by Amara Graps
Dis-Entangling the Quantum Future
The European Commission rarely takes budgetary risks for its 27 member states. Its former ‘Future Emerging Technologies’ funding program a decade ago was an unusual beacon of high-risk, cutting-edge ideas that was popular enough to develop further, spawning the European Innovation Council (EIC) to support the commercialization of high-risk, high-impact technologies in the EU. It’s no surprise, then, that the EIC has developed a Futurist mindset.
The report by Mochan et al., 2024, (Dis)Entangling the Future – Horizon scanning for emerging technologies and breakthrough innovations in the field of quantum technologies is a useful document of European policy recommendations, developed using foresight methodologies, which emerged from the EU Policy Lab.
The report:
- Provides outcomes from a horizon scanning exercise as part of the FUTURINNOV project, which is a collaboration between the European Innovation Council (EIC) and the Joint Research Centre (JRC).
- The primary goal was to evaluate and prioritize trends in emerging technologies and innovations within the EIC’s Quantum technologies portfolio.
- Nine key topics were identified, including quantum sensing, quantum algorithms, materials for quantum, and AI for quantum.
- Factors influencing these technologies include technical advancements, investment, collaboration, regulation, talent, market maturity, and application utility.
Nine key topics emerged as outcomes, first described here generally. See Mochan et al., 2024’s Report for more details.
- Quantum sensing
- CFD – Quantum algorithms for lattice-based computational fluid dynamics models
- Materials for quantum
- AI for quantum
- Error correction
- Solid-state scalability
- Quantum for AI
- Quantum as a Service (metacloud)
- Quantum computers
- Wild card final selection
- Quantum algorithms for lattice-based computational fluid dynamics models (CFD)
- Quantum sensing AI on edge
- Quantum as a Service – Metacloud
- Molecular spin qubits
If you need to be prepared for these industries but don’t know where to start, GQI offers a number of frameworks for analyzing them. (*)
In addition to the outcomes, the distillation process was remarkable. The research classified various quantum professionals into two groups based on their quantum technology interests and fields. Each group was asked to select ‘signals’ (as defined in Scanning Deep Tech Horizons) that could answer the question “What are the technologies and innovations that are more likely to breakthrough / grow / advance in the next 5 to 10 years?” and to explain ‘why?’. Participants assigned TRL values to these signals. Participants regrouped and discussed 5-10 signals or clusters in total. The group activity culminated with selecting the most unique and disruptive signals as a wild card for this EIC portfolio.
Participants were additionally asked to identify drