Secure storage and secondary use of individual human genome data is increasingly important for genome research and personalized medicine. Currently, it is necessary to store the whole genome sequencing information (FASTQ data), which enables detections of de novo mutations and structural variations in the analysis of hereditary diseases and cancer. Furthermore, bioinformatics tools to analyze FASTQ data are frequently updated to improve the precision and recall of detected variants. However, existing secure secondary use of data, such as multi-party computation or homomorphic encryption, can handle only a limited algorithms and usually requires huge computational resources. Here, we developed a high-performance one-stop system for large-scale genome data analysis with secure secondary use of the data by the data owner and multiple users with different levels of data access control. Our quantum secure cloud system is a distributed secure genomic data analysis system (DSGD) with a "trusted server" built on a quantum secure cloud, the information-theoretically secure Tokyo QKD Network. The trusted server will be capable of deploying and running a variety of sequencing analysis hardware, such as GPUs and FPGAs, as well as CPU-based software. We demonstrated that DSGD achieved comparable throughput with and without encryption on the trusted server Therefore, our system is ready to be installed at research institutes and hospitals that make diagnoses based on whole genome sequencing on a daily basis.
Secure storage and secondary use of individual human genome data is increasingly important for genome research and personalized medicine. Currently, it is necessary to store the whole genome sequencing information (FASTQ data), which enables detections of de novo mutations and structural variations in the analysis of hereditary diseases and cancer. Furthermore, bioinformatics tools to analyze FASTQ data are frequently updated to improve the precision and recall of detected variants. However, existing secure secondary use of data, such as multi-party computation or homomorphic encryption, can handle only a limited algorithms and usually requires huge computational resources. Here, we developed a high-performance one-stop system for large-scale genome data analysis with secure secondary use of the data by the data owner and multiple users with different levels of data access control. Our quantum secure cloud system is a distributed secure genomic data analysis system (DSGD) with a "trusted server" built on a quantum secure cloud, the information-theoretically secure Tokyo QKD Network. The trusted server will be capable of deploying and running a variety of sequencing analysis hardware, such as GPUs and FPGAs, as well as CPU-based software. We demonstrated that DSGD achieved comparable throughput with and without encryption on the trusted server Therefore, our system is ready to be installed at research institutes and hospitals that make diagnoses based on whole genome sequencing on a daily basis.
Quantum key distribution (QKD) is a technology for distributing cryptographic keys between two communication parties based on the quantum physics for the secure communication. A trusted nodebased key relay technique is integrated with QKD to overcome the technical limitations of QKD and to enable key distribution between two arbitrary communication parties in the trusted node network, which we refer to as the QKD network. In addition, secret sharing technology has been integrated with the QKD network to realize secure data storage and secure data communication, which we call a quantum secure cloud. This paper presents the development and evaluation of the proof-of-concept (PoC) system for the QKD network and quantum secure cloud, specially applied to the genome medicine domain. The PoC system was developed at Tohoku University and Toshiba sites to address the “cancer clinical sequencing” use-case. We evaluated three practical scenarios with the PoC system: (1) real-time transmission of genome analysis data, (2) “expert panel”, an online video conference for medical experts' discussion, and (3) distributed backup of genome analysis data. To support these scenarios, we developed three new functions: (1) a function for monitoring output data and pipeline processing of data encryption/decryption and transmission for secure large-scale data transfer, (2) a key management system function to achieve both large-scale data transmission and low-latency data communication, and (3) a function for preemptive key data reading and direct access to storage devices to enable high-speed data transmission and distributed data backup using a secret sharing scheme. These scenarios and functions were evaluated and demonstrated using real or simulated genome data. The evaluation results reveal that QKD network and quantum secure cloud technologies can be applied to cancer clinical sequencing as a use-case of the genome medicine domain.
High-quality processing and interpretation of large-scale genomic datasets in the high dimensionality required to reach treatment determinations within precision medicine hinges on discovered computational approaches, but computational approaches are often unsuccessful in processing information at this level, due to difficulties in processing high volumes of data coupled with noise and high dimensionality, usually becoming computationally slow and inconclusive throughout the process. We propose a new framework incorporating quantum computing to enable and enhance engagement with genomic data throughout clinically relevant scenarios. With the incorporation of existing quantum algorithms that facilitate dimensionality reduction, clustering, and classification, or proposed frameworks may improve disease biomarker detection and patient stratification capabilities. Our framework will incorporate a variety of quantum encoding methods and hybrid designs to minimize the classical limitations on these datasets, leading to a rapid clarification of the ontogeny of genomic datasets, and thus additionally providing the user with more clinically relevant data to interpret. Our results presented a significant increase in processing times and plausibility to clinical relevance, opening trajectories toward real-time, scalable, genomic diagnostics, providing a clear path toward confidence to broaden practice using quantum computing throughout precision medicine with genomic datasets as a basis for patient treatment.
