Analog quantum computing is expected to arrive before digital quantum computing, as it avoids the complexity associated with quantum error correction (QEC), which is essential in digital systems. While digital quantum computing relies on error correction algorithms to manage the effects of noise in qubits—delaying its full development—analog systems, particularly those with Hamiltonian gap protection, are inherently more robust. This will allow analog quantum computing to evolve more quickly. Moreover, since analog systems are especially suited to solving problems in simulation, optimization, and AI applications, there will be significant overlap between these areas and the specific needs of Smart Cities.
Smart Cities, on the other hand, generate large volumes of data from their infrastructure and the activities of those who use them. This data helps determine the state of traffic, energy consumption, available reserves, security, urban services, and more. When modeling these data, we encounter large-scale optimization problems, such as traffic management, intelligent energy distribution, environmental resource allocation, and improving decision-making based on the AI models employed.
In this post, we explore how quantum computing, particularly analog, could transform Smart Cities, tackling critical areas like transportation, energy management, AI-driven decision-making, and environmental sustainability.
Optimizing Transportation Networks and Traffic
Smart Cities face significant challenges in managing traffic flow and urban transportation. Quantum algorithms can provide real-time solutions to reduce wait times at traffic lights, find more efficient routes for vehicles, reduce wait times at traffic lights, and optimize the distribution of public transportation.For example, at Qilimanjaro we’ve helped optimize EV charger locations to maximize availability while minimizing costs.
Energy Management and Smart Distribution
With the increasing use of renewable energy sources, efficient management of energy grids is vital. Quantum computing can forecast energy demand more accurately, optimize resource allocation, and enhance storage capabilities.
► At Qilimanjaro, we’ve worked with several energy companies and implemented, for one specific case, algorithmic improvements that reduced processing time by up to 70%, improving the efficiency of their optimization algorithms.
Enhanced AI for Decision-Making
Smart Cities generate vast amounts of real-time data, and AI models are essential for making decisions. Quantum computing, combined with AI, is expected to enhance the precision and energy efficiency of training models, cutting costs and reducing environmental impact.
► At Qilimanjaro, we’re working on the development of AI models that consume energy more efficiently during their training phase (currently, these energy costs run into the millions and are not sustainable at the pace of growth we’re accustomed to). Likewise, these AI models are expected to be smaller in size while maintaining their accuracy.
► At Qilimanjaro, we are actively involved in the trend of combining quantum computers with classical computers, and we anticipate that, in the future, this will improve the accuracy of predictive models, making AI-based automated decisions, like traffic management or security, not only fast but also efficient.
Resource Management, Environment, and Sustainability
As cities grow, the efficient management of water, waste, and other resources becomes increasingly complex. Quantum systems can help optimize these networks, much like they do for traffic or energy systems, as well as other networks like telecommunications. This optimal management can range from controlling water pipelines to minimizing food waste. A noteworthy aspect from a sustainability perspective is the energy consumption associated with computing itself. Today, supercomputers have energy consumption levels comparable to thousands of homes and are often located near or within cities. The computing required for smart city operations will be anything but insignificant, so choosing a sustainable computing option in a future of limited energy resources will be the preferred choice.
In this regard, Qilimanjaro, along with other quantum computer manufacturers in the sector, observes that the projected consumption of our computers to reach the computing power of current classical computers will remain orders of magnitude below classical consumption levels.
► Read more on our blog about quantum computing and its future role in sustainability.
Urban Infrastructure and Material Science Advancements
Beyond direct applications, quantum computing’s impact on material sciences will indirectly benefit urban infrastructures, leading to improvements like more efficient solar cells and better battery technologies, which will enhance urban energy efficiency.
The integration of quantum computing into Smart Cities promises to solve some of the most pressing challenges, from optimizing urban traffic to improving energy management and decision-making through AI. With Qilimanjaro leading efforts in quantum computing solutions, we are on the verge of a revolution that will make cities smarter, more efficient, and sustainable. As the technology continues to evolve, so will the landscape of urban living.
Quantum Computing: A Path Toward Sustainable Innovation