Many fields of finance involve complex optimization problems under strict time constraints — problems where even marginal improvements could generate immense value for firms. Could quantum computing bring such improvements? Two recent IBM studies with major financial industry participants have explored potential use cases for quantum computing in finance.Continue Reading Quantum Computing: Quantum Applications in Finance
Quantum-as-a-Service: Practical Considerations for Drafting and Negotiating Agreements
Quantum computing is beginning to move from labs into commercial deployment, and one of the main ways companies will be able to access this technology is through Quantum-as-a-Service (QaaS) offerings. Instead of companies investing in costly quantum hardware on-site, the QaaS model would allow them to tap into quantum capabilities via remote access services, much like they would with Software-as-a-Service (SaaS) arrangements. But while the delivery model may be akin to the SaaS model, quantum technology is still in its early stages and has unique hardware and infrastructure related challenges, as further described in a recent Covington blog post. Continue Reading Quantum-as-a-Service: Practical Considerations for Drafting and Negotiating Agreements
Harnessing the complementary power of AI and Quantum Computing
Artificial Intelligence (“AI”) continues to command attention as today’s prominent technological asset, revolutionizing key markets and sectors. Simultaneously, discussions of another advanced technology known as quantum computing have gained traction. Because both technologies expand the universe of problems that can be tackled by computers, one might wonder, if we have AI, do we also need quantum computing technologies? In this article, we will discuss quantum computing and how it complements AI, including its ability to enhance AI models, and conversely, also explore AI’s ability to strengthen the power of quantum computing.Continue Reading Harnessing the complementary power of AI and Quantum Computing
Quantum Computing and its Impact on the Life Science Industry
Quantum computing uses quantum mechanics principles to solve certain complex mathematical problems faster than classical computers. Whilst classical computers use binary “bits” to perform calculations, quantum computers use quantum bits (“qubits”). The value of a bit can only be zero or one, whereas a qubit can exist as zero, one, or a combination of both states (a phenomenon known as superposition) allowing quantum computers to solve certain problems exponentially faster than classical computers.
The potential applications of quantum computing are wide-ranging and industry-agnostic. For instance, they could be used to enhance the analysis of large, complex data sets, optimize supply-chain processes, and enhance artificial intelligence (“AI”) technologies and improve machine learning algorithms.
Given the potential applications, quantum computing could have a significant impact on companies in the life sciences sector, and more specifically could be used to improve:
1. Drug discovery
Classical computational methods play a crucial role in the drug discovery and design process by providing tools and techniques to model, predict and analyze the behavior of chemical systems. Quantum computing technologies have the potential to offer a more powerful, accurate and efficient alternative to classical computers, and can simulate more intricate chemical structures and interactions, leading to more optimized drug design.
Quantum computing can also be used as a complementary technology in conjunction with AI, which is already being used for drug discovery, thus exponentially increasing the accuracy and speed of the drug discovery process and potentially reducing the associated costs.
2. Clinical development
Quantum computers are far superior to classical computers when handling problems with multiple variables and complex datasets and it is exactly these capabilities that can be leveraged in clinical development phases. For example, quantum computing can be used to optimize the design of clinical trials; to enhance the data analysis and modelling process post-clinical trial by quickly detecting complex patterns and correlations; to monitor and adapt clinical trials in real-time, allowing for dynamic adjustments to protocols to maximize individual patient outcomes and address emerging safety concerns.
Quantum computers can be used in conjunction with AI, specifically machine learning, to analyze genetic and biomolecular data in order to better predict individual responses to specific treatments and create optimal treatment plans tailored to the genetic makeup and health condition a specific patient.Continue Reading Quantum Computing and its Impact on the Life Science Industry
U.S. AI and IoT Legislative Update – Year-End 2021
As 2021 comes to a close, we will be sharing the key legislative and regulatory updates for artificial intelligence (“AI”), the Internet of Things (“IoT”), connected and automated vehicles (“CAVs”), and privacy this month. Lawmakers introduced a range of proposals to regulate AI, IoT, CAVs, and privacy as well as
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