Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session AAA05: V: Quantum Machine Learning |
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Sponsoring Units: DQI Chair: Ryan Sweke, IBM Quantum Room: Virtual Room 5 |
Wednesday, March 22, 2023 12:30PM - 12:42PM |
AAA05.00001: Experimental Quantum End-to-End Learning on a Superconducting Processor Xiaoxuan Pan, Xi Cao, Weiting Wang, Ziyue Hua, Weizhou Cai, Xuegang Li, Haiyan Wang, Jiaqi Hu, Yipu Song, Dong-Ling Deng, Chang-Ling Zou, Re-Bing Wu, Luyan Sun Machine learning can be substantially powered by a quantum computer owing to its huge Hilbert space and inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed in an end-to-end fashion, i.e., the quantum ansatz is parameterized by directly manipulable control pulses without circuit design and compilation. Such gate-free models are hardware friendly and can fully exploit limited quantum resources. Here, we report the first experimental realization of quantum end-to-end machine learning on a superconducting processor. The trained model can achieve 98% recognition accuracy for two handwritten digits (via two qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National Institute of Standards and Technology) database. The experimental results exhibit the great potential of quantum end-to-end learning for resolving complex real-world tasks when more qubits are available. |
Wednesday, March 22, 2023 12:42PM - 12:54PM |
AAA05.00002: Comparing Generalization Performances of Quantum and Classical Generative Models Mohamed Hibat-Allah, Marta Mauri, Manuel S Rudolph, Alejandro Perdomo-Ortiz Generating novel and high-quality data is the most desirable feature in generative model tasks. This property can be very beneficial to a wide range of applications including molecular discovery and combinatorial optimization. Recently, a well-defined framework [1] has been proposed to quantify generalization for different generative models on an equal footing. In this work, we aim to build on top of these results to compare the generalization performances of quantum and classical generative models. On the quantum side, we use Quantum Circuit Born Machines (QCBMs), which are known for their ability to model complex probability distributions, and which can be implemented on near-term quantum devices. On the classical side, we use different generative models including autoregressive recurrent neural networks, which are known to be universal approximators of sequential data and have promoted significant progress in natural language processing. In our experiments, we choose a synthetic but application-inspired dataset as a test bed [2]. Our results show that by introducing different rules of comparing our generative models, we can obtain different results, that can sometimes yield an advantage of quantum over classical models.
[1] Gili, Mauri, and Perdomo-Ortiz, “Evaluating generalization in classical and quantum generative models,” arXiv:2201.08770.
[2] Gili, Hibat-Allah, Mauri, Ballance, and Perdomo-Ortiz, “Do Quantum Circuit Born Machines Generalize?,” arXiv:2207.13645.
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Wednesday, March 22, 2023 12:54PM - 1:06PM |
AAA05.00003: Enhancing Quantum Adversarial Robustness via Randomized Encodings Weiyuan Gong, Dong Yuan, Weikang Li, Dong-Ling Deng The interplay between quantum physics and machine learning gives rise to an emergent frontier of quantum machine learning, where advanced quantum learning models may outperform their classical counterparts in solving certain challenging problems. However, quantum learning systems are vulnerable to adversarial attacks: adding tiny carefully-crafted perturbations on legitimate input samples can cause misclassifications. Here, we propose a general scheme to protect quantum learning systems from adversarial attacks by randomly encoding legitimate data samples and analytically study the effectiveness of our approaches. We first rigorously prove that both global and local random unitary encoder on any input data leads to exponentially vanishing gradients (i.e. barren plateaus) for adversary variational quantum circuits that add perturbations, regardless of the inner structures of adversarial circuits and classifiers. We apply this random encoding technique to the classification of topological phases of matter and numerically demonstrate the robustness improvement through exponentially vanishing adversarial gradients. Based on the observation that experimental noise is mostly local, we provide an analytical bound on the vulnerability of quantum classifiers under local unitary adversarial attacks. We additionally show that random black-box quantum error correction encoders can protect quantum classifiers against local adversarial noise and the robustness increases as we concatenate error correction codes in fault-tolerant quantum computation. To quantify the robustness, we adapt the concept of quantum differential privacy to measure the stability of the prediction given by a quantum classifier. Our work sparks new connections among concepts and techniques for evaluating and improving the security of quantum learning systems, which will provide valuable guidance for both near-term and future quantum machine learning technologies. |
Wednesday, March 22, 2023 1:06PM - 1:18PM |
