A Hybrid Quantum Computing Pipeline for Real World Drug Discovery
- URL: http://arxiv.org/abs/2401.03759v3
- Date: Wed, 24 Jul 2024 07:27:24 GMT
- Title: A Hybrid Quantum Computing Pipeline for Real World Drug Discovery
- Authors: Weitang Li, Zhi Yin, Xiaoran Li, Dongqiang Ma, Shuang Yi, Zhenxing Zhang, Chenji Zou, Kunliang Bu, Maochun Dai, Jie Yue, Yuzong Chen, Xiaojin Zhang, Shengyu Zhang,
- Abstract summary: This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design.
Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design.
- Score: 6.944038990445816
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing \rev{a hybrid} quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.
Related papers
- Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries [1.8195318084816288]
The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry.
This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery.
arXiv Detail & Related papers (2024-09-24T01:17:34Z) - End-to-End Quantum Simulation of a Chemical System [2.603151203581752]
We demonstrate the first end-to-end integration of high-performance computing, reliable quantum computing, and AI.
We present a hybrid computation workflow to determine the strongly correlated reaction configurations and estimate, for one such configuration, its active site's ground state energy.
arXiv Detail & Related papers (2024-09-09T17:41:44Z) - Quantum-machine-assisted Drug Discovery: Survey and Perspective [26.938073657909097]
Traditional computer-aided drug design (CADD) has made significant progress in accelerating this process.
By leveraging the inherent capabilities of quantum computing, we might be able to reduce the time and cost associated with bringing new drugs to market.
arXiv Detail & Related papers (2024-08-24T05:38:31Z) - 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) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - 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) - Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
We present an approach where the quantum computation is supplemented by a classical result.
Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative.
In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
arXiv Detail & Related papers (2022-09-12T15:47:44Z) - 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) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - 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.