Call for papers - Quantum Computing and the City (Cities, Submission deadline: 31 October 2026)

Quantum Computing and the City

Submission deadline: 31 October 2026

Quantum computing represents a profound shift beyond classical computation, harnessing superposition, entanglement, and quantum tunnelling to tackle complex urban challenges that conventional systems cannot efficiently address. Its emerging applications span core urban domains: quantum algorithms may deliver real-time optimisation of metropolitan transport networks; quantum simulation could enhance climate adaptation through high-resolution environmental modelling; and quantum methods offer new capabilities for managing dynamic energy grids and integrating distributed renewables. These advances create opportunities for cities for more robust, evidence-based infrastructure planning, resource allocation, and policy design. Beyond computation, quantum sensing supports improved environmental monitoring, quantum communication enhances infrastructure security, and quantum AI enables deeper insights from large-scale urban datasets. When integrated with digital twins, intelligent grids, and emergency response systems, these technologies raise important questions about governance, regulation, equity, and institutional readiness. This special issue seeks cutting-edge research on the conceptual, technical, practical, and policy intersections between quantum technologies and urban systems, positioning quantum innovations not only as computational breakthroughs but also as potential enablers and disruptors of more adaptive, efficient, equitable, and sustainable urban futures, while recognising the new risks and trade-offs they introduce.

Guest editors:

Tan Yigitcanlar, Queensland University of Technology, Australia tan.yigitcanlar@qut.edu.au

Yuan Lai, Tsinghua University, China yuanlai@mail.tsinghua.edu.cn

Araz Taeihagh, National University of Singapore, Singapore spparaz@nus.edu.sg

Steven Jige Quan, Seoul National University, Republic of Korea sjquan@snu.ac.kr

Yanjie Fu, Arizona State University, USA yanjie.fu@asu.edu

Special issue information:

Emerging scholarship, e.g., Quantum AI Urbanism: Redefining the Future of Artificial Intelligence in Cities (Yigitcanlar et al., 2025), explores how next-generation computational paradigms may reshape urban technology, governance, and policy. Quantum computing marks a fundamental shift from classical bit-based logic by harnessing superposition, entanglement, and tunnelling to solve complex, large-scale problems that exceed conventional computational capacity. Potential applications span transport optimisation, fine-grained climate and environmental simulation, and integration of distributed renewables into dynamic energy grids. Combined with quantum sensing, quantum communication, and quantum machine learning, these capabilities open new possibilities for evidence-based urban planning, infrastructure management, and resilient policy design; while raising critical questions about governance, regulation, equity, and institutional preparedness.

This Special Issue, Quantum Computing and the City, invites cutting-edge research on full-stack quantum technologies (hardware, algorithms, sensing, communication), quantum-inspired optimisation and AI, and post-quantum cryptography—provided submissions demonstrate clear implications for urban systems, planning, policy, or governance. Purely technical papers without substantive urban relevance fall outside the scope. We welcome conceptual, theoretical, methodological, empirical, design-oriented, and policy-focused work using any methodological approach, including comparative, critical, and speculative studies grounded in urban concerns. All submissions must clearly articulate how the research advances understanding or practice in urban planning, design, governance, or management beyond generic smart-city or AI narratives.

The Special Issue aims to consolidate emerging knowledge and shape an international research agenda for quantum-enabled urbanism. By bridging urban studies, computer science, physics, engineering, and public policy, it seeks to:

• Critically assess the promises, challenges, and limitations of quantum technologies for urban contexts.

• Advance interdisciplinary understandings of quantum-enabled urbanism and its wider implications.

• Showcase pioneering quantum AI applications, experimental implementations, and urban policy-relevant case studies.

• Stimulate debate on responsible, ethical, and equitable deployment of quantum technologies in cities.

• Develop a forward-looking roadmap for integrating quantum technologies into urban innovation, planning, and governance.

• Explore the societal, economic, and environmental implications of quantum technologies for sustainable urban futures.

• Foster collaboration between researchers, policymakers, and industry to accelerate practical and inclusive quantum urban solutions.

Manuscript submission information:

You are invited to submit your manuscript at any time before the submission deadline, 31 October 2026. For any inquiries about the appropriateness of contribution topics, please contact Prof. Tan Yigitcanlar via tan.yigitcanlar@qut.edu.au.

