Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning

• URL: http://arxiv.org/abs/2309.09424v1
• Date: Mon, 18 Sep 2023 01:49:36 GMT
• Title: Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning
• Authors: Maxwell T. West, Azar C. Nakhl, Jamie Heredge, Floyd M. Creevey, Lloyd C.L. Hollenberg, Martin Sevior, Muhammad Usman
• Abstract summary: We implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation.
• Score: 0.5181797490530444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning (QML) is emerging as an application of quantum computing with the potential to deliver quantum advantage, but its realisation for practical applications remains impeded by challenges. Amongst those, a key barrier is the computationally expensive task of encoding classical data into a quantum state, which could erase any prospective speed-ups over classical algorithms. In this work, we implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Our results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation, obtaining drastic savings in circuit depth and gate count without unduly sacrificing classification accuracy. Additionally, the QML models trained and evaluated on approximately encoded data display an increased robustness to adversarially generated input data perturbations. This partial alleviation of adversarial vulnerability, possible due to the "drowning out" of adversarial perturbations while retaining the meaningful large-scale features of the data, constitutes a considerable benefit for approximate state preparation in addition to lessening the requirements of the quantum hardware. Our results, based on simulations and experiments on IBM quantum devices, highlight a promising pathway for the future implementation of accurate and robust QML models on complex datasets relevant for practical applications, bringing the possibility of NISQ-era QML advantage closer to reality.

Related papers

• Towards Scalable Quantum Key Distribution: A Machine Learning-Based Cascade Protocol Approach [2.363573186878154]
  Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. We propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol.
  arXiv  Detail & Related papers  (2024-09-12T13:40:08Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Classification of the Fashion-MNIST Dataset on a Quantum Computer [0.0]
  Conventional methods for encoding classical data into quantum computers are too costly and limit the scale of feasible experiments on current hardware. We propose an improved variational algorithm that prepares the encoded data using circuits that fit the native gate set and topology of currently available quantum computers. We deploy simple quantum variational classifiers trained on the encoded dataset on a current quantum computer ibmq-kolkata and achieve moderate accuracies.
  arXiv  Detail & Related papers  (2024-03-04T19:01:14Z)
• Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications [1.7041248235270652]
  This survey focuses on selected supervised and unsupervised learning applications executed on quantum hardware. It covers techniques like encoding, ansatz structure, error mitigation, and gradient methods to address these challenges.
  arXiv  Detail & Related papers  (2023-07-03T10:12:34Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• Resource frugal optimizer for quantum machine learning [0.7046417074932257]
  Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. We advocate for simultaneous random sampling over both the datasets as well as the measurement operators that define the loss function.
  arXiv  Detail & Related papers  (2022-11-09T15:29:03Z)
• Potential and limitations of quantum extreme learning machines [55.41644538483948]
  We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
  arXiv  Detail & Related papers  (2022-10-03T09:32:28Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• Concentration of Data Encoding in Parameterized Quantum Circuits [7.534037755267707]
  Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term quantum advantages in meaningful tasks. In this paper, we make progress by considering the common data encoding strategies based on parameterized quantum circuits. We prove that, under reasonable assumptions, the distance between the average encoded state and the maximally mixed state could be explicitly upper-bounded.
  arXiv  Detail & Related papers  (2022-06-16T16:09:40Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.