Quantum Computers Promise Faster Designs for Greener Cities and Buildings

A new computational approach utilising superposition, entanglement, and other quantum phenomena offers potential for revolutionising areas such as energy management in buildings and renewable energy planning for cities. Liangzhu Leon Wang at Concordia University in collaboration with University of Central Florida,Syracuse University and Tongji University, and colleagues show how this method addresses complex challenges in the built environment and urban microclimate research. Recognising the current limitations of noisy intermediate-scale quantum (NISQ) hardware, the team proposes the “BITE” principle to focus efforts on problems most amenable to quantum acceleration, enabling more sustainable and climate-resilient urban development.

Quantum annealing accelerates urban energy optimisation via a tailored problem selection principle

A speedup of 2300 states examined simultaneously is now possible using quantum annealing, a capability impossible with classical computation for problems of this scale. Classical computers process information as bits representing 0 or 1, limiting their ability to explore numerous possibilities concurrently. Quantum computers, however, utilise qubits, which, through the principle of superposition, can represent 0, 1, or a combination of both simultaneously. This allows quantum algorithms to explore a vastly larger solution space than their classical counterparts. Previously, the computational time required for optimising energy use across an entire urban neighbourhood with numerous buildings and varying environmental factors was prohibitive, often requiring simplifications that compromised accuracy. By leveraging superposition and entanglement, a phenomenon where two or more qubits become linked and share the same fate, regardless of the distance separating them, the team tackled combinatorial optimisation challenges inherent in urban microclimate research and building performance modelling. These challenges typically involve finding the best solution from a huge number of possible combinations, a task ideally suited to quantum algorithms.

This advancement unlocks the possibility of exploring vast design spaces, allowing for more comprehensive analysis of complex urban systems. Traditional optimisation methods often get trapped in local optima, failing to identify the globally optimal solution. Quantum annealing, a specific type of quantum computation, employs quantum tunneling to escape these local optima and efficiently search for the best possible outcome. Modelling solar photovoltaic system deployment across expansive urban areas requires optimisation of multi-objective functions encompassing power generation, budgetary constraints, and carbon emissions. The complexity arises from the interplay of these factors and the sheer number of potential installation locations and configurations. Binary variables representing installation status and panel tilt angle allow for exploration of a complex design space, where each combination represents a unique deployment scenario. The optimisation process aims to maximise power generation while minimising costs and environmental impact.

This approach was also used to calibrate city-scale urban building energy models, discretising uncertain parameters into finite sets and encoding them with binary variables to minimise discrepancies between simulated and measured energy use across multiple buildings. Accurate urban building energy models are crucial for predicting energy demand and designing effective energy efficiency strategies. However, these models often rely on numerous parameters that are difficult to measure precisely. By treating these uncertain parameters as variables to be optimised, the team improved the model’s accuracy and predictive power. A mid-rise neighbourhood simulation, incorporating ten buildings and a surrounding plaza, utilised both continuous and binary variables to optimise energy use, plaza temperature, and occupant comfort simultaneously. This multi-objective optimisation considered factors such as heating, ventilation, and air conditioning (HVAC) system performance, solar shading, and pedestrian thermal comfort. Quantum computing, which utilises superposition, entanglement, interference, and tunneling, offers potential for addressing complex urban challenges and accelerating renewable energy planning by balancing building performance with climate conditions. Interference, another key quantum principle, allows for the amplification of correct solutions and the suppression of incorrect ones, further enhancing the efficiency of the optimisation process.

Prioritising solvable urban problems unlocks near-term quantum computing advances

A future is envisioned where quantum computers optimise everything from building energy use to city-wide renewable energy grids. However, current quantum processors, while rapidly evolving, remain susceptible to errors and operate at a limited scale, presenting a fundamental tension. Quantum bits (qubits) are inherently fragile and prone to decoherence, losing their quantum properties due to environmental noise. This limits the complexity of computations that can be performed reliably. The “BITE” principle, prioritising problems with broad search spaces but simplified inputs, is a pragmatic response, acknowledging that not every urban challenge is currently suited to quantum acceleration. The acronym “BITE” stands for Broad Input, Tight Output, reflecting the principle’s focus on problems where the initial data is relatively simple, but the number of potential solutions is large.

Algorithms can be tested and refined on near-term hardware by focusing on “BITE” problems, those with large potential solutions but simple initial data. This allows researchers to validate quantum algorithms and demonstrate their potential benefits without being hampered by the limitations of current hardware. This approach doesn’t dismiss the challenges; it prioritises achievable gains within the constraints of today’s technology. Incremental advances will ultimately pave the way for fully realised quantum optimisation of complex urban systems. The development of error correction techniques is crucial for overcoming the limitations of NISQ hardware and enabling more complex quantum computations. Applying quantum computation to urban systems offers a pathway towards tackling increasingly complex city challenges, such as traffic flow optimisation, waste management, and disaster response.

A practical framework is now established to focus efforts, prioritising problems requiring broad exploration but with simplified data and manageable calculations, given the current limitations of Noisy Intermediate-Scale Quantum (NISQ) hardware. The genesis of quantum theory dates back to 1924 with Planck’s hypothesis, and has since undergone significant development, with key contributions from scientists like Schrödinger, Heisenberg, and Dirac. Scientists can refine quantum algorithms and demonstrate potential benefits in areas like building energy management and renewable energy integration by concentrating on suitable problems, building upon the initial successes achieved with the tailored problem selection principle. Further research will focus on developing hybrid quantum-classical algorithms that leverage the strengths of both types of computation, and on exploring new quantum algorithms specifically tailored to urban challenges. The long-term goal is to create a quantum-powered urban planning platform that can optimise city infrastructure and improve the quality of life for urban residents.

Researchers demonstrated the potential of quantum computing for optimising complex urban challenges, such as building energy management and renewable energy planning. This work is important because it proposes a practical approach, prioritising ‘Big, Input-light, Tiny, Evaluative’ problems, to utilise current, limited quantum hardware effectively. By focusing on manageable calculations with simple data, scientists can refine quantum algorithms and validate their benefits. The authors intend to develop hybrid quantum-classical algorithms and explore new quantum solutions tailored to city-specific problems.