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Building an all-purpose and fault-tolerant quantum computer pushes the boundaries of research, showing what could one day be possible. However, the end product requires more time, money and equipment than any one company can rightfully justify for results that may take tens of years to demonstrate.

SEEQC’s approach to quantum computing is application-specific; creating bespoke quantum computers is more energy efficient, practical and most importantly scalable. This approach makes meeting the needs of early-adopters a distant reality when compared to all-purpose quantum computers.

Machine Learning / AI

Quantum computing can power machine learning and AI not just by providing faster computation, but by enabling the processing of much more complex structures. This will enable organizations to address applications and problems that may be impossible to solve with classical computing.

Logistics Optimization

Logistics optimization is a notoriously challenging problem to solve and scales very poorly when a large number of tasks or constraints are involved. Because quantum optimization can find better solutions for complex systems with many different possibilities and variables, there is much potential for quantum computing enhanced logistics and supply chain operations.

Quantum Chemistry

Chemical interactions are quantum mechanical by nature. Quantum computing-powered simulation can enable faster and more accurate characterizations of complex molecular systems. This enables analysis of chemicals, and reactions with other chemicals, that could lead to breakthroughs in material and physical sciences. To date, R&D in these sectors remains a primarily iterative and manual labor-intensive process. Using quantum computing would drastically speed the research process while reducing R&D spend.


Just as quantum computing can be applied to physical sciences, so can it be applied to drug discovery and development. Quantum computing enhanced computation can provide more accurate pharmaceutical simulations. This will enable deeper understanding of the potential drug candidates and in silico screening processes, reducing the reliance on costly lab-based pharmaceutical development.


The financial industry with its high volume of data and transactions, its increasing complexity, and its need for high levels of security, has many applications for quantum computing. Financial modeling, for one example, can result in more accurate risk analysis in financial portfolios.