Quantum-Secure Database Market Outlook

According to our latest research, the global quantum-secure database market size reached USD 1.24 billion in 2024, reflecting a robust demand for advanced data protection solutions as quantum computing threats loom larger. The market is experiencing a remarkable growth trajectory, registering a CAGR of 32.7% from 2025 to 2033. By 2033, the market is projected to attain a value of USD 17.45 billion. This explosive growth can be attributed to the increasing need for quantum-resistant encryption mechanisms across critical sectors, such as BFSI, healthcare, and government, as organizations race to future-proof their data infrastructure against quantum-enabled cyberattacks.

One of the primary growth drivers for the quantum-secure database market is the accelerating pace of quantum computing advancements. As quantum computers become more powerful, traditional cryptographic methods are rendered increasingly vulnerable, compelling organizations to seek quantum-resistant solutions. The rapid digital transformation across industries, fueled by the proliferation of cloud computing, big data, and IoT, has amplified the urgency to safeguard sensitive information from potential quantum threats. Regulatory bodies and industry watchdogs are also tightening compliance requirements, further pressuring enterprises to adopt quantum-secure databases to ensure the long-term confidentiality, integrity, and availability of mission-critical data.

Another significant factor propelling the quantum-secure database market is the heightened awareness and proactive investment by both public and private sectors. Governments worldwide are launching strategic initiatives and funding programs to accelerate the development and deployment of quantum-safe technologies. This includes not only research and development grants but also pilot projects in sectors like defense, finance, and healthcare, which are highly susceptible to data breaches. The collaboration between academia, technology vendors, and end-users is fostering an ecosystem that promotes innovation and rapid commercialization of quantum-secure database solutions. Furthermore, the increasing frequency and sophistication of cyberattacks underscore the importance of adopting next-generation cryptographic techniques, driving demand for quantum-secure databases.

The quantum-secure database market is also benefiting from the ongoing evolution of enterprise IT architectures. As organizations migrate to hybrid and multi-cloud environments, the complexity of securing data across diverse platforms intensifies. Quantum-secure databases offer a compelling value proposition by enabling seamless integration with existing IT infrastructure while providing robust protection against future quantum threats. The emergence of zero-trust security models and the adoption of advanced authentication mechanisms are further catalyzing the adoption of quantum-resistant solutions. Vendors are focusing on enhancing the scalability, performance, and usability of their offerings, making quantum-secure databases accessible to organizations of all sizes, from SMEs to large enterprises.

Regionally, North America is at the forefront of the quantum-secure database market, driven by substantial investments in quantum technology research and a highly mature cybersecurity landscape. Europe follows closely, bolstered by stringent data protection regulations and a strong emphasis on digital sovereignty. The Asia Pacific region is emerging as a significant growth engine, propelled by rapid digitalization, expanding IT infrastructure, and increasing government support for quantum-safe initiatives. Latin America and the Middle East & Africa are gradually catching up, with growing awareness and adoption of quantum-secure solutions in critical sectors such as banking, government, and telecommunications.

Component Analysis

The quantum-secure database market by component is segmented into software, hardware, and services. The software segment dominates the marke

Equilibrium structures of the tautobase(reference) optimized at the level of theory of popular quantum chemical databases (QM9,PC9 and ANI-E). The structures were generated from the SMILES structures of the original publication and then optimized using Gaussian09. For simplicity, structures are divided on type 'A' and 'B'. The database consists of 1257 pairs (2514 molecules) for each database evaluated. The format of the database is .xyz

The data set contains the data for 20 cannabinoids and cannabinoid-related compounds computed with quantum-chemical methods. This includes the optimized geometries for the conformers and the corresponding rotational constants, vibrational frequencies, and electronic energies.

Quantum-Resistant Database Encryption Market Outlook

According to our latest research, the global quantum-resistant database encryption market size reached USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 38.7% projected through 2033. By the end of 2033, the market is forecasted to achieve a value of USD 18.36 billion, reflecting the surging demand for advanced cryptographic solutions to protect sensitive data against the looming threat of quantum computing. This exceptional growth is primarily driven by the urgent need for next-generation encryption technologies across critical industries, as enterprises and governments worldwide race to future-proof their cybersecurity infrastructure.

