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arXiv:2407.02553

Large-scale quantum reservoir learning with an analog quantum computer

Milan Kornjača, Hong-Ye Hu, Chen Zhao, Jonathan Wurtz, Phillip Weinberg, Majd Hamdan, Andrii Zhdanov, Sergio Cantu, Hengyun Zhou, Rodrigo Araiza Bravo, Kevin R. Bagnall, James I. Basham, Joseph Campo, Adam Choukri, Robert DeAngelo, Paige Frederick, David Haines, Julian Hammett, Hsu, Ning, Ming-Guang Hu, Huber, Florian, Niklas Jepsen, Ningyuan Jia, Thomas Karolyshyn, Kwon, Minho, Long, John, Jonathan Lopatin, Lukin, Alexander, Tommaso Macrì, Ognjen Marković, Luis Á. Martínez-Martínez, Meng, Xianmei, Evgeny E. Ostroumov, David Paquette, Robinson, John, Pedro Sales Rodriguez, Anshuman Singh, Nandan Kumar Sinha, Henry Thoreen, Noel Wan, Daniel Waxman-Lenz, Tak Wong, Kai-Hsin Wu, Pedro L. S. Lopes, Yuval Boger, Nathan Gemelke, Takuya Kitagawa, Alexander Keesling, Xun Gao, Alexei Bylinskii, Susanne F. Yelin, Fangli Liu, Sheng-Tao Wang

Published Jul 2, 2024• 9 citations
arXivHTMLPDF

Abstract

Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks.

DOI
10.48550/arxiv.2407.02553

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The rapid development of machine learning and quantum computing has placed quantum machine learning at the forefront of research. However, existing quantum machine learning algorithms based on quantum variational algorithms face challenges in trainability and noise robustness. In order to address these challenges, we introduce a gradient-free, noise-robust quantum reservoir computing algorithm that harnesses discrete time crystal dynamics as a reservoir. We first calibrate the memory, nonlinear, and information scrambling capacities of the quantum reservoir, revealing their correlation with dynamical phases and non-equilibrium phase transitions. We then apply the algorithm to the binary classification task and establish a comparative quantum kernel advantage. For ten-class classification, both noisy simulations and experimental results on superconducting quantum processors match ideal simulations, demonstrating the enhanced accuracy with increasing system size and confirming the topological noise robustness. Our work presents the first experimental demonstration of quantum reservoir computing for image classification based on digital quantum simulation. It establishes the correlation between quantum many-body non-equilibrium phase transitions and quantum machine learning performance, providing new design principles for quantum reservoir computing and broader quantum machine learning algorithms in the NISQ era.
Quantum computation is a form of computation utilizing quantum mechanical phenomena.A formal formulation of quantum computer was proposed in 1985, leading to novel quantum algorithms whose computation time scales better, with respect to the problem size, than the classical counterparts.However, running these quantum algorithms requires fault-tolerant implementations, and hence, such engineering in quantum systems can be an overhead in utilizing quantum systems for information processing.This suggests, in addition to seeking faster quantum algorithms, the necessity of exploring how to harness quantum systems more naturally without extensive engineering, leading to another form of quantum information processing.Quantum machine learning (QML) is expected to be a promising approach for harnessing quantum systems for information processing.This is motivated by the fact that the success of neural networks has led to the possibility of solving ML tasks more efficiently with non-digital computer designs.Several QML algorithms that resemble neural networks utilize quantum systems in a manner distinct from digital quantum algorithms.However, training quantum systems is fundamentally challenging, suggesting that it still remains necessary to explore an alternative way of harnessing quantum systems for ML.Along with this direction, the quantum reservoir model harnesses quantum systems in a unique way: nontrivial outputs from an untrained quantum system are leveraged by a linear classical machine learning model to solve various tasks.Hence, the quantum part does not need to be programmable and can be analog quantum dynamics.Without extensive engineering in the quantum part, a quantum reservoir model was shown to be capable of performing nontrivial ML tasks using physical dynamics, which had already been realized experimentally.Therefore, the quantum reservoir model should be one of the promising directions for pursuing another form of quantum information processing.In this thesis, I will investigate the computational capability of natural quantum dynamics in the quantum reservoir model.I will tackle the following critical open questions in this context: (i) how well natural dynamics perform nontrivial ML tasks compared to engineered systems, and (ii) what kind of engineering in quantum dynamics is essential and appropriate for a given task.To tackle the first question (i), I will benchmark the learning performance of various natural quantum dynamics against random circuits that require complicated engineering, thereby revealing the computational power of natural dynamics.For the second question (ii), I will present a characterization method of quantum dynamics that has the potential to bridge quantum properties and their machine learning roles, providing concrete insights into essential and appropriate engineering to design quantum systems for ML.v
Neutral-atom quantum devices offer a natural and powerful platform for Quantum Reservoir Computing (QRC), a non-variational approach to quantum-enhanced machine learning that is attracting growing interest in the Noisy Intermediate-Scale Quantum (NISQ) era. In this work, we implement a QRC-based learning model tailored to Pasqal neutral-atom hardware, where classical data are encoded into time-dependent detuning waveforms and processed through programmable many-body dynamics. Feature embeddings are extracted from single- and two-qubit Pauli-ZZZ observables and used within a simple linear readout model. We systematically investigate both global and local detuning encoding strategies on a supervised image classification task, benchmarking performance against a purely classical linear baseline. Our results show that processing the data through the quantum reservoir yields a consistent $\sim$4\% improvement in accuracy, highlighting the system effectiveness as a nonlinear feature map. Both encoding strategies exhibit comparable performance, suggesting that QRC can leverage different control schemes while remaining compatible with near-term and emerging hardware capabilities. These findings provide concrete evidence that QRC can enhance classical machine learning pipelines by injecting non-classical correlations without the need for variational optimization, thereby avoiding issues such as barren plateaus and training instabilities. More broadly, this work establishes neutral-atom platforms as a promising and scalable setting for QRC, opening the way toward practical quantum-enhanced learning on near-term devices.
Quantum Artificial Intelligence (Quantum AI) represents a rapidly developing interdisciplinary field at the intersection of quantum computing and machine learning (ML). It holds the promise of unlocking unprecedented computational capabilities for complex optimization tasks, large-scale data processing, and advanced pattern recognition. In this research, we provide a comprehensive examination of two principal quantum algorithms—the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE)—applied to classical ML challenges. Using a hybrid simulation framework that integrates TensorFlow, scikit-learn, Qiskit, and Cirq, we extensively benchmark quantum-enhanced approaches against conventional methods on both combinatorial optimization and image classification tasks. Our findings indicate that while noise and qubit limitations remain critical barriers, quantum-enhanced models can achieve competitive, and sometimes superior, performance compared to purely classical solutions. We elaborate on the practical implications of these results, discuss hardware and algorithmic constraints, and propose future research directions focusing on error mitigation, scalability, and quantum-native ML models. These insights pave the way for a new computational paradigm, in which quantum resources are harnessed to address previously intractable ML problems.
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We then propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime. For near-term implementations, we demonstrate a significant prediction advantage over some classical models on engineered data sets designed to demonstrate a maximal quantum advantage in one of the largest numerical tests for gate-based quantum machine learning to date, up to 30 qubits.