Quantum computation, a paradigm shift in computer science, holds the potential to affect research in economics and finance profoundly. Quantum algorithms, such as quantum annealing and the Quantum Approximate Optimization Algorithm, can significantly speed up optimization and asset allocation by exploring vast solution spaces more efficiently. Quantum Monte Carlo methods enhance the accuracy and speed of simulations used in pricing derivatives and assessing risk. Additionally, quantum machine learning can improve predictive models. A conference and a special issue of the Journal of Economic Dynamics and Control are organized to promote cross-disciplinary exchanges on implementing quantum computational methods to problems of interest to economics and finance. The topics include, but are not limited to, solving DSGE models, pricing of financial risk, quantum ambiguity and econometric model specification, quantum money, and cryptography, among others.

In March 2023 the story of crypto hinged on FTX, but crypto has turned a page in a new chapter and that was clearly the case this year. There was an upbeat mood among industry participants, which included some of the creators of the recent SEC approved spot Bitcoin ETFs. Capital inflow into crypto funds has surpassed their two-year highs. However, regulatory uncertainty around the digital assets industry remains.

Traditional contact tracing, where workers query patients about their interactions to see who else might have been infected — and perhaps to find out where they got the infection — is being widely used. But for the first time, many public health authorities are considering, or have implemented, smart phone apps to partially automate and scale contact tracing.

Although artificial intelligence (AI) has become a crucial component of digital transformation efforts tied to organizational strategy, many firms struggle to articulate the strategic value of emerging AI systems. In this article we argue that the power of AI as a strategic resource lies in its self-learning capabilities. Such learning capabilities are only realized in partnership with humans through mutual learning. By building on a learning-centered framework, we elaborate on how AI can contribute to organizational learning to create a competitive advantage. We formulate the concept of artificial capital and the ways artificial and human capital can together drive routinization and strategic learning processes that connect internal and external environments of the organization. Finally, we use this conception to formulate practical recommendations for managing and developing AI to meet strategic business goals.