The primary growth factor propelling the quantum-resistant database encryption market is the accelerating advancement in quantum computing technologies. As quantum computers edge closer to mainstream adoption, traditional encryption algorithms such as RSA and ECC face obsolescence due to their vulnerability to quantum attacks. This has galvanized organizations across the BFSI, healthcare, and government sectors to proactively transition towards quantum-resistant encryption protocols. Furthermore, regulatory bodies are increasingly mandating the adoption of post-quantum cryptography to safeguard national security interests and ensure compliance with emerging data protection standards. The heightened awareness of quantum threats, combined with the proliferation of mission-critical digital assets, is catalyzing investments in quantum-safe database encryption solutions.

Another significant driver is the rapid digital transformation and cloud migration observed across enterprises of all sizes. As organizations pivot towards hybrid and multi-cloud environments, the risk of data exposure intensifies, necessitating robust encryption mechanisms that can withstand both classical and quantum attacks. Quantum-resistant encryption technologies are gaining traction for their ability to provide long-term data confidentiality in dynamic, distributed computing ecosystems. Additionally, the surge in remote work, IoT adoption, and cross-border data flows is amplifying the need for scalable, future-proof database encryption platforms. Vendors are responding by integrating quantum-safe algorithms into their software and hardware offerings, enabling seamless deployment across diverse IT architectures.

The competitive landscape is further shaped by the increasing frequency and sophistication of cyberattacks targeting enterprise databases. High-profile data breaches and ransomware incidents have underscored the inadequacy of legacy encryption methods, prompting a paradigm shift towards quantum-resistant solutions. Industry leaders are collaborating with academic institutions and cryptographic research organizations to develop and standardize post-quantum encryption algorithms. The market is also witnessing significant government funding and strategic partnerships aimed at accelerating the commercialization of quantum-safe database encryption technologies. As a result, the ecosystem is evolving rapidly, with startups and established players vying for market share through innovation, interoperability, and compliance-driven offerings.

From a regional perspective, North America currently dominates the quantum-resistant database encryption market, accounting for over 42% of global revenue in 2024. This leadership is attributed to the presence of major technology providers, early adoption by financial institutions, and robust government initiatives focused on quantum-safe cybersecurity. Europe follows closely, driven by stringent data privacy regulations and a strong emphasis on digital sovereignty. The Asia Pacific region is emerging as the fastest-growing market, fueled by rapid digitalization, expanding cloud infrastructure, and heightened investments in quantum computing research. Latin America and the Middle East & Africa are also witnessing gradual uptake, supported by increasing awareness and regulatory support for advanced encryption technologies.

Quantum computing papers' reference database acquired from Scopus for a Bibliometric Analysis.

This dataset captures key operational, energy, and security indicators from quantum-inspired data center environments. It reflects diverse workload demands, renewable energy integration, and performance-security trade-offs that are critical for building sustainable and secure infrastructures.

Key Features:

Includes 1000 records with unique identifiers for traceability

Covers workload types such as AI training, cloud storage, IoT processing, and database queries

Provides resource consumption details: compute (TFLOPs), storage (TB), and network demand (Gbps)

Tracks energy consumption, carbon emissions, and renewable energy share

Features scenario strategies: Balanced, Energy-Efficient, Performance-Driven, Carbon-Neutral, Conventional

Contains security attributes, including levels (Low, Medium, High, Quantum-Safe) and PQC enablement

Includes target metrics: Energy Efficiency Index, Service Quality Index, Secure Operations Score, Operational Cost, and Performance Metric

