QCI quantum computer solves BMW’s 3854-variable problem in 6 minutes

QCI quantum computer solves BMW’s 3854-variable problem in 6 minutes

70 times better performance than last year’s proven system.

QCI (Quantum Computing Inc.), a company specializing in quantum computing, announces that it solved a problem with 3,854 BMW variables in just six minutes. This is the EQC solution (Entropy Quantum Computing) who achieved this record time as part of the VSPC (The Challenge of Vehicle Sensor Installation) 2022. According to the company’s statement, its new EQC quantum system delivered 70 times better performance than last year, based on D-Wave’s quantum hybrid process.

Credit: BMW

Challenge: install sensors on a BWM autonomous vehicle with 3,854 variables and more than 500 constraints. A very difficult task due to the multitude of parameters that must be considered (frame design, freedom from constraint, balancing, etc.). In short, this is a problem that requires a lot of trial and error to arrive at an optimal solution; the ideal playground of quantum computing and its computational probabilistic approach.

MIT researchers were able to maintain quantum states for 10 seconds

15 sensors, for 96% coverage.

QCI’s quantum system solved BMW’s optimization problem in less than six minutes. It resulted in an optimal arrangement of 15 sensors providing 96% coverage of the vehicle. In comparison, QCI reports that the NISQ quantum computer (Intermediate Quantum Noise Level) of IBM’s Eagle type QPU can process only 127 variables for a problem of comparable difficulty. The company considers that its performance demonstrates the ability of its quantum system to solve real problems, including in industrial environments.

Bob Liscouski, CEO of QCI, said, “We are very proud to have achieved what we consider to be an important result in the evolution of quantum computing. We believe this proves that innovative quantum technologies can solve real business problems today. What is more important is the complexity of problem to solve. This wasn’t just a simple problem to demonstrate that quantitative solutions would one day be feasible; this was a very big problem whose solution could help the self-driving car industry.”

Source: QCI