Quantum-Enhanced Predictive Genomics Market Outlook

As per our latest research, the global Quantum-Enhanced Predictive Genomics market size in 2024 is valued at USD 1.32 billion, driven by the convergence of quantum computing and advanced genomics analytics. The market is expected to expand at a robust CAGR of 29.4% during the forecast period, reaching an estimated USD 11.88 billion by 2033. This impressive growth trajectory is primarily fueled by the rising demand for precision medicine, the increasing complexity of genomic datasets, and the need for faster, more accurate predictive models in healthcare and life sciences industries.

The rapid growth of the Quantum-Enhanced Predictive Genomics market is underpinned by several transformative factors. Foremost among these is the exponential increase in genomic data generated by next-generation sequencing technologies, which has outpaced the capabilities of classical computing methods. Quantum computing, with its superior processing power and ability to solve complex optimization problems, is revolutionizing how researchers interpret vast genomic datasets. This technological leap is enabling faster identification of genetic markers, accelerating the discovery of disease pathways, and enhancing the predictive accuracy of genomic models. Additionally, the integration of quantum machine learning algorithms is facilitating deeper insights into multifactorial diseases, paving the way for breakthroughs in personalized medicine and targeted therapeutics.

Another significant growth driver is the strategic investments and collaborations between quantum technology providers, pharmaceutical giants, and research institutions. These partnerships are fostering the development of scalable quantum solutions tailored for genomics applications. Governments and private sector stakeholders are allocating substantial funding to quantum research initiatives, recognizing the potential to transform healthcare delivery and disease prevention. Furthermore, the increasing prevalence of chronic and genetic disorders worldwide is amplifying the need for predictive genomics, incentivizing healthcare providers to adopt quantum-enhanced solutions for early diagnosis and risk assessment. This confluence of technological advancement and market demand is creating fertile ground for innovation and commercialization in the Quantum-Enhanced Predictive Genomics sector.

The evolution of regulatory frameworks and data security protocols is also contributing to market expansion. As genomic data becomes increasingly sensitive and valuable, ensuring its confidentiality and integrity is paramount. Quantum cryptography is emerging as a critical solution for safeguarding genomic information against cyber threats and unauthorized access. Regulatory agencies are beginning to establish guidelines for the ethical use of quantum technologies in genomics, which is fostering greater trust among end-users and accelerating adoption. Moreover, the shift towards cloud-based deployment models is democratizing access to quantum-enhanced genomics platforms, enabling smaller research institutes and healthcare providers to participate in this technological revolution.

From a regional perspective, North America currently dominates the Quantum-Enhanced Predictive Genomics market, accounting for the largest share in 2024 due to its advanced healthcare infrastructure, strong presence of quantum technology companies, and proactive government initiatives. Europe follows closely, supported by robust research funding and a collaborative innovation ecosystem. Asia Pacific is poised for the fastest growth, driven by expanding genomics research, increasing investments in quantum computing, and rising healthcare expenditure in countries like China, Japan, and India. Latin America and the Middle East & Africa are gradually emerging as promising markets, propelled by growing awareness and the gradual adoption of precision medicine approaches. Collectively, these regions are shaping a dynamic global landscape for Quantum-Enhanced Predictive Genomics, with significant opportunities for market participants over the next decade.

Technology Analysis

The technology segment of the Quantum-Enhanced Predictive Genomics market is a critical determinant of innovation and competitive advantage. Among the leading technologies, quantum computing stands out for its unparalleled abilit

Quantum-Enhanced Medical Diagnosis Support Market Outlook

According to our latest research, the global Quantum-Enhanced Medical Diagnosis Support market size reached USD 1.27 billion in 2024, with a robust market growth driven by rapid advancements in quantum technologies and their integration into healthcare diagnostics. The market is expected to expand at a CAGR of 24.8% from 2025 to 2033, reaching a projected value of USD 9.73 billion by 2033. This exponential growth is primarily fueled by increasing demand for precision medicine, rising prevalence of complex diseases, and the growing adoption of quantum computing and artificial intelligence in clinical settings.

One of the most significant growth drivers for the Quantum-Enhanced Medical Diagnosis Support market is the escalating need for high-accuracy diagnostic tools capable of handling vast, complex datasets. Traditional diagnostic methods, while effective, often struggle with the sheer volume and intricacy of modern medical data, especially in areas such as genomics, radiology, and oncology. Quantum technologies, particularly quantum machine learning and quantum computing, offer unprecedented computational power, enabling faster and more accurate analysis of heterogeneous medical information. This capability allows for earlier detection of diseases, more precise risk stratification, and the tailoring of treatment strategies to individual patients, significantly improving clinical outcomes and reducing healthcare costs.

