Quantum Computing Inc. (QCI), a quantum computing company, Solve BMW’s 3,854 variable optimization problem.. The company leveraged Entropy Quantum Computing (EQC), a new hardware-based quantum computing solution, to solve the ideal placement of vehicle sensors in BMW’s Vehicle Sensor Placement Challenge (VSPC) 2022. The new quantum system has achieved 70 performances. Double the 2021 entry utilizing the company’s hybrid quantum implementation derived from the quantum computing player D-Wave.
Bob Liscouski, CEO of QCI, said in a press release: “I believe this proves that innovative quantum computing technology can solve real-world business problems today. More importantly, the complexity of the problems solved. This is quantum. It wasn’t just a basic problem to show that the solution would one day be feasible. It’s a very real and serious problem, and the solution is to accelerate the realization of the computing industry. May contribute. “
It shows how quantum computing can be used to solve practical real-world problems where classical computer solutions take an exponentially long time. According to QCI, this demonstrates the advantages of the approach to quantum computing over other quantum systems currently available. All alternatives, such as IBM’s 127 Cubit Eagle Quantum Processing Unit (QPU) and Quantum Brilliance’s diamond-based QPU (already deployed in data center environments), are classified as Noisy Intermediate Scale Quantum (NISQ) systems. QCI states that the demonstration is evidence of achieving quantum advantage, the moment a quantum computer solves a problem not possible with a classical system.
Placing sensors in vehicles, especially self-driving cars, is an incredible challenge. Various variables need to be considered, such as chassis design (affecting vehicle security), no obstacles (different placements have different fields of view, less chance of error), wind resistance, weight balance, etc. I have. To give just a few examples.
This is a problem that requires a lot of trial and error processes that may not provide the best solution and needs to be redone for every new vehicle and every new sensor advancement. This is part of the reason why vehicle designs have remained relatively dull for years-deviations from known solutions add cost, which reduces profits.
Due to the large number of variables and constraints (QCI cites 3,854 variables and 500 constraints imposed on the solution), it is costly to calculate all possible positions for placing sensors in traditional systems. Hit the wall — as an F1 team, calculation time is a costly pursuit.
Even before money is counted, the very realistic cost of computational time in a classic system makes many, many problems (logistics management, step ordering, prioritization, etc.) unsolvable. did.
These are problems that quantum computing with a probabilistic approach to computing can solve in a fraction of the time. QCI solves BMW’s optimization problem in less than 6 minutes, providing the best possible solution to the immediate deployment problem. By doing so, we provided a 15-sensor solution and leveraged QCI’s quantum hardware and software systems to achieve 96% vehicle coverage.
QCI took advantage of a new hardware form of quantum computing to support VSPC. Called entropy quantum computingEliminates the requirements of a near-perfect environment in which qubits operate, significantly reducing design, installation, and operating costs. Entropy refers to the natural evolution of the system and tends to occur towards chaos (or disorder in this case).
Quantum computer deployments are much more feasible if we can escape from noisy environments (environments where temperature, electromagnetic radiation, and other variables allow more coherence in quantum systems).
Coherence is a basic requirement of quantum computers. When the environment changes, the state can change inadvertently, which is costly to calculate and in some cases causes errors that interfere with the calculation.
QCI’s entropy quantum computing approach works by taking into account the environment itself in the computational results. It saves time and money because you don’t have to control all the variables outside the quantum processing unit itself. Instead, the system adapts to changing environments and analyzes the implications of feedback and qubits for quantum states.
Think of how things can be greatly simplified and modern processors dynamically change voltage and frequency depending on the workload, taking into account variables such as power consumption and operating temperature.
The commercial and general feasibility of QCI’s quantum computing solutions is not yet known. It is interesting to note that companies with more resources and history than QCI are choosing other approaches to quantum computing. Others like Microsoft are still chasing their own specific qubits. Each of them praises the benefits of the approach they choose.
It’s less competitive (although there is competition for funding and market share) as it involves exploring different parts of quantum computing. It probably speaks to its complexity, so there are so many possible approaches to take advantage of what is likely to be the next big frontier in computing science.