The journal’s submission platform (Editorial Manager®) is now available for receiving submissions to this Special Issue. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: Quantum Computing and the City” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage here: https://www.sciencedirect.com/journal/cities

All the submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Upon its editorial acceptance, your article will go into production immediately. It will be published in the latest regular issue, while be presented on the specific Special Issue webpage simultaneously. In regular issues, Special Issue articles will be clearly marked and branded.

Keywords: Quantum urbanism; Quantum-enabled cities; Algorithmic urbanism; Quantum infrastructure; Responsible quantum AI; Ethical quantum sensing; Beyond smart cities; Urban planning and policy; Urban governance

CALL FOR PAPERS - ICPP6 T13P05 - PLATFORM GOVERNANCE IN TURBULENT TIMES

CALL FOR PAPERS

T13P05 - PLATFORM GOVERNANCE IN TURBULENT TIMES

https://www.ippapublicpolicy.org/conference/icpp6-toronto-2023/panel-list/17/panel/platform-governance-in-turbulent-times/1428

Abstract submission deadline: 31 January 2023

GENERAL OBJECTIVES, RESEARCH QUESTIONS AND SCIENTIFIC RELEVANCE

Platforms significantly increase the ease of interactions and transactions in our societies. Crowdsourcing and sharing economy platforms, for instance, enable interactions between various groups ranging from casual exchanges among friends and colleagues to the provision of goods, services, and employment opportunities (Taeihagh 2017a). Platforms can also facilitate civic engagements and allow public agencies to derive insights from a critical mass of citizens (Prpić et al. 2015; Taeihagh 2017b). More recently, governments have experimented with blockchain-enabled platforms in areas such as e-voting, digital identity and storing public records (Kshetri and Voas, 2018; Taş & Tanrıöver, 2020; Sullivan and Burger, 2019; Das et al., 2022).

How platforms are implemented and managed can introduce various risks. Platforms can diminish accountability, reduce individual job security, widen the digital divide and inequality, undermine privacy, and be manipulated (Taeihagh 2017a; Loukis et al. 2017; Hautamäki & Oksanen 2018; Ng and Taeihagh 2021). Data collected by platforms, how platforms conduct themselves, and the level of oversight they provide on the activities conducted within them by users, service providers, producers, employers, and advertisers have significant consequences ranging from privacy and ethical concerns to affecting outcomes of elections. Fake news on social media platforms has become a contentious public issue as social media platforms offer third parties various digital tools and strategies that allow them to spread disinformation to achieve self-serving economic and political interests and distort and polarise public opinion (Ng and Taeihagh 2021). The risks and threats of AI-curated and generated content, such as a Generative Pre-Trained Transformer (GPT-3) (Brown et al., 2020) and generative adversarial networks (GANs) are also on the rise (Goodfellow et al., 2014) while there are new emerging risks due to the adoption of blockchain technology such as security vulnerabilities, privacy concerns (Trump et al. 2018; Mattila & Seppälä 2018; Das et al. 2022).

The adoption of platforms was further accelerated by COVID-19, highlighting their governance challenges. The rise of misinformation and digital health technologies have created heated debates around trust and privacy on these platforms, and the term ‘misinfodemic’, though coined in 2018, is now used to refer to misinformation related to the pandemic (Marrelli, 2020). The US Sub-Committee on Antitrust, Commercial and Administrative Law recently released its report investigating competition in digital markets (US Antitrust Report, 2020). The report also finds that due to the absence of competition, dominant tech firms bear little financial consequence when misinformation is promoted online, and content moderation of unlawful and harmful content hosted on such platforms is an ongoing issue.
 
With this backdrop, countries worldwide have started looking into regulating technology platforms more seriously. This panel will present papers discussing the various dimensions of the use of digital platforms and their implications for policy-making.

CALL FOR PAPERS

This panel welcomes papers that explore the challenges of platform governance. Key research questions to be addressed are:
 
·       Theoretical and empirical papers using various qualitative and quantitative approaches from disciplines that provide insights about the implications of the rapid adoption of these platforms and their effect on policy-making.

·       The emerging theoretical, conceptual and empirical approaches to understanding new and unconventional regulatory approaches and governance strategies, as well as lessons learnt from the public and private organisations' standard-setting activities and development of guidelines for managing online platforms

·       Theoretical, conceptual, or empirical studies that evaluate the effects of platforms on public service delivery and analyse how these platform activities affect the perceived political legitimacy of governments.