The quantum cryptography market is experiencing robust growth, projected to reach $0.58 billion in 2025 and expand significantly over the forecast period (2025-2033). A compound annual growth rate (CAGR) of 29.19% signifies substantial market expansion driven by increasing concerns regarding cybersecurity vulnerabilities in the face of advancing computing power, including the potential threat of quantum computing to current encryption methods. The rising adoption of cloud-based services and the increasing need for secure data transmission across diverse sectors, such as BFSI (Banking, Financial Services, and Insurance), government and defense, and healthcare, are key drivers. Furthermore, the development of more sophisticated and cost-effective quantum cryptography solutions is fueling market expansion. While the market faces certain restraints, such as the high initial investment costs associated with implementing quantum cryptography infrastructure and the relatively nascent stage of technological development, the long-term security benefits and growing awareness of quantum computing threats are overcoming these challenges. The market is segmented by component (solutions and services), application (network, application, and database security), and end-user (IT & telecommunications, BFSI, government & defense, healthcare, and others). The North American market is expected to hold a significant share, followed by Europe and the Asia-Pacific region, reflecting the high adoption rates of advanced technologies in these regions. The competitive landscape features a mix of established players and emerging startups. Companies like IBM, Toshiba, and Infineon Technologies leverage their expertise in related technologies, while smaller firms focus on developing innovative solutions and specialized components. Strategic partnerships, acquisitions, and technological advancements are key competitive strategies within this dynamic market. The continuous evolution of quantum cryptography technology, along with the expansion of its applications across various sectors, is projected to sustain its high growth trajectory throughout the forecast period. The increasing demand for secure communication channels, driven by stricter data privacy regulations and growing digitalization, will further bolster market growth. We project a steady increase in market value, with a considerable expansion beyond the $0.58 billion mark in 2025 as the technology matures and becomes more accessible. Recent developments include: December 2023: Quantum Xchange collaborated with the National Cybersecurity Center of Excellence (NCCoE) as part of the Migration to Post-Quantum Cryptography Project Consortium. This partnership is done to bring awareness to the issues involved in migrating to the National Institute for Standards and Technology's (NIST's) post-quantum cryptography and building practices to ease in terms of replacing current public-key algorithms with NIST-standardized post-quantum algorithms., December 2023: QNu Labs secured a total of USD 6.5 million in its Pre-Series A1 funding round. This new investment is a strong financial boost, capitalizing on the vision and capabilities at QNu Labs. With these funds, the company is poised to enhance the overall completion of its state-of-the-art quantum technology solutions, further increasing its product suite.. Key drivers for this market are: Rising Number of Cyberattacks, Growing Need for Next Generation Security Solutions for Cloud and IoT Technologies; Evolution of Wireless Network Technologies. Potential restraints include: Rising Number of Cyberattacks, Growing Need for Next Generation Security Solutions for Cloud and IoT Technologies; Evolution of Wireless Network Technologies. Notable trends are: BFSI Sector to Witness Major Growth.

Includes the circuits that were executed, the samples that were collected, the corrections predicted by several decoders, as well as other intermediate files. See the README.txt file at the root of the ZIP archive for a more detailed overview.

This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database. Here, we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output file data stored as a dictionary. This dataset is published as part of our publication: A Quantum-Chemical Bonding Database for Solid-State Materials. Details about the data generation, validation, and metadata description can be found in our publication. Additionally, all the scripts and tools used for curating (including metadata description), benchmarking, and reusing our data are documented in the openly accessible repository. This enables one to reproduce the data and results presented in our work fully. These scripts can be accessed from either of the following links: Zenodo: https://doi.org/10.5281/zenodo.8172527 Github: https://github.com/naik-aakash/lobster-database-paper-analysis-scripts/ (v1.0.6) The dataset will also be available through The Materials Project soon.

Context

Pitt Quantum Repository (PQR) is an online database of molecular properties predicted from quantum mechanics with integrated capabilities for molecular visualization and data sharing. Based on the number of molecules, PQR is currently the largest open database of molecular quantum calculations. PQR uses the PM6 semiempirical quantum chemical model as implemented in the MOPAC program package.