Another key factor propelling the market is the increasing investment from both public and private sectors in quantum research and its application to healthcare. Governments across North America, Europe, and Asia Pacific are allocating substantial funding to quantum technology initiatives, recognizing their transformative potential in medical diagnostics. Simultaneously, leading technology firms and healthcare providers are forming strategic collaborations to develop quantum-enhanced solutions for clinical use. These investments are accelerating the translation of quantum innovations from laboratory research to real-world medical applications, fostering an ecosystem that supports rapid commercialization and adoption of these advanced diagnostic tools.

The surging prevalence of chronic and complex diseases, such as cancer, cardiovascular disorders, and neurological conditions, is also contributing to the growth of the Quantum-Enhanced Medical Diagnosis Support market. As the global population ages and the incidence of such diseases increases, healthcare systems are under pressure to deliver faster, more accurate, and cost-effective diagnostic services. Quantum-enhanced technologies, such as quantum imaging and quantum sensing, enable the detection of disease markers at earlier stages and with higher sensitivity than conventional methods. This not only improves prognosis and patient outcomes but also supports the shift towards preventive and personalized medicine, further driving market expansion.

From a regional perspective, North America currently dominates the Quantum-Enhanced Medical Diagnosis Support market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to its advanced healthcare infrastructure, strong presence of leading quantum technology companies, and high levels of R&D investment. However, Asia Pacific is anticipated to exhibit the fastest growth over the forecast period, driven by increasing healthcare expenditure, rapid adoption of advanced technologies, and supportive government initiatives. Europe also remains a key market, benefiting from a robust innovation ecosystem and a focus on collaborative research in quantum healthcare applications.

Technology Analysis

The technology segment of the Quantum-Enhanced Medical Diagnosis Support market encompasses Quantum Machine Learning, Quantum Imaging, Quantum Sensing, and Quantum Computing. Quantum Machine Learning is emerging as a pivotal technology, leveraging quantum algorithms to accelerate the analysis of complex medical datasets. By harnessing the unique properties of quantum bits (qubits), these systems can process and learn from large-scale medical data far more efficiently than classical machine learning models. This capability is particularly valuable in applications such as genomics and radiology, where rapid, high-accuracy pattern recognition can lead to earlier and

The global healthcare quantum computing market is poised for significant growth, projected to reach $61 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 3.7% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing need for faster and more accurate diagnostic assistance is fueling demand for quantum computing's superior processing power. Quantum algorithms can analyze complex medical datasets far more efficiently than classical computers, enabling earlier and more precise disease diagnosis. Secondly, the rise of precision medicine, with its focus on personalized treatments, necessitates advanced computational capabilities to analyze individual patient genomes and tailor therapies accordingly. This intricate analysis is perfectly suited to quantum computing's unique strengths. Finally, advancements in quantum hardware and software are making the technology more accessible and cost-effective, further accelerating market adoption. Hospital systems and research institutions are leading adopters, leveraging quantum computing for drug discovery, clinical trial optimization, and improved patient care. While the market is still in its nascent stages, the potential applications and ongoing technological advancements indicate a promising future for healthcare quantum computing. The market segmentation reveals strong growth prospects across various applications. Diagnostic assistance, leveraging quantum machine learning for image analysis and disease prediction, is expected to dominate the market. Precision medicine, enabling personalized treatments based on genomic data, is another key driver of growth. Geographically, North America, particularly the United States, is expected to hold a significant market share due to substantial investments in research and development and the presence of major players. However, Europe and Asia-Pacific regions are also showing rapid growth potential, driven by increasing healthcare expenditure and a growing focus on technological advancements in healthcare delivery. The competitive landscape includes a mix of established technology giants like IBM and Microsoft, alongside specialized quantum computing companies like D-Wave and Rigetti. This dynamic environment fosters innovation and competition, further accelerating the overall market development.

This dataset presents molecular properties critical for battery electrolyte design, specifically solvation energies, ionization potentials, and electron affinities. The dataset is intended for use in machine learning model testing and algorithm validation. The properties calculated include solvation energies using the COSMO-RS method [1] and ionization potentials and electron affinities using various high-accuracy computational methods as implemented in MOLPRO [2]. Computational details can be found in Ref. [3], with scripts used to generate the data mostly uploaded to our github repository [4].

Molecular Datasets Considered:

• QM9 Dataset: Contains small organic molecules broadly relevant for quantum chemistry [5]

• Electrolyte Genome Project (EGP): Focuses on materials relevant to electrolytes.[6]

• GDB17 and ZINC databases: Offer a broad chemical diversity with potential application in battery technologies. [7, 8]

Data structure

How to Load the Data:

All files can be loaded with

import json

with open("file.json", "r") as f:
data_dict = json.load(f)

and the filestructure can be explored with

data_dict.keys()

Solvation energies

The data is stored in two types of JSON archives: files for full molecules of GDB17 and ZINC and files for amons of GDB17 and ZINC. They are structured differently as amon entries are sorted by the number of heavy atoms in the amon (e.g., all amons with 3 heavy atoms are stored in ni3). Because of the large number of amons with 6 or 7 heavy atoms,they are further split into ni6_1, ni6_2, and so on. A sub dictionary of an amon dictionary or a full molecule dictionary contains the following keys:

ECFP - ECFP4 representation vector

SMILES - SMILES string

SYMBOLS - atomic symbols

COORDS - atomic positions in Angstrom

ATOMIZATION - atomization energy in [kcal/mol]

DIPOLE - dipole moment in Debye

ENERGY - energy in Hartree

SOLVATION - solvation energy in [kcal/mol] for different solvents at 300 K.

