Quantum Computing for Healthcare: A Review

Rasool, Raihan Ur; Ahmad, Hafiz Farooq; Rafique, Wajid; Qayyum, Adnan; Qadir, Junaid; Anwar, Zahid

doi:10.3390/fi15030094

Cited by 79 publications

(18 citation statements)

References 195 publications

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“…Quantum computing offers the prospect of tailoring medical treatments to individual patients through the use of quantum algorithms [15]. By analyzing a patient's genetic and health data with high precision, personalized treatment plans can be developed, optimizing therapeutic outcomes while minimizing side effects.…”

Section: Personalized Medicinementioning

confidence: 99%

The Quantum-Medical Nexus: Understanding the Impact of Quantum Technologies on Healthcare

Shams,

Choudhari,

Reyes

et al. 2023

Cureus

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In a world characterized by rapid technological evolution, the integration of quantum technologies into the realm of healthcare has emerged as a transformative force. This narrative review explores the journey of quantum innovations in medicine, delving into the fundamental principles of quantum mechanics that underpin quantum computing, sensing, and communication. From the birth of quantum theory to the advent of practical quantum applications, we journey through historical milestones that have paved the way for a quantum-powered future in healthcare.The narrative unfolds to reveal the profound implications of quantum technologies in healthcare, ranging from accelerated drug discovery and genomic analysis to secure data transmission and telemedicine. Realworld case studies illuminate successful applications, while the review addresses the ethical, societal, and regulatory considerations that accompany this quantum revolution. As we peer into the future, we contemplate the challenges that lie ahead and offer recommendations for researchers and policymakers to forge a harmonious and equitable synergy between quantum and medicine. In a world where innovation outpaces the tick of the clock, this narrative review serves as a timely guide for those poised to shape the quantum healthcare landscape, where precision and compassion converge and the possibilities are limitless.

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Section: Personalized Medicinementioning

confidence: 99%

The Quantum-Medical Nexus: Understanding the Impact of Quantum Technologies on Healthcare

Shams,

Choudhari,

Reyes

et al. 2023

Cureus

View full text Add to dashboard Cite

show abstract

“…Even though it is not a mature technology, QC has gathered a lot of attention from the scientific community as it offers researchers and practitioners a revolutionary paradigm for tackling different kinds of practical optimization problems 8,9 . Specifically, a large range of problems coming from domains such as healthcare 10 , economics 11 , industry 12 , energy 13 , or logistics 14 have benefited from QC lately.…”

Section: Introductionmentioning

confidence: 99%

Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers

Osaba,

Villar-Rodriguez,

Asla

2024

Preprint

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Research focused on the conjunction between quantum computing and routing problems has been very prolific in recent years. Most of the works revolve around classical problems such as the Traveling Salesman Problem or the Vehicle Routing Problem. Even though working on these problems is valuable, it is also undeniable that their academic-oriented nature falls short of real-world requirements. The main objective of this research is to present a solving method for realistic instances, avoiding problem relaxations or technical shortcuts. Instead, a quantum-classical hybrid solver has been developed, coined Q4RPD, that considers a set of real constraints such as a heterogeneous fleet of vehicles, priority deliveries, and capacities characterized by two values: weight and dimensions of the packages. \Q4RPD resorts to the Leap Constrained Quadratic Model Hybrid Solver of D-Wave. To demonstrate the application of Q4RPD, an experimentation composed of six different instances has been conducted, aiming to serve as illustrative examples.

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“…Moreover, advanced classical AI methods require increased computational power as the amount of genomic and clinical data increases. Nevertheless, classical computers equipped with CPU and GPU face limitations in resolving certain complex health issues, such as early cancer diagnosis, tumor classification, simulation of molecular structures, and drug discovery [49,50]. This motivates the necessity of quantum computing and quantum computing-based AI techniques capable of generating solutions to the aforementioned problems, facilitating precise and detailed early disease discovery.…”

Section: Introductionmentioning

confidence: 99%

Beyond Limits: Charting New Horizons in Glioma Tumor Classification through Hybrid Quantum Computing with The Cancer Genome Atlas (TCGA) Data

Akpinar,

Oduncuoglu

2024

Preprint

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Gliomas, which are the most common malignant primary brain tumors, present significant challenges in terms of varying survival rates, treatment modalities, and prognostic processes between patients with low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification and grading of LGGs and HGGs are crucial for appropriate treatment planning and assessment of overall prognosis. The classification and grading of gliomas have undergone evolution over time, and the inclusion of molecular markers in the classification of glioma tumors by the WHO Central Nervous System (CNS) Tumors Classification in 2016 and the incorporation of advanced molecular diagnostics in 2021 have improved glioma tumor characterization. However, the high cost and limited accessibility of molecular genetic tests, coupled with the time-consuming nature of obtaining results, can lead to delays in critical treatment decisions. To address these challenges, various classical artificial intelligence methods, such as machine learning and deep learning, have been applied to problems in this field, yielding a certain level of success. Nevertheless, the continuous expansion of medical data dimensions, the inherent noise level in the data, and the limitations of the classical vector space pose significant challenges that classical artificial intelligence methods struggle to overcome. Recent studies have demonstrated that the use of quantum computing and quantum artificial intelligence technologies in healthcare not only addresses these problems but also accelerates complex data analyses and processes large datasets more efficiently. This paper presents a novel hybrid quantum (or classical-quantum) computing model aimed at differentiating between LGGs and HGGs using data from The Cancer Genome Atlas (TCGA). To the best of our knowledge, this study is the first to investigate the classification of LGGs and HGGs using a hybrid classical-quantum framework within the TCGA dataset. In the classical part of the study, an ensemble feature selection method was used to identify the most important molecular markers and clinical features within the TCGA glioma dataset. In the quantum section, six variational quantum classifier (VQC) models with different hyperparameters are proposed. These classifiers are subsequently used to differentiate between LGGs and HGGs using the features obtained from the ensemble model. The computational results show that among the six VQC models, the VQC-1 model, which incorporates Rx and CX gates in the feature map and Ry, Rz and CY gates in the parameterized quantum circuit and utilizes the AQCD optimization method, achieves the highest classification accuracy of 0.74. This study provides a novel perspective on the classification of glioma tumors by combining classical and quantum computing methods.

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Quantum Computing for Healthcare: A Review

Cited by 79 publications

References 195 publications

The Quantum-Medical Nexus: Understanding the Impact of Quantum Technologies on Healthcare

The Quantum-Medical Nexus: Understanding the Impact of Quantum Technologies on Healthcare

Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers

Beyond Limits: Charting New Horizons in Glioma Tumor Classification through Hybrid Quantum Computing with The Cancer Genome Atlas (TCGA) Data

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