·       Analysis of the roles of different actors in influencing policy outcomes through participation in platforms and at different stages of policy making.

·       Analysis of the role of tech companies in addressing and/or exacerbating the governance challenges of platforms.

·       Examining the different types of platform governance structures (e.g., in blockchain), their risks and unintended consequences, and the organisational, administrative, and institutional changes to accommodate these platforms.

·       Cross-national and cross-sectoral studies and theoretically informed case studies examining different types of platforms (e.g., social media, blockchain, sharing economy, crowdsourcing) are especially welcome.

Abstract submission deadline (up to 500 words) 31 January 2023

REFERENCES
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, … Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33.

Das, S., Rout, J., & Mishra, M. (2022). Blockchain Technology: Applications and Open Issues. In 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) (pp.1-6). IEEE.

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Proceedings of the 27th International Conference on Neural Information Processing Systems – Vol(2):2672–2680.

Hautamäki, A., & Oksanen, K. (2018). Digital Platforms for Restructuring the Public Sector. In Collaborative Value Co-creation in the Platform Economy (pp.91-108). Springer, Singapore.

Kshetri, N., & Voas, J. (2018). Blockchain-enabled e-voting. Ieee Software, 35(4):95-99.

Loukis, E., Charalabidis, Y., & Androutsopoulou, A. (2017). Promoting open innovation in the public sector through social media monitoring. Government Information Quarterly, 34(1):99-109.

Marrelli, M. (2020). Exploring COVID-19 in Emerging Economies: Announcing the 2020 Global Misinfodemic Report. Meedan

Mattila, J., & Seppälä, T. (2018). Distributed Governance in Multi-sided Platforms: A Conceptual Framework from Case: Bitcoin. In Collaborative Value Co-creation in the Platform Economy (pp.183-205). Springer, Singapore.

Ng, L. H., & Taeihagh, A. (2021). How does fake news spread? Understanding pathways of disinformation spread through APIs. Policy & Internet, 13(4):560-585.

Prpić, J., Taeihagh, A., & Melton, J. (2015). The fundamentals of policy crowdsourcing. Policy & Internet, 7(3):340-361.

Sullivan, C., & Burger, E. (2019). Blockchain, digital identity, e-government. In Business Transformation through Blockchain (pp.233-258). Palgrave Macmillan, Cham.

Taeihagh, A. (2017a). Crowdsourcing, Sharing Economies and Development, Journal of Developing Societies, Vol 33(2):191–222.

Taeihagh, A. (2017b). Crowdsourcing: a new tool for policy-making? Policy Sciences Journal, 50(4):629-647

Taş, R., & Tanrıöver, Ö. Ö. (2020). A systematic review of challenges and opportunities of blockchain for E-voting. Symmetry, 12(8):1328.

Trump, B. D., Wells, E., Trump, J., & Linkov, I. (2018). Cryptocurrency: Governance for what was meant to be ungovernable. Environment Systems and Decisions, 38(3):426-430.

US Subcommittee on Antitrust, Commercial and Administrative Law (2020). Investigation of Competition in Digital Markets. Majority Staff Report and Recommendations.  

CALL FOR PAPERS - ICPP6 T13P03 - GOVERNANCE AND POLICY DESIGN LESSONS FOR TRUST BUILDING AND RESPONSIBLE USE OF AI, AUTONOMOUS SYSTEMS AND ROBOTICS

CALL FOR PAPERS

T13P03 - GOVERNANCE AND POLICY DESIGN LESSONS FOR TRUST BUILDING AND RESPONSIBLE USE OF AI, AUTONOMOUS SYSTEMS AND ROBOTICS

https://www.ippapublicpolicy.org/conference/icpp6-toronto-2023/panel-list/17/panel/governance-and-policy-design-lessons-for-trust-building-and-responsible-use-of-ai-autonomous-systems-and-robotics/1390

Abstract submission deadline: 31 January 2023

GENERAL OBJECTIVES, RESEARCH QUESTIONS AND SCIENTIFIC RELEVANCE

Artificial intelligence (AI), Autonomous Systems (AS) and Robotics are key features of the fourth industrial revolution, and their applications are supposed to add $15 trillion to the global economy by 2030 and improve the efficiency and quality of public service delivery (Miller & Sterling, 2019). A McKinsey global survey found that over half of the organisations surveyed use AI in at least one function (McKinsey, 2020). The societal benefits of AI, AS, and Robotics have been widely acknowledged (Buchanan 2005; Taeihagh & Lim 2019; Ramchurn et al. 2012), and the acceleration of their deployment is a disruptive change impacting jobs, the economic and military power of countries, and wealth concentration in the hands of corporations (Pettigrew et al., 2018; Perry & Uuk, 2019).

