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Artificial Intelligenceand Quantum Computing:The Fundamentals


This is a thought leadership report issued by S&P Global. This report does not constitute a rating action, neither was it discussed by a rating committee.

 

Highlights


  • Quantum computing's combination with artificial intelligence promises a paradigm shift in computational speed and capability but will also bring new threats in terms of cyber security, privacy, and the potential for damaging bias.


  • Recent developments in both areas have been remarkable. Yet, advancements in their combination will require scarce human talent and the development of specialized hardware and algorithms, meaning quantum AI will remain the preserve of well-funded institutions and that widespread commercial adoption is unlikely over the next decade.


  • The potential for negative outcomes demands the implementation of ethical and environmental frameworks to guide applications and ensure that the technology benefits humanity.

 

AI and quantum computing each offer foundations for new technologies and applications that are capable of redefining the boundaries of computational possibility. Yet it is the combination of AI's ability to synthesize results from vast amounts of data and quantum's supercharged computing power that promises to be truly revolutionary.


The pairing could lead to unprecedented advancements across a host of sectors from healthcare, via accelerated drug discovery, to energy production, where optimization could deliver sustainability breakthroughs.


The technologies are nascent and expertise, particularly at their nexus, remains concentrated in the hands of relatively few people and institutions. Yet understanding the potential, and limitations, inherent to the combination of AI and quantum computing will be key to predicting possible applications, foreseeing the effects of adoption across industries, and understanding the emerging risks that will come with those developments.


Figure 1: AI may be supercharged by quantum computing


Quantum computing fundamentals


Quantum computing uses the principles of quantum mechanics to perform calculations and simulations exponentially faster and more accurately than classical digital (or binary) computing. Use cases for the technology should prove plentiful, though practical application has so far focused on three main areas: 


  • Advancements in quantum computing technology itself (e.g., mathematics, physics, and technical developments). 

  • Natural sciences, including simulations and process research (e.g., behaviors of atomic particles and biological processes).

  • Problems related to novel and complex search and its optimization (e.g., portfolio optimization problems, insurance, and credit risk).


S&P Global believes that the evolution and application of quantum computing will ultimately prove a paradigm shift, offering the potential to solve problems that are currently too complex, or too time consuming, to solve with classical computing methods. The resulting breakthroughs have the potential to deliver a new wave of innovation.


How quantum computing works


We are used to working in a world of classical (or binary) computing, where information is stored and processed using bits as the primary unit. Each bit can be either a 0 (off) or a 1 (on) and processing occurs serially, one step after another, though multiple serial operations can happen in parallel.


Quantum computing's fundamental difference is its use of quantum bits, or qubits, as the primary unit. Qubits can represent (or store) and process more complex information than a classical bit because of two quantum phenomena, called superposition and entanglement.


  • Superposition is a fundamental property of quantum mechanics that describes the ability of qubits to be in several states at the same time. This ability (compared to the 0 or 1 state of a classical bit) enables quantum computing to simultaneously process a vastly greater number of possibilities than binary computing.

  • Entanglement describes the phenomena of qubits becoming coupled in such a way that the state of one qubit instantly affects the state of its entangled partners, regardless of their distance apart. This offers two major benefits to computing: it can protect communication and cryptography by detecting any efforts to intercept or listen in on transmissions; and, because entangled qubits can be manipulated collectively, it allows parallel processing of information that is beyond the ability of classical computing and its binary bits.d by a r


Figure 2: Classical computing versus quantum computing​

The state of quantum computing


Quantum computing is predominantly at an experimental and developmental phase, but it is moving out of university physics laboratories and into commercial research and development facilities. Investment and work is underway at several commercial organizations (including IBM, Google, Microsoft, Rigetti, and IonQ) and is delivering advancements in the technology and its capabilities.


Nonetheless, there are still many technical challenges to overcome before quantum computing can be readily deployed to solve real-world problems or be made generally available (see figure 3).


Figure 3: Quantum computing’s challenges​

 

A brief history of quantum computing


Quantum computing is rooted in quantum mechanics, a branch of physics developed in the early 20th century from the foundational work of scientists including Max Planck, Albert Einstein, and Niels Bohr. It started to gain momentum in the early 1980s, largely following physicist Richard Feynman's suggestion that quantum systems could perform certain computations more efficiently than classical computers. This work was further developed by the likes of Paul Benioff, David Deutsch, and (in the 1990s) Peter Shor’s and Lov Grover’s work on quantum algorithms—Shor on factoring large numbers and Grover on searching large databases. The sum of this work demonstrated the potential of quantum computing, sparking significant interest and research. (For more on AI see Machine Learning: The Fundamentals, Nov. 29, 2023).

 

The potential of AI and quantum computing


The convergence of AI and quantum computing promises to be revolutionary. Across a host of industries the technologies could solve problems that due to complexity or their inherent nonlinearity are currently intractable for classical computers.


Table 1: Applications of AI and quantum computing


Sector

Potential benefits

Healthcare (drug discovery)

Quantum computing's ability to simulate quantum systems should aid the understanding of molecule and material behavior, with particular benefits for materials sciences, including in the complex and time-consuming process of drug discovery. AI can analyze vast datasets to identify potential drug candidates, predict their effects, and optimize clinical trials. Quantum computing enabled simulations should enhance this process, leading to more accurate predictions of drug efficacy and safety.

