Quantum Computing for Smart Cities: Algorithms, Architectures, and Applications for Urban Optimization

RAMAKRISHNA REDDY BIJJAM; Bollavaram Kiran Babu

doi:10.36948/ijfmr.2026.v08i02.73986

Quantum Computing for Smart Cities: Algorithms, Architectures, and Applications for Urban Optimization

Author(s)	Mr. RAMAKRISHNA REDDY BIJJAM, Mr. Bollavaram Kiran Babu
Country	India
Abstract	Managing contemporary smart cities demands computational capabilities that far exceed what conventional processors can deliver. Traffic signal coordination across hundreds of intersections, real-time balancing of renewable energy grids, and cryptographically securing millions of IoT endpoints each constitute combinatorially hard problems that classical algorithms cannot resolve at urban scale within practical time budgets. This paper addresses these limitations by developing and evaluating a hybrid quantum-classical architecture designed for practical deployment in mid-sized smart cities. Three core technical modules are proposed and validated: a Quantum Approximate Optimization Algorithm (QAOA) module for adaptive traffic signal control, a quantum machine learning (QML) module for short-term renewable energy forecasting, and a multi-node Quantum Key Distribution (QKD) architecture for quantum-resilient IoT security. Simulation experiments conducted on IBM Qiskit using realistic urban datasets from Bangalore, India, demonstrate that QAOA-based signal optimization reduces average intersection wait times by 47–53% compared to fixed-timing baselines and executes 4–5× faster than genetic algorithm solvers on equivalent instances. The QML forecasting model achieves 8–12% lower RMSE than classical LSTM networks on solar generation prediction tasks. The QKD-ECC hybrid security framework sustains a 99.9% device authentication success rate while remaining quantum-safe under post-quantum threat models. A phased deployment roadmap tailored for Indian smart cities is provided, alongside a frank assessment of current hardware constraints and practical mitigation strategies. The findings contribute a unified integration blueprint, quantified performance benchmarks on urban data, and evidence-based guidance for cities beginning the transition toward quantum-enhanced infrastructure.
Keywords	quantum computing, smart cities, QAOA, quantum machine learning, quantum key distribution, urban optimization, IoT security, traffic signal control, energy grid management, hybrid architecture
Field	Computer Applications
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-14
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.73986

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Quantum Computing for Smart Cities: Algorithms, Architectures, and Applications for Urban Optimization

Share this