However, the rapid adoption of these technologies threatens to outpace the regulatory responses of governments around the world, which must grapple with the increasing magnitude and speed of these transformations (Taeihagh 2021). Furthermore, concerns about these systems' deployment risks and unintended consequences are significant for citizens and policymakers. Potential risks include malfunctioning, malicious attacks, and objective mismatch due to software or hardware failures (Page et al., 2018; Lim and Taeihagh, 2019; Tan et al., 2022). There are also safety, liability, privacy, cybersecurity, and industry risks that are difficult to address (Taeihagh & Lim, 2019) and The opacity in AI operations has also manifested in potential bias against certain groups of individuals that lead to unfair outcomes (Lim and Taeihagh 2019; Chesterman, 2021).

These risks require appropriate governance mechanisms to be mitigated, and traditional policy instruments may be ineffective due to insufficient information on industry developments, technological and regulatory uncertainties, coordination challenges between multiple regulatory bodies and the opacity of the underlying technology (Scherer 2016; Guihot et al. 2017; Taeihagh et al. 2021), which necessitate the use of more nuanced approaches to govern these systems. Subsequently, the demand for the governance of these systems has been increasing (Danks & London, 2017; Taeihagh, 2021).

CALL FOR PAPERS

Many studies have highlighted the urgency for and the challenges of governing AI, AS and Robotics (Firlej and Taeihagh 2021; He et al. 2020; Tan and Taeihagh 2021; Tan et al. 2021; Radu 2021; Taeihagh 2021). In this panel, we are interested in governance and policy design lessons for Responsible Use and Building  trust in AI, AS and Robotics by answering the following key research questions:

·      What governance and policy design lessons have been learnt so far in addressing risks and unintended consequences of adopting AI, AS and Robotics in different domains and geographies?

 ·      What are the challenges of responsible use of AI, AS and Robotics, particularly in the public sector?

 ·      What are the emerging theoretical, conceptual and empirical approaches to understanding new and unconventional regulatory approaches, governance strategies, institutions and discourses to govern these systems?

 ·      What lessons have been learnt so far from the public and private organisations' standard setting and development of guidelines in managing these systems?

 ·      How can the public and expert viewpoints be better considered for the regulation and governance of AI, AS, and Robotics to increase trust in AI?

 ·    What is the role of governments in promoting trustworthy AI and building trust in AI?

Abstract submission deadline (up to 500 words) 31 January 2023

REFERENCES

 Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. Ai Magazine, 26(4), 53.

 Chesterman, S. (2021). Through a Glass, Darkly: Artificial Intelligence and the Problem of Opacity. The American Journal of Comparative Law, 69(2), 271-294.

Danks, D. (2019). The value of trustworthy AI. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 521–522.

 Guihot, M., Matthew, A. F., & Suzor, N. P. (2017). Nudging Robots: Innovative Solutions to Regulate Artificial Intelligence. Vand. J. Ent. & Tech. L., 20, 385.

 He, H., Gray, J., Cangelosi, A., Meng, Q., McGinnity, T. M., & Mehnen, J. (2020). The Challenges and Opportunities of Artificial Intelligence for Trustworthy Robots and Autonomous Systems. 2020 3rd International Conference on Intelligent Robotic and Control Engineering (IRCE), 68–74.

 Lim, H. S. M., & Taeihagh, A. (2019). Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities. Sustainability, 11(20), 5791.

 McKinsey. (2020, November 17). Global survey: The state of AI in 2020 | McKinsey.https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020

 Miller, H., & Stirling, R. (2019) The Government Artificial Intelligence (AI) Readiness Index Report 2019.https://www.oxfordinsights.com/ai-readiness2019

 Page, J., Bain, M., & Mukhlish, F. (2018, August). The risks of low level narrow artificial intelligence. In 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) (pp. 1-6). IEEE.