Files:

GDB17.json.zip (unpack with unzip first!) - subset of GDB17 random molecules

AMONS_ZINC.json - all amons of ZINC up to 7 heavy atoms

EGP.json - EGP molecules

AMONS_GDB17.json - all amons of GDB17 up to 7 heavy atoms

Atomic energies $E_{at}$ at BP and def2-TZVPD level in Hartree [Ha]

We follow the convention of negative atomization energies for stablity compared to the isolated atoms:

$E_{atomization} = E_{mol} - \sum_{i} E_{at,i}$

Free energy of solvation at 300 K in [kcal/mol]:

Ionization potentials and electron affinities

The upload contains two JSON files, QM9IPEA.json and QM9IPEA_atom_ens.json. QM9IPEA.json summarizes MOLPRO calculation data grouping it along the following dictionary keys:

COORDS - atom coordinates in Angstroms.

SYMBOLS - atom element symbols.

ENERGY - total energies for each charge (0, -1, 1) and method considered.

CPU_TIME - CPU times (in seconds) spent at each step of each part of the calculation.

DISK_USAGE - highest total disk usage in GB.

ATOMIZATION_ENERGY - atomization energy at charge 0.

QM9_ID - ID of the molecule in the QM9 dataset.

All energies are given in Hartrees with NaN indicating the calculation failed to converge. Ionization potentials and electron affinities can be recovered as energy differences between neutral and charged (+1 for ionization potentials, -1 for electron affinities) species.

"CPU_time" entries contain steps corresponding to individual method calculations, as well as steps corresponding to program operation: "INT" (calculating integrals over basis functions relevant for the calculation), "FILE" (dumping intermediate data to restart file), and "RESTART" (importing restart data). The latter two steps appeared since we reused relevant integrals calculated for neutral species in charged species' calculations; we also used restart functionality to use HF density matrix obtained for the neutral species as the initial density matrix guess for the SCF-HF calculation for charged species. NaN CPU time value means the step was not present or that the calculation is invalid. Note that the CPU times were measured while parallelizing on 12 cores and were not adjusted to single-core.

QM9IPEA_atom_ens.json contains atomic energies used to calculate atomization energies in QM9IPEA.json, the dictionary keys are:

SPINS - the spin assigned to elements during calculations of atomic energies.

ENERGY - energies of atoms using different methods.

(Note that H has only one electron and thus does not require a level of theory beyond Hartree-Fock.)

NOTE: Additional calculations were performed between publication of arXiv:2308.11196 and creation of this upload. For the version of the dataset used in the manuscript, please refer to DOI:10.5281/zenodo.8252498.

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957189 (BIG-MAP) and No. 957213 (BATTERY 2030+). O.A.v.L. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772834). O.A.v.L. has received support as the Ed Clark Chair of Advanced Materials and as a Canada CIFAR AI Chair. O.A.v.L. acknowledges that this research is part of the University of Toronto’s Acceleration Consortium, which receives funding from the Canada First Research Excellence Fund (CFREF). Obtaining the presented computational results has been facilitated using the queueing system implemented at https://leruli.com. The project has been supported by the Swedish Research Council (Vetenskapsrådet), and the Swedish National Strategic e-Science program eSSENCE as well as by computing resources from the Swedish National Infrastructure for Computing (SNIC/NAISS).

References

[1] Klamt, A.; Eckert, F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria 2000, 172, 43–72

[2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schutz, M. Molpro: a general-purpose quantum chemistry program package. WIREs Comput. Mol. Sci. 2012, 2, 242–253

[3] arxiv link of draft

[4] https://github.com/chemspacelab/ViennaUppDa

[5] Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014, 1, 140022

[6] Qu, X.; Jain, A.; Rajput, N. N.; Cheng, L.; Zhang, Y.; Ong, S. P.; Brafman, M.; Mag- inn, E.; Curtiss, L. A.; Persson, K. A. The Electrolyte Genome Project: A big data approach in battery materials discovery. Comput. Mater. Sci. 2015, 103, 56–67

[7] Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enu- meration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. Journal of Chemical Information and Modeling 2012, 52, 2864–2875

[8] Irwin, J. J.; Shoichet, B. K. ZINC A Free Database of Commercially Available Compounds for Virtual Screening. Journal of Chemical Information and Modeling 2005, 45, 177–182.