AAA05.00004: Benchmarking of quantum generative adversarial networks using NVIDIA’s Quantum Optimized Device Architecture Pooja Rao, Zohim Chandani, Amalee Wilson, Eric Schweitz, Bruno Schmitt, Anthony Santana, Bryce A Lelbach, Alexander McCaskey Quantum generative adversarial models (QGANs) have the potential to vastly improve the training of machine learning models by providing accelerated learning and stronger expressivity compared to classical GANs. In this study, we present our results from benchmarking a GPU accelerated hybrid QGAN with a quantum generator and a classical discriminator using Nvidia’s Quantum-Optimized Device Architecture (QODA). QODA provides a heterogeneous quantum-classical workflow that is ideal for such applications. Its modern C++ based programming model is designed for interoperability with existing classical parallel programming models. |
Wednesday, March 22, 2023 1:18PM - 1:30PM |
AAA05.00005: Experimental quantum adversarial learning with programmable superconducting qubits Weikang Li Quantum computing promises to enhance machine learning and artificial intelligence. Yet, recent theoretical works show that similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from adversarial perturbations as well. Here, we report an experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built upon variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150~$mu$s, and average fidelities of simultaneous single- and two-qubit gates above 99.94\% and 99.4\% respectively, with both real-life images (e.g., medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99\%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would substantially enhance their robustness to such perturbations. |
Wednesday, March 22, 2023 1:30PM - 1:42PM |
AAA05.00006: Renormalisation Through The Lens Of QCNNs Nathan A McMahon, Petr Zapletal, Michael J Hartmann The cluster-Ising model is an example of a quantum model with a symmetry protected topological (SPT) phase. The efficiency of phase recognition has recently been improved over measuring string order parameter (SOP) by the use of a particular quantum convolutional neural network (QCNN) that was motivated by renormalisation theory. |
Wednesday, March 22, 2023 1:42PM - 1:54PM |
AAA05.00007: Dynamical simulation via quantum machine learning with provable generalization Joe Gibbs Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. This provides a guarantee that our algorithm is resource-efficient, both in terms of qubit and data re- quirements. Our numerics exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota. |
Wednesday, March 22, 2023 1:54PM - 2:06PM |
AAA05.00008: Redundant and synergistic coding in a driven nonlinear Bose-Hubbard dimer Krai Cheamsawat, Thiparat Chotibut We study the capability of a small quantum system to encode the temporal information of a classical driving signal (input time series) to explore if and when the whole of the system can encode more information than when each individual unit encodes the input information separately, i.e. synergistic coding. In particular, we theoretically and numerically investigate an information encoding capability of a Bose-Hubbard dimer driven by a time-dependent external field. We found that the growth of quantum correlation, as measured by the entanglement entropy, can lead to higher synergy (lower redundancy) of coding. We also study the classical limit of our model to investigate whether quantum correlation can enhance the information encoding capability. Our work suggests how to harness quantum entanglement to encode time-series data efficiently in a small, physically realizable quantum system. |
Wednesday, March 22, 2023 2:06PM - 2:18PM |
AAA05.00009: Markovian Quantum Neuroevolution for Machine Learning Zhide Lu Neuroevolution, a field that draws inspiration from the evolution of brains in nature, harnesses evolutionary algorithms to construct artificial neural networks. It bears a number of intriguing capabilities that are typically inaccessible to gradient-based approaches, including optimizing neural-network architectures, hyperparameters, and even learning the training rules. In this paper, we introduce a quantum neuroevolution algorithm that autonomously finds near-optimal quantum neural networks for different machine-learning tasks. In particular, we establish a one-to-one mapping between quantum circuits and directed graphs, and reduce the problem of finding the appropriate gate sequences to a task of searching suitable paths in the corresponding graph as a Markovian process. We benchmark the effectiveness of the introduced algorithm through concrete examples including classifications of real-life images and symmetry-protected topological states. Our results showcase the vast potential of neuroevolution algorithms in quantum architecture search, which would boost the exploration towards quantum-learning advantage with noisy intermediate-scale quantum devices. |
Wednesday, March 22, 2023 2:18PM - 2:30PM |
AAA05.00010: Learning Protocols for Quantum Entanglement Generation Noah H Johnson, Jake Navas, M. Jaden Brewer, Manuel Guerrero, Niquo Ceberio, Inès Montaño In addition to an ever-growing list of applications in areas such as cybersecurity, medicine, and science, Machine Learning (ML) algorithms are also increasingly being applied to the field of quantum science, such as, e.g., quantum algorithms, quantum material science, quantum chemistry, quantum optics, and quantum many-body systems. We here investigate the potential of ML algorithms to drive progress in quantum information science, specifically quantum networks. In particular, we study if it is possible for an ML algorithm to self-learn optimal protocols for entanglement generation and distribution. Long-distance entanglement is a key requirement for quantum communication, specifically the realization of a long-distance quantum network (quantum internet). We will discuss the potential of using a projective-simulation-based reinforcement algorithm to identify successful entanglement generation protocols in noisy conditions. |
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