Content

PQR features geometries, heats of formation, dipole moments, orbital energies, and molecular point groups. Specifically, it includes 106,066 molecules with the following 20 items: 1. "ark" - database file path, typically "ark:/c7614/..." 2. "cas" - CAS Registry Number(s) for molecule, if available. Otherwise "null" 3. "chemspider_id" - ChemSpider ID number for molecule. 4. "doi" - Digital Object Identifier for source. 5. "elements" - The combination of periodic table elements making up the molecule. 6. "exact mass" - mass calulated from a molecular formula using known masses of specific isotopes in units of grams/mol 7. "formula" - chemical formula 8. "inchi" - IUPAC International Chemical Identifier 9. "inchikey" - IUPAC International Chemical Identifier key with SHA-256 10. "iupac_name" - IUPAC name 11. "molecular mass" - mass of the molecule in units of grams/mol (most commonly used) 12. "name" - common housheold name 13. "pm7" - contains the following chemical properties from semiempirical quantum chemical model { }: "alpha" - Values of alpha. "dipole" - Value of dipole. "dipoleMoment" - Measure of electric polarity of a system of charges. Units of D (debye). "heatOfFormation" - change of enthaly from the formation of 1 mol of a compount from its elements at 1 atm. Units of kJ/mol. "homo" - Highest occupied molecular orbital energy. Units of eV. "lumo" - Lowest unoccupied molecular orbital energy. Units of eV. "orbitals" - Valued of molecular orbitals. Units of eV. "polarizability" - Value of polarizability in the molecule. "rotational" - Value of rotational in molecule. "surfaceArea" - Total surface area of the molecule. Units of angstrom squared. "volume" -Volume of the molecule. Units of angstrom cubed. 14. "pointGroup" - Group of geometric symmetries that keep one point fixed. 15. "pubchem_cid" - PubChem identifier 16. "shortdoi" - short version of Digital Object Identifier. 17. "smiles" - Simplified Molecular Input Line Entry System for showing chemical structure. 18. "synonyms" - synonyms for the molecule 19. "tags" - tags of the moleculer which includes features, classifiaction of molecules, or organic chemistry categories/groups. 20. "wiki" - Wikipedia entry (if any), otherwise simply empty.

Acknowledgements

The PQR database is made available under the Open Database License.

As a summary, everyone is free to: To Share: To copy, distribute and use the database. To Create: To produce works from the database. To Adapt: To modify, transform and build upon the database. But is required to: Attribute: You must attribute any public use of the database, or works produced from the database. Share-Alike: If you publicly use any adapted version of this database, or works produced from an adapted database, you must also offer that adapted database under the ODbL. Keep open: If you redistribute the database, or an adapted version of it, then you may use technological measures that restrict the work (such as DRM) as long as you also redistribute a version without such measures. Basically, we want you to adhere to the basics of scientific practice. You can, of course, use data from the contents in manuscripts, papers, websites, software, etc. Please make sure to cite the relevant DOIs on each page and the PQR itself doi: 10.17614/Q46M33R18.

Copyright © 2016 University of Pittsburgh Department of Chemistry

Inspiration

"Chemistry, unlike other sciences, sprang originally from delusions and superstitions, and was at its commencement exactly on a par with magic and astrology." Thomas Thomson, 1773-1852

This dataset is published as part of our publication: A Quantum-Chemical Bonding Database for Solid-State Materials Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)

This dataset contains a portion of MNISQ: a dataset of quantum circuits that encode data from MNIST, Fashion-MNIST, and Kuzushiji-MNIST.

This dataset contains Hamiltonian information, molecular data, VQE data, and tapering data for the O2 Molecule using the STO-3G basis set at various bondlengths.

This dataset contains Hamiltonian information, molecular data, VQE data, and tapering data for the H2O Molecule using the STO-3G basis set at various bondlengths.

Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device. Each sample data is stored as a 100 x 100-pixel map from plunger gate voltages to (i) current through the device at infinitesimal bias, (ii) output of the charge sensor evaluated as the Coulomb potential at the sensor location - the experimentally relevant parameters that can be measured, (iii) information about the number of charges on each dot (with a default value 0 for short circuit and a barrier), and (iv) a label determining the state of the device, distinguishing between a single dot, a double dot, a short circuit, and a barrier state. The expanded dataset, QFlow 2.0, consists of 1599 idealized simulated measurements stored as roughly 250 x 250-pixel maps from plunger gate voltages to (i) output of the charge sensor, (ii) net charge on each dot, and (iii) a label determining the state of the device, distinguishing between a left, central, and right single QD, a double QD, and a barrier or short circuit (no QD) state. In addition, the QFlow 2.0 dataset includes two sets of noisy simulated measurements, one with the noise level varied around 1.5 times the optimized noise level and the other one with the noise level ranging from 0 to 7 times the optimized noise level. See the "Project description" and "Data structure" documents for additional information about these datasets. Acknowledgments: This research is sponsored in part by the Army Research Office (ARO), through Grant No. W911NF-17-1-0274. The development and maintenance of the growth facilities used for fabricating samples were supported by the Department of Energy, through Grant No. DE-FG02-03ER46028. We acknowledge the use of clean room facilities supported by The National Science Foundation (NSF) through the UW-Madison MRSEC (DMR-1720415) and electron beam lithography equipment acquired with the support of the NSF MRI program (DMR-1625348). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ARO or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

Quantum-Resistant Data Lake Market Outlook

According to our latest research, the quantum-resistant data lake market size reached USD 1.47 billion globally in 2024, and it is expected to grow at a robust CAGR of 32.8% from 2025 to 2033. Driven by the accelerating threat of quantum computing to classical cryptography and the rising adoption of data lakes for enterprise-scale analytics, the market is forecasted to reach USD 19.09 billion by 2033. The exponential growth is primarily attributed to the urgent need for advanced cryptographic solutions that can safeguard massive, centralized repositories of sensitive data against future quantum attacks, as well as the increasing regulatory pressure on data privacy and security across critical sectors.

The primary growth factor underpinning the expansion of the quantum-resistant data lake market is the escalating awareness and preparedness among enterprises and governments regarding the risks posed by quantum computing to conventional data encryption methods. As quantum computers inch closer to practical viability, their potential to break widely used cryptographic algorithms has become a significant concern for organizations storing vast amounts of sensitive data in centralized data lakes. This has led to a surge in demand for post-quantum cryptography (PQC) solutions and quantum-resistant architectures, prompting organizations to invest heavily in upgrading their data lake infrastructures. The integration of quantum-resistant algorithms ensures that data remains secure both in transit and at rest, even against the advanced computational capabilities of future quantum adversaries, making it a critical investment for organizations prioritizing long-term data security.

Another key driver fueling the growth of the quantum-resistant data lake market is the rapid digital transformation across industries such as BFSI, healthcare, government, and retail. As organizations increasingly rely on data lakes to aggregate, store, and analyze ever-growing volumes of structured and unstructured data, the security and integrity of these repositories have become paramount. Regulatory frameworks such as GDPR, HIPAA, and emerging quantum-safe mandates are compelling organizations to adopt next-generation data protection measures. The convergence of big data analytics with quantum-resistant security protocols is not only helping organizations achieve compliance but also enabling them to maintain customer trust and competitive advantage in an era of heightened cyber threats. The growing ecosystem of quantum-safe technology providers, coupled with heightened investments in R&D, is further accelerating the pace of innovation and adoption in this market.

The market’s growth is also being propelled by technological advancements in quantum-resistant encryption, key management, and secure data sharing mechanisms tailored for large-scale data lake environments. Leading vendors are focusing on developing scalable, high-performance software and hardware solutions that can seamlessly integrate with existing data lake architectures, whether on-premises or in the cloud. Furthermore, strategic collaborations between technology providers, research institutions, and government agencies are fostering the development of industry standards and best practices for quantum-safe data management. As a result, organizations are increasingly able to future-proof their data lakes against evolving threats, ensuring business continuity and resilience in the face of quantum-driven disruptions.