The emergence of quantum computing threatens classical cryptographic systems, prompting the development of quantum-resistant solutions. At the same time, blockchain technology has been recognized for its capacity to provide immutable, auditable records, but existing implementations largely rely on cryptographic primitives vulnerable to quantum attacks. In genomic research, the integrity, provenance, and confidentiality of sequence data are paramount: any undetected tampering can compromise downstream analyses, misguide clinical decisions, or violate patient trust. This paper introduces Quantum Blockchain for Verifiable Genome Mapping Systems (QB-VGMS), a hybrid architecture that melds quantum key distribution (QKD)–based secure channels with a permissioned, quantum-resistant blockchain to enable end-to-end verifiability of genome mapping operations. In our design, genome fragments are hashed using SHA-3 and stored on-chain alongside metadata, while bulk sequence data resides in encrypted off-chain repositories. The consensus protocol employs QKD to distribute fresh symmetric keys among validator nodes, coupled with a quantum-random leader election to resist both classical and quantum adversaries. We describe a prototype implementation using simulated QKD links and Hyperledger Fabric extended for quantum security. Performance evaluation on human chromosome 21 datasets (48 MB total, partitioned into 1 MB fragments) demonstrates sub-one-second average transaction latency and throughput scaling up to 15 fragments per second under concurrent load. Security assessments confirm tamper-evident auditing and confidentiality against unauthorized decryption attempts. We conclude by discussing practical deployment challenges—such as QKD hardware integration, consensus scalability, and regulatory compliance—and outline pathways for integrating advanced privacy-enhancing technologies.
The explosive growth of genomic information calls for computational methods to handle complex sequence analysis and genome assembly assays. Based on the principles of superposition and entanglement, quantum computing represents an another way of computation to achieve potentially faster and more efficient operations than classical systems. This article offers a comparative overview of 4 major studies dedicated to the use of QC in genomics, spanning theoretical algorithmical conceptualization, systematic literature mapping, analyses on practical challenges in QC-aided genomics and hybrid Q-C designs. The review indicates that there is potential for significant speed-up based on theoretical formulation, while current applications are limited by hardware resource constraint, data-loading latency and scalability. Hybrid models, in particular those that hybridize quantum annealing with classical optimization, seem to offer the most practicable route to near-term genomic computing. The analysis also shines a light on the increasingly pivotal positions that quantum-inspired approaches and hybrid orchestration systems are playing as crucial stepping stones towards full-quantum advantage. The results suggest that quantum computing is in its infancy as far as genomic analysis is concerned but the potential seems to be promising for future bioinformatics research and applications. Such opportunities are only accessible with further advancements in quantum hardware, data encoding and benchmark development.
Abstract This study presents a Federated Quantum Variational Quantum Classifier (FedVQC) framework designed to address critical challenges in healthcare data privacy and computational efficiency, particularly in the domain of genomic analysis. Conventional machine learning approaches in healthcare often face obstacles stemming from privacy concerns, regulatory constraints, and the fragmented nature of medical data across different institutions. The proposed FedVQC framework leverages the principles of federated learning and quantum machine learning to enable secure, decentralized training of predictive models without requiring the sharing of sensitive patient data. Extensive experimentation on multiple genomic datasets reveals a strong correlation between the number of participating institutions and the model's accuracy, with performance consistently improving as more clients contribute to the training process. The framework demonstrates high accuracy levels, ranging from 70% to 96%, across diverse datasets, highlighting its robustness and applicability in healthcare analytics. By harnessing the computational power of quantum computing and the collaborative nature of federated learning, the FedVQC framework provides a scalable and secure solution for genomic research. This study underscores the potential of quantum federated learning in advancing privacy-preserving precision medicine, large-scale genomic collaboration, and broader applications in bioinformatics, drug discovery, and personalized healthcare analytics
Background and Aims: Rising quantum hazards and flaws in conventional encryption make cloud-based healthcare data security harder. Quantum-Secure HealthChain, a new architecture using blockchain and quantum computing, improves medical data security, patient privacy, and data fidelity. Methods: To prevent quantum attacks, the proposed system uses Quantum Key Distribution (QKD) for safe cryptographic key exchange and quantum-resistant encryption. Blockchain technology secures medical records, while multi-layered encryption ensures data privacy. Quantum Biometric Authentication improves access control using quantum entanglement and biometric data. Key generation, encryption, blockchain storage, authentication, and decryption are system process steps. Experimental evaluation focuses on encryption speed, resource economy, throughput, and scalability using simulated healthcare data. Results: Experimental data demonstrate system strength and efficiency. Encryption and decryption perform consistently for 1 to 100 MB data sizes with negligible overhead. Throughput can reach 105 transactions per second under normal demand; CPU (82%) and memory (210 MB) utilization are low. Scalability studies show linear expansion lets the system handle increased data volumes and user demands without sacrificing performance. Security study confirms quantum attack, data corruption, and unauthorized access resistance. Conclusion: Quantum-Secure HealthChain offers a revolutionary method to cloud-based healthcare system security. Blockchain-quantum computing integration ensures strong authentication, safe key exchange, and quantum-resistant encryption. Its security, scalability, and efficiency make it a future-ready platform for safe medical data management, reducing quantum computing hazards.