the time series neural network integration can better highlight timing information and noise information in quantum circuits, the proposed method outperforms the IBM Quantum Experience framework in terms of circuit depth and network depth for quantum circuits of different scales, exhibiting robustness and stability.
The current advancement in quantum computers has been focusing on increasing the number of qubits and enhancing their fidelity. However, the available quantum devices, known as intermediate scale quantum (NISQ) computers, still suffer from different sources of noise that impact their reliability. Thus, practical noise modeling is of great importance in the development of quantum error mitigation approaches. In this article, we propose a machine learning (ML)-based scheme to predict the output fidelity of the quantum circuit executed on NISQ devices. We show the benefit of using graph neural network (GNN)-based models compared to traditional ML-based models in capturing the quantum circuit structure in addition to its gates’ features, which enable characterizing unpredicted quantum circuit errors. We use different metrics to measure the fidelity of the quantum circuit output. Our experimental results using different quantum algorithms executed on IBM Q Guadalupe quantum computer show the high prediction accuracy of our ML reliability models. Our results also show that our models can guide the single-qubit gate rescheduling to improve the output fidelity of the quantum circuit without the need for prior execution of dedicated calibration circuits.
Forecasting temporal dynamics underpins many areas of science and engineering, from large-scale atmospheric prediction to nanoscale quantum control. Classical approaches, including autoregressive models and deep neural networks, have advanced sequential learning often at the expense of known model order, or large dataset and parameters, resulting in computational cost. Here, we investigate whether quantum entanglement can serve as a resource for temporal pattern learning using shallow and structured quantum circuits. We have proposed a Quantum Time Series (QTS) framework that encodes normalised sequential data into single-qubit rotations and captures temporal correlations through forward and cross-entanglement layers. Among several encoding schemes, phase encoding-based sparse entanglement provides hardware efficiency by scaling to larger qubit systems with linear circuit depth and two-qubit complexity of [Formula: see text] for qubit size n. This offers a reduction in parameters and depth compared with deep variational quantum circuits such as Heisenberg-inspired circuits, and random-parametric unitary architectures. Experiments on synthetic and geophysical datasets show that shallow QTS circuits reproduce complex temporal pattern from limited data by leveraging structured quantum entanglement. Executions on IBM's Heron and Eagle-class processors demonstrate robustness and scalability up to 100 qubits. These results suggest that structured entanglement may offer a short-term memory effect for time-series analysis, providing a scalable route for near-term quantum applications.
This research presents a Dual Quantum-Traditional Auto encoder network designed for near-term quantum processors. This Dual AE consists of a variational Auto encoder and decoder network which are processed and trained using a minimal computer. In the encoding stage quantum circuits and angle based encoding method is utilized for implementation. Further the decoder model is implemented using differential two stage quantum encoding circuits. A compatible approach is developed for quantum networks thereby providing control over the accuracy balance and complexity of the circuit mechanism. By using this compatible approach scalability of the model can be improved and more qubits can be concerned in dataset dimensions. Models’ loss function is obtained for the implementation for quantum circuits in order to eliminating the need for more auxiliary bits. IBM Qis Kit development kit is utilized for simulating the process. However, a traditional computer is used for training and testing the variational auto encoder model by using Ada grad optimization algorithm. A dual traditional-quantum method is implemented using a four-stage network with varying qubit system. Proposed method is tested and the obtained results shows that the model achieve optimal performance which includes 68 gates and various trainable parameters. Minimal divergence is obtained between real data when compared with generated ones.
A quantum error mitigation technique based on machine learning is proposed, which learns how to adjust the probabilities estimated by measurement in the computational basis. Neural networks in two different designs are trained with random quantum circuits consisting of a set of one- and two-qubit unitary gates whose measurement statistics in the ideal (noiseless) and real (noisy) cases are known. Once the neural networks are trained, they infer the amount of probability adjustment to be made on the measurement obtained from executing an unseen quantum circuit to reduce the error. The proposed schemes are tested with two-, three-, five-, and seven-qubit quantum circuits of depth up to 20 by computer simulations with realistic error models and experiments using the IBM quantum cloud platform. In all test cases, the proposed mitigation technique reduces the error effectively. Our method can be used to improve the accuracy of noisy intermediate-scale quantum (NISQ) algorithms without relying on extensive error characterization or quantum error correction.
Although quantum computers will not necessarily transform ML in general, they hold immense potential for specific uses, such as dealing with high-dimensional data and complex correlations that cannot be effectively modeled using classical systems. It is, however, difficult to design efficient quantum circuits, especially with the existing Noisy Intermediate-Scale Quantum (NISQ) hardware that has constraints including decoherence, a limited number of qubits, and gate fidelity limitations. The authors propose an Adaptive Quantum Circuit Architecture Search (AQCAS) framework whose structure and parameters are optimized dynamically to improve the performance of the task-specific ML. The framework integrates quantum circuit encoding, a hybrid search strategy that is based on reinforcement learning and evolutionary algorithms, and a task-specific adaptivity mechanism. Benchmark datasets were used for experimental evaluations of both simulation using Qiskit Aer and classical optimization using the Python frameworks TensorFlow and PennyLane. Fixed and standard variational quantum circuits were compared with performance measurements like the classification accuracy, circuit depth, convergence speed, and computational efficiency. The results show that the accuracy of AQCAS can be raised to 94.1%, and the circuit depth is lowered at the same time. It converged rapidly and is computationally efficient. Ablation studies show that both hybrid search and adaptivity mechanisms help to enhance generalization and stability under NISQ constraints. The results demonstrate the potential of AQCAS as a practical approach to using adaptive quantum circuits in specialized ML applications, and lay the groundwork for future development of scaling to larger devices, implementing noise-aware optimization, and integrating with hybrid quantum-classical ML pipelines.
We proposed a time-delayed quantum extreme learning machine (TD-QELM) for efficient time-series prediction on noisy intermediate-scale quantum (NISQ) devices. By encoding multiple past inputs simultaneously, TD-QELM achieves shallow circuit depth independent of sequence length, thereby, mitigating noise accumulation and reducing computational complexity. Experiments using the NARMA benchmark on both noiseless simulations and IBM's 127-qubit processor demonstrate that TD-QELM consistently outperforms conventional quantum reservoir computing in prediction accuracy and noise robustness. These results highlight TD-QELM as a practical and scalable framework for time-series learning on current NISQ hardware.