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.

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.

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).

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

Source: S&P Global

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.

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.