Quantum Innovative Computing Kit

Our mission: Bringing Quantum Computing to All

We translate classical algorithm to quantum and chose the best hardware to run it.


Real-time market simulations and fraud-detection.

Quick decisions in finance are worth gold, but it may take days for classical computers to conduct simulations of global markets, rendering most of them of little use.

Quantum computers, on the other hand, offer real-time simulations, enabling fast and precise pricing of derivatives and risk management.

Quantum computing also allows more precise fraud-detection in a shorter amount of time, saving money and reducing customer friction.


Faster and safer drug discovery with less cost.

Discovering new drugs is an expensive and time-consuming effort [2], which can be made more efficient with simulations.

It turns out that simulations of large molecules, which make up most pharmaceutical drugs, are a limited or even impossible function for classical computers.

This is no problem for quantum computers, which can simulate even the largest molecules in fine detail, paving the way for faster and safer drug discovery and considerably lowering its cost.

New Materials

Materials tailored to your needs.

Sometimes we need to study a new material’s mechanical properties or its electric and heat conductivities. One way, of course, is to build the material and perform measurements, but another is through simulations. Again, classical computers cannot tackle the majority of practical calculations in feasible times [3]. Quantum computers overcome this difficulty with flying colors. The reason for this is simple: quantum computers follow the laws of quantum mechanics, just as the material’s properties do.

Quantum computers will help us build improved batteries and find higher temperature superconductors. Both of these are revolutionary by themselves. These developments, in turn, will help humanity reduce its environmental footprint.


For quantum computers, these problems are no problem.

Imagine you are a truck driver starting your shift with many deliveries ahead. Knowing the shortest path while delivering all the parcels will save you both time and fuel, not to mention the reduced CO2 emissions. If you have only five deliveries on your schedule, there are 120 possible routes. A classical computer could help you find the shortest one quickly.

If you have 20 deliveries, however, there are 2,432,902,008,176,640,000 different routes, or about as many grains of sand there are [4], and a classical computer might stumble to find the shortest.

Quantum computers have no need for analyzing each instance separately. They test all of them simultaneously and provide you with the answer quickly.

Portfolio Optimization

Fast optimization no matter the size of your portfolio.

A similar case is finding a company’s portfolio that maximizes return-on-investment or that minimizes risk. Again, trying many possibilities simultaneously is what quantum computers are built for.

Quantum annealers, powerful yet non-universal quantum computers, are specially suited for these tasks. These devices allow optimizing investment portfolios according to the risk margin. Recent studies predicted up to 60% annual return-on-investment, which is an amazing mark [5].

You bring your needs. The quantum is on us.

These are only a few applications of quantum computing.
Many others are coming, and ColibrITD wants to make them available to everyone.
We believe every company should profit from powerful quantum computing tools.

[1] https://spectrum.ieee.org/tech-talk/computing/hardware/how-much-power-will-quantum-computing-need « NVIDIA Tesla GPUs Power World’s Fastest Supercomputer » (Press release). Nvidia. 29 October 2010.
[2] Mullin, Rick (24 November 2014). « Cost to Develop New Pharmaceutical Drug Now Exceeds $2.5B ». Scientific American. Retrieved 6 March 2017.
[3] Current classical supercomputers can only simulate up to 61 qubits:
[4] https://text.npr.org/161096233
[5] Hybrid Quantum Investment Optimization with Minimal Holding Period. S. Mugel et al. https://arxiv.org/abs/2012.01091