Financial services

AI is transforming financial services by enabling better risk assessment, fraud detection, increasing process automation, and personalizing customer experiences. Quantum computing should enable greater complexity and thus optimization of financial models, and improved accuracy in risk analysis. For instance, quantum algorithms can improve portfolio optimization and trading strategies and simulations, leading to more efficient and potentially more profitable financial operations. They should also prove adept at simulating risks that may occur simultaneously, systematic risks, and risk relationships that might otherwise go unseen.

Supply chain management

AI can be used in demand prediction, to optimize inventory, and improve logistics. Quantum computing can tackle the complexity of supply chain networks by solving optimization problems faster and more accurately. For example, quantum algorithms could be used to optimize delivery routes, reducing fuel consumption and delivery times to provide cost savings and environmental benefits.

Energy

AI can optimize energy consumption, predict equipment failures, and manage smart grids. Quantum computing can provide further gains by optimizing energy production and distribution networks. For instance, quantum algorithms can improve the efficiency of renewable energy sources, by optimizing the placement of turbines and panels (including dynamically, i.e., moving solar panels automatically to optimize solar exposure).

Cyber security

Quantum computing and AI pose both opportunities and challenges for cyber security. Quantum computers can potentially break current public-key encryption methods but should also enable the development of quantum-resistant encryption techniques. AI can enhance cybersecurity by detecting and mitigating threats in real-time.

Source: S&P Global


Risks from AI and quantum computing


The commercial implementation of AI and quantum computing remains at a developmental and planning phase. Our understanding of its implications will evolve with use, but there is an evident need to be aware of the risks that may emerge as we move to adoption.


Cyber security

AI and quantum computing have the potential to be weaponized by bad actors and could be used to cause serious harm to nations and businesses. Key areas of concern include:


Breaking of cryptographic ciphers. Quantum computing's potential to break current cryptographic ciphers is the most evident and significant cyberthreat. Notably, it jeopardizes public-key based encryptions used to secure data and systems--primarily RSA (named after its inventors Rivest-Shamir-Adleman) and elliptic curve cryptography (ECC). The potential for damage from the interception and deciphering of encrypted communications is evident, and there is also a threat from criminals harvesting encrypted data now, in readiness for quantum decryption technology.


Automated vulnerability discovery and exploitation. AI combined with quantum computing could be used to scan networks and systems for vulnerabilities at unprecedented rates, enabling attackers to exploit weaknesses before they can be patched.


Phishing and deepfakes. Attackers can use AI to generate highly convincing and personalized phishing emails. Manipulated videos and images could be used for disinformation campaigns or to impersonate individuals for malicious purposes. These types of attacks are present now but will become more sophisticated as AI and quantum technology evolves.


Privacy

AI feeds on vast amounts of data, and quantum computing provides unprecedented processing speed. As these systems are combined and develop, so will their ability to infer sensitive information from seemingly innocuous data, potentially exposing individuals and companies to privacy breaches and identity theft.


Environmental 

AI and quantum computing will lead to advanced language models, deep learning models, and deep learning solutions, all of which will require significant energy. Widespread implementation of these technologies, without careful management, could raise potentially severe environmental, sustainability, and health-related concerns.


Bias 

Quantum computing powered machine learning algorithms that generate and work with inaccurate predictions and data could become endemic if algorithms are not carefully designed to be free of prejudice, gender discrimination, and other biases.


Digital repression

AI and quantum computing’s ability to process vast amounts of data at incredible speeds will boost surveillance technologies' capabilities. That creates the potential for sophisticated and invasive analysis of personal behaviors and social interactions, including real-time monitoring, social and financial classification, and sorting and scoring of individuals at an increasingly granular level.


Key trends to watch over the next five years


As AI and quantum computing evolve, several trends are emerging that will shape their future and the impact they will have. 


Hybrid classical-quantum systems

Hybrid systems that combine classical and quantum computing, leveraging the strengths of both technologies and orchestrating them, are likely to emerge in the near term. Such systems might employ classical computers to handle routine tasks and quantum computers for complex problems requiring significant processing speed. The resulting efficiency and optimization should markedly improve the scalability of solutions, reduce compute times, expand use cases, and thus promote adoption across industries.


Quantum machine learning (QML)

This emerging field combines the principles of quantum computing and AI. QML algorithms can potentially process and analyze data at unprecedented speeds, opening new possibilities for pattern recognition, data clustering, and optimization. As quantum hardware improves, QML could revolutionize fields such as image and speech recognition, natural language processing, and predictive analytics.


Enhanced cyber security

AI and quantum computing present both an opportunity and a threat for cyber security. Quantum computers can potentially break current encryption methods, but they can also enable the development of quantum-resistant encryption. AI can enhance cyber security by detecting and mitigating threats in real-time. The combination of AI and quantum computing will be crucial in developing robust cyber security solutions, though the traditional action/reaction dynamic between attackers and defenders will remain a hallmark of cyber security dynamics.


Ethical and societal implications

The combination of AI and quantum computing raises important ethical and societal questions surrounding issues including data privacy, trust, algorithmic bias, and employment. These issues will need to be addressed as the technologies become more pervasive. It will be essential to establish ethical frameworks and regulatory guidelines to ensure responsible and equitable use. Transparent standards will be particularly important given that the computational designs of such solutions may be difficult to simply describe, explain, and thus monitor.


Development and deployment of the synergies between AI and quantum computing must take place in an environment that remains mindful of ethical and societal implications, ensuring that these advancements benefit humanity as a whole.

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