Who we are
Inspiration-Q is a CSIC spin-off that helps forward-looking and innovative companies by delivering new solutions in optimization, simulation and machine learning based on quantum algorithms that work both in quantum as well as in ordinary computers. We make sure you are not only ready for the quantum future, but that your company or firm enjoys quantum advantage now.
We are a team of quantum scientists and consultants, with ample experience in the design and simulation of quantum hardware and quantum algorithms. Inspiration-Q is created to bring the best of our research into real-world applications, lowering the costs and barrier to quantum computing and quantum-inspired technologies.
Quantum inspired solutions
In designing quantum algorithms for quantum computers, we learn about what makes those algorithms work and provide computational advantages. We transform this knowledge into quantum inspired solutions, that run in ordinary computers with performance or accuracy benefits, proven by state of the art research.
This means in working with us you get the best of both worlds: quantum algorithms for the quantum computers of the now and the future, and quantum inspired solutions that provide immediate benefit in many real-life production scenarios.
Use case: Optimization
We have developed state-of-the-art optimization solutions that combine classical and quantum-inspired accelerations, and applied them to canonical problems, such as portfolio optimization.
The plot on the left illustrates a problem with 100 assets from S&P500, each represented with 4 bits in a Markowitz portfolio optimization problem. The space of solutions contains about 1040 portfolios. A quantum inspired technique finds the frontier of optimal portfolios, with significant improvement in KPI’s such as total return or Sharpe ratios.
Use case: Simulation
Our research also tackles problems in risk analysis, pricing, and engineering problems that demand sophisticated simulations. Our research in this area shows that quantum inspired solutions can provide exponential savings in memory and time [Reference].
These improvements imitate the exponential gains that are obtained when representing those problems in a quantum computer. This is possible because not all hard computational problems exploit all the potential of a quantum computer!
|Dimension||Resolution||Classical algorithm||Quantum inspired|
|2D||16000||2 Gb||0,000009 Gb|
|2D||200k||254 Tb||0,001 Gb|
|3D||2048||64 Gb||0,001 Gb|