A recent report from Novo Holdings reveals that between 2014 and 2025, approximately 70% of the $13.9 billion in private investments in the quantum sector was directed towards equipment and components. However, funding for business applications remains insufficient compared to their potential value.

According to the authors, the next phase of the sector’s development will necessitate investments in specialized algorithms and services, alongside advancements in quantum computers. These solutions should assist clients in selecting molecules for research, planning experiments, and addressing specific tasks.

As of September 2, 2026, the total disclosed private funding for quantum companies reached $4.797 billion, surpassing the entire amount for 2025, which stood at $4.706 billion.

Private funding for quantum companies by sector from 2014 to 2026. Data for 2026 is as of September 2. Source: Novo Holdings.

The Need for More than Just Equipment

Analysts emphasize that the concentration of capital in processors and components was essential for technological advancement. However, the commercial success of these technologies hinges on the problems they can solve.

Novo Holdings believes that companies offering solutions that are difficult to replicate will gain a competitive edge. For application developers, this means not only creating algorithms but also validating calculation accuracy, accumulating proprietary data, and integrating their products into client operations.

A complex software tool alone does not guarantee a sustainable business. For instance, error correction tools and software preparation might become integrated into products offered by equipment manufacturers or cloud platforms, making it harder for independent providers to sell them separately.

Developers of specialized services can build relationships with clients even before powerful quantum systems are available. In the early stages, they can undertake customized scientific projects.

The authors view this model as promising, provided that each project leaves the company with algorithms, data, and tools that can be utilized for future clients.

Enhancing algorithms can also bring the technology closer to practical applications. Reducing the number of required operations would enable certain tasks to be performed on less powerful machines, the researchers noted.

Potential Applications for Quantum Computing

One of the most promising areas identified by Novo Holdings is life sciences, particularly pharmaceuticals. Companies are already investing in molecular modeling and computational chemistry, so a new service could tap into existing budgets if it offers more valuable outcomes.

Assessment of quantum technology prospects across various sectors: applicability, economic impact, and sustainability of competitive advantages. Source: Novo Holdings.

However, the authors do not expect early quantum systems to handle the entire drug development process. Even fault-tolerant machines using error correction for reliable computations will initially have limitations.

"In the early stages, fault-tolerant quantum computers will not model biological processes from start to finish; they will focus on a small but critical part of the workflow where classical approximations fail," the report states.

This involves calculations related to the formation and breaking of chemical bonds or the behavior of electrons in specific areas of a molecule. The remaining tasks in these scenarios will continue to be handled by conventional computers.

Moreover, more accurate calculations should influence client decisions regarding which experiments to conduct or which compounds to investigate further. If the results do not alter these choices, their commercial value remains questionable, analysts emphasized.

Experts suggest that useful applications in materials science, energy, and chemistry may emerge sooner than in pharmaceuticals. Certain tasks related to catalysts and batteries may be better suited to the capabilities of limited-power quantum systems.

The report examines two potential scenarios: the arrival of early fault-tolerant machines in the late 2020s to early 2030s, followed by more scalable systems in the mid-2030s.

Among the risks identified by analysts is the continued advancement of classical computing and AI, which could diminish the advantages of quantum methods in specific tasks before suitable hardware is available. Additionally, pharmaceutical companies and cloud providers may develop the necessary solutions independently.

Notably, in June, Quantum Motion and Nvidia unveiled a molecule modeling approach in which AI assists in data preparation, reducing the demands on quantum hardware.