Regionally, North America is at the forefront of the quantum-resistant data lake market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s dominance can be attributed to the presence of leading technology companies, robust cybersecurity regulations, and significant investments in quantum research and development. Meanwhile, Asia Pacific is projected to exhibit the fastest growth over the forecast period, driven by the rapid digitalization of economies, expanding cloud adoption, and increasing government initiatives to bolster quantum-safe infrastructure. Europe’s strong regulatory landscape and focus on data privacy are also contributing to the market’s growth, while emerging markets in Latin America and the Middle East & Africa are gradually catching up as awareness and investments in quantum-resistant technologies gain momentum.

This dataset is published as part of our publication: A Quantum-Chemical Bonding Database for Solid-State Materials. Details about the data generation, validation, and metadata description can be found in our publication. Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)

This dataset contains Hamiltonian information, molecular data, VQE data, and tapering data for the CH4 Molecule using the STO-3G basis set at various bondlengths.

This dataset is a synthetic representation of a quantum decision-making scenario, designed to explore how quantum states, environmental variables, and decision-making processes might interact in a theoretical or simulated environment. It is not derived from real-world data but rather generated to reflect possible outcomes in a controlled, experimental context.

Dataset Overview:

Q-State: Represents a simplified quantum state as a pair of floating-point numbers. In real quantum systems, these states are more complex and described in higher-dimensional spaces. Temperature: Simulated temperature values, possibly influencing the decision-making environment. Pressure: Simulated pressure values within the environment. External Observers: Number of simulated observers that might influence the outcome due to the quantum measurement problem. Entanglement Level: A floating-point number representing the degree of quantum entanglement between entities in this simulated environment. Probability of Outcome: The likelihood of a specific decision outcome occurring, based on the simulated quantum state and environmental factors. Decision Superposition: A binary variable indicating whether the decision state is in superposition (0 or 1). Outcome: The final decision outcome, categorized as "Yes" or "No."

Intended Use:

This dataset is ideal for use in the following types of projects and research:

Theoretical Quantum Computing Research: Exploring how quantum states might influence decision-making processes in a theoretical or simulated quantum environment. Machine Learning Algorithm Development: Testing and developing algorithms that can handle quantum-inspired data or decision-making processes, especially in fields like quantum machine learning, quantum-inspired neural networks, and other hybrid classical-quantum models. Educational Purposes: Teaching concepts related to quantum computing, quantum decision theory, and the intersection of quantum mechanics with machine learning. Proof-of-Concept Projects: Demonstrating the application of machine learning models on synthetic quantum data to explore potential future applications of quantum computing in decision-making. Limitations:

This dataset should not be used in the following contexts:

Real-World Decision-Making Models: Since the data is synthetic, it doesn't reflect actual real-world scenarios or outcomes and should not be used for building models intended for deployment in practical decision-making systems. Empirical Research: The dataset is not suitable for empirical research that requires real-world data, as it does not contain actual measurements or observations. General Machine Learning Projects: If the goal is to train models on data that reflects real-world patterns, this dataset may not provide the necessary grounding in reality, given its synthetic and theoretical nature.

This Quantum Decision-Making Dataset offers a unique opportunity to explore quantum-inspired decision-making in a controlled, synthetic environment. While it is not grounded in real-world data, it can be a valuable resource for theoretical research, educational purposes, and the development of quantum-inspired algorithms. Users should be mindful of its limitations and avoid applying it in contexts where real-world data is required.

Japan Exports Quantum Index: Chemicals data was reported at 135.990 2000=100 in Sep 2008. This records a decrease from the previous number of 146.770 2000=100 for Aug 2008. Japan Exports Quantum Index: Chemicals data is updated monthly, averaging 113.390 2000=100 from Jan 1998 (Median) to Sep 2008, with 129 observations. The data reached an all-time high of 163.650 2000=100 in Jun 2008 and a record low of 72.010 2000=100 in Jan 1998. Japan Exports Quantum Index: Chemicals data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA093: Exports Quantum Index: 2000=100. Rebased from 2000=100 to 2005=100. Replacement Series ID: 199124702