 Perry, B., & Uuk, R. (2019). AI governance and the policymaking process: key considerations for reducing AI risk. Big Data and Cognitive Computing, 3(2), 26.

 Pettigrew, S., Fritschi, L., & Norman, R. (2018). The potential implications of autonomous vehicles in and around the workplace. International Journal of Environmental Research and Public Health, 15(9), 1876.

 Radu, R. (2021). Steering the governance of artificial intelligence: national strategies in perspective. Policy and Society, 40(2), 178-193.

 Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. R. (2012). Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence. Communications of the ACM, 55(4), 86-97.

 Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harv. JL & Tech., 29, 353.

Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society, 40(2), 137-157.

 Taeihagh, A., & Lim, H. S. M. (2019). Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transport reviews, 39(1), 103-128.

 Taeihagh, A., Ramesh, M., & Howlett, M. (2021). Assessing the regulatory challenges of emerging disruptive technologies. Regulation & Governance, 15(4), 1009-1019.

Tan, S.Y., & Taeihagh, A. (2021). Adaptive governance of autonomous vehicles: Accelerating the adoption of disruptive technologies in Singapore. Government Information Quarterly, 38(2), 101546.

Tan, S.Y., Taeihagh, A., & Tripathi, A. (2021). Tensions and antagonistic interactions of risks and ethics of using robotics and autonomous systems in long-term care. Technological Forecasting and Social Change, 167, 120686.

 Tan, S., Taeihagh, A., & Baxter, K. (2022). The Risks of Machine Learning Systems. arXiv preprint arXiv:2204.09852.

CALL FOR PAPERS - ICPP6 T07P01 - EXPLORING TECHNOLOGIES FOR POLICY ADVICE

CALL FOR PAPERS

T07P01 - EXPLORING TECHNOLOGIES FOR POLICY ADVICE

https://www.ippapublicpolicy.org/conference/icpp6-toronto-2023/panel-list/17/panel/exploring-technologies-for-policy-advice/1295

Abstract submission deadline: 31 January 2023

GENERAL OBJECTIVES, RESEARCH QUESTIONS AND SCIENTIFIC RELEVANCE

Knowledge and expertise are key components of policy-making and policy design, and many institutions and processes exist – universities, professional policy analysts, think tanks, policy labs, etc. –  to generate and mobilize knowledge for effective policies and policy-making. Despite many years of research, however. many critical ssues remain unexplored, including the nature of knowledge and non-knowledge, how policy advice is organized into advisory systems or regimes, and when and how specific types of knowledge or evidence are transmitted and influence policy development and implementation. These long-standing issues have been joined recently by use of Artificial Intelligence and Big data, and other kinds of technological developments – such as crowdsourcing through open collaboration platforms, virtual labour markets, and tournaments – which hold out the promise of automating, enhancing. or expanding policy advisory activities in government. This panel seeks to explore all aspects of the application of current and future technologies to policy advice, including case studies of its deployment as well as theoretical and conceptual studies dealing with moral, epistemological and other issues surrounding its use.

CALL FOR PAPERS

You are invited to submit proposals for papers on different aspects of emerging technoglies for assisting in generation and dissemination of policy advice. The  panel will explore all aspects of the application of current and future technologies to policy advice, including case studies of its deployment as well as theoretical and conceptual studies dealing with moral, epistemological, political, technical and other issues surrounding its use. Knowledge and expertise are key components of policy-making and policy design, and many institutions and processes exist  – universities, professional policy analysts, think tanks, policy labs, etc. –  to generate and mobilize knowledge for effective policies and policy-making. Despite many years of research, however, many critical issues remain unexplored, including the nature of knowledge and non-knowledge, how policy advice is organized into advisory systems or regimes, and when and how specific types of knowledge or evidence are transmitted to influence policy development and implementation. These long-standing  issues have been joined recently by the use of Artificial Intelligence and Big data and other kinds of technological developments – such as crowdsourcing through open collaboration platforms, virtual labour markets, and tournaments – which hold out the promise of automating, enhancing. or expanding policy advisory activities in government. Papers on both longer-term and more recent issues surrounding policy advice, and related topics, are welcome.

Abstract submission deadline (up to 500 words) 31 January 2023