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Effective quantum feature maps in quantum extreme reservoir computation from the XY model

Aoi Hayashi, Akitada Sakurai, William J. Munro, Kae Nemoto

Published Feb 18, 2025• 0 citations
OpenAlex

Abstract

It is well known in quantum machine learning that the form of the feature map has a significant effect on the operation and performance of those tasks being undertaken. It is essential to investigate how to generate an effective feature map within the limitations of the number of qubits and quantum gates to apply in noisy intermediate-scale quantum devices. In this paper, we explore how the qubit connectivity impacts the performance of quantum extreme reservoir computation (QERC) for image classification using the XY Hamiltonian. We find that networks of qubits with simple connectivity generate an effective feature map, whereas a network with high connectivity does not since the XY Hamiltonian on it yields a unitary map with a periodic property. The existence of the periodicity can be fully determined by the underlying connectivity and the overall symmetry of the Hamiltonian. However, the symmetries in the XY Hamiltonians can contribute to a better generalization for QERC. We further find that it is possible to design QERC to give this high generalization property while maintaining high accuracy. Our work suggests that Hamiltonian dynamics with simple connectivity or symmetry can be exploited as a computational resource in quantum machine learning.

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It is well known in quantum machine learning that the form of the feature map has a significant effect on the operation and performance of those tasks being undertaken. It is essential to investigate how to generate an effective feature map within the limitations of the number of qubits and quantum gates to apply in noisy intermediate-scale quantum devices. In this paper, we explore how the qubit connectivity impacts the performance of quantum extreme reservoir computation (QERC) for image classification using the XY Hamiltonian. We find that networks of qubits with simple connectivity generate an effective feature map, whereas a network with high connectivity does not since the XY Hamiltonian on it yields a unitary map with a periodic property. The existence of the periodicity can be fully determined by the underlying connectivity and the overall symmetry of the Hamiltonian. However, the symmetries in the XY Hamiltonians can contribute to a better generalization for QERC. We further find that it is possible to design QERC to give this high generalization property while maintaining high accuracy. Our work suggests that Hamiltonian dynamics with simple connectivity or symmetry can be exploited as a computational resource in quantum machine learning.
It is well known in quantum machine learning that the form of the feature map has a significant effect on the operation and performance of those tasks being undertaken. It is essential to investigate how to generate an effective feature map within the limitations of the number of qubits and quantum gates to apply in noisy intermediate-scale quantum devices. In this paper, we explore how the qubit connectivity impacts the performance of quantum extreme reservoir computation (QERC) for image classification using the Hamiltonian. We find that networks of qubits with simple connectivity generate an effective feature map, whereas a network with high connectivity does not since the Hamiltonian on it yields a unitary map with a periodic property. The existence of the periodicity can be fully determined by the underlying connectivity and the overall symmetry of the Hamiltonian. However, the symmetries in the Hamiltonians can contribute to a better generalization for QERC. We further find that it is possible to design QERC to give this high generalization property while maintaining high accuracy. Our work suggests that Hamiltonian dynamics with simple connectivity or symmetry can be exploited as a computational resource in quantum machine learning.
The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computation. Key to QERC is the generation of a complex quantum reservoir (feature space) that does not need to be optimized for different problem instances. Originally, a periodically driven system Hamiltonian dynamics was employed as the quantum feature map. In this work we capture how the quantum feature map is generated as the number of time-steps of the dynamics increases by a method to characterize unitary matrices in the form of weighted networks. Furthermore, to identify the key properties of the feature map that has sufficiently grown, we evaluate it with various weighted network models that could be used for the quantum reservoir in image classification situations. At last, we show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly optimal performance while removing the necessity of programming of the quantum processor gate by gate.
Quantum machine learning (QML) is expected to offer new opportunities to process high-dimensional data efficiently by exploiting the exponentially large state space of quantum systems. In this work, we apply quantum extreme reservoir computing (QERC) to the classification of microstructure images of polymer alloys generated using self-consistent field theory (SCFT). While previous QML efforts have primarily focused on benchmark datasets such as MNIST, our work demonstrates the applicability of QERC to engineering data with direct materials relevance. Through numerical experiments, we examine the influence of key computational parameters-including the number of qubits, sampling cost (the number of measurement shots), and reservoir configuration-on classification performance. The resulting phase classifications are depicted as phase diagrams that illustrate the phase transitions in polymer morphology, establishing an understandable connection between quantum model outputs and material behavior. These results illustrate QERC performance on realistic materials datasets and suggest practical guidelines for quantum encoder design and model generalization. This work establishes a foundation for integrating quantum learning techniques into materials informatics.
Interest in quantum machine learning is growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use of quantum machine-learning techniques for image-classification tasks. We exploit a quantum extreme learning machine by taking advantage of its rich feature map provided by the quantum reservoir substrate. We systematically analyze different phases of the quantum extreme learning machine process, from dataset preparation to final image classification. In particular, we test different encodings, together with principal component analysis and the use of autoencoders, and we examine the dynamics of the model through the use of different Hamiltonians for the quantum reservoir. Our results show that the introduction of a quantum reservoir systematically improves the accuracy of the classifier. In addition, our findings indicate that variations in encoding methods can significantly influence performance and that Hamiltonians with distinct structures exhibit the same discrimination rate, depending on how their eigenstates are related to the encoding and measurement basis.
Reservoir models offer a hardware-efficient learning paradigm for noisy intermediate-scale quantum devices by exploiting untrained quantum dynamics as a fixed feature map and restricting optimization to a simple classical readout layer. We propose a quantum extreme learning machine implemented using gaussian boson sampling and an encoding strategy that achieves high classification accuracy while reducing optical resource requirements. Classical inputs are jointly encoded in the squeezing parameters and in the interferometer unitary, enabling sampling-based, highly nonlinear feature maps while leveraging large-scale GBS output statistics, which are conjectured to be classically intractable. We systematically compare multiple families of quantum features accessible in the same setup and find that photon-number sampling probabilities provide the best performance, consistent with their higher effective feature dimensionality. Finally, we benchmark against classical nonlinear baselines and analyse robustness under noisy scenarios, showing competitive performance with fewer trainable parameters and indicating practical promise for near-term photonic implementations.
Quantum reservoir computing employs fixed quantum dynamics as a feature map for machine learning. Integrating multiple quantum reservoirs, however, raises a key question: how few inter-module connections are sufficient to match the performance of a single reservoir? To address this, we explicitly separate intra-module dynamics from inter-module couplings and systematically examine different connectivity schemes. We find that even a small number of well-placed connections between two modules can match single-reservoir accuracy, with simple one-to-one connections proving highly effective. Performance generally improves with increasing inter-module entanglement, and these correlations persist for both ZZZZZZ-coupled and random modular reservoirs. Extensions to three modules and evaluations across multiple datasets (MNIST, Fashion-MNIST, CIFAR-10) suggest that the modular architecture can be applied to diverse reservoir types and image-classification datasets. These results motivate modular quantum reservoir designs that align naturally with realistic hardware, such as two-dimensional quantum-chip layouts or networks of small integrated quantum systems.