Robust Quantum Reservoir Computing for Molecular Property Prediction

• URL: http://arxiv.org/abs/2412.06758v1
• Date: Mon, 09 Dec 2024 18:49:18 GMT
• Title: Robust Quantum Reservoir Computing for Molecular Property Prediction
• Authors: Daniel Beaulieu, Milan Kornjaca, Zoran Krunic, Michael Stivaktakis, Thomas Ehmer, Sheng-Tao Wang, Anh Pham,
• Abstract summary: We propose the quantum reservoir computing (QRC) approach to predict the biological activity of potential drug molecules. We observe more robust QRC performance as the size of the dataset decreases. In addition, we leverage the uniform manifold approximation and projection technique to analyze structural changes as classical features are transformed through quantum dynamics.
• Score: 0.5399129278613575
• License:
• Abstract: Machine learning has been increasingly utilized in the field of biomedical research to accelerate the drug discovery process. In recent years, the emergence of quantum computing has been followed by extensive exploration of quantum machine learning algorithms. Quantum variational machine learning algorithms are currently the most prevalent but face issues with trainability due to vanishing gradients. An emerging alternative is the quantum reservoir computing (QRC) approach, in which the quantum algorithm does not require gradient evaluation on quantum hardware. Motivated by the potential advantages of the QRC method, we apply it to predict the biological activity of potential drug molecules based on molecular descriptors. We observe more robust QRC performance as the size of the dataset decreases, compared to standard classical models, a quality of potential interest for pharmaceutical datasets of limited size. In addition, we leverage the uniform manifold approximation and projection technique to analyze structural changes as classical features are transformed through quantum dynamics and find that quantum reservoir embeddings appear to be more interpretable in lower dimensions.

Related papers

• Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
  Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
  arXiv  Detail & Related papers  (2024-11-14T12:27:50Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Empirical Quantum Advantage Analysis of Quantum Kernel in Gene Expression Data [0.0]
  We focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility.
  arXiv  Detail & Related papers  (2024-11-11T15:34:53Z)
• 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)
• Large-scale quantum reservoir learning with an analog quantum computer [45.21335836399935]
  We develop a 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. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning.
  arXiv  Detail & Related papers  (2024-07-02T18:00:00Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• A perspective on the current state-of-the-art of quantum computing for drug discovery applications [43.55994393060723]
  Quantum computing promises to shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms. We provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex.
  arXiv  Detail & Related papers  (2022-06-01T15:05:04Z)
• Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery [8.871042314510788]
  Variational autoencoder is one of the computer-aided design methods which explores the chemical space based on existing molecular dataset. We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction.
  arXiv  Detail & Related papers  (2021-11-15T00:26:19Z)
• Measurements as a roadblock to near-term practical quantum advantage in chemistry: resource analysis [0.0]
  We estimate the number of qubits and number of measurements required to compute the combustion energies of small organic molecules. We consider several key modern improvements to VQE, including low-rank factorizations of the Hamiltonian.
  arXiv  Detail & Related papers  (2020-12-07T19:13:16Z)
• Simulating quantum chemistry in the seniority-zero space on qubit-based quantum computers [0.0]
  We combine the so-called seniority-zero, or paired-electron, approximation of computational quantum chemistry with techniques for simulating molecular chemistry on gate-based quantum computers. We show that using the freed-up quantum resources for increasing the basis set can lead to more accurate results and reductions in the necessary number of quantum computing runs.
  arXiv  Detail & Related papers  (2020-01-31T19:44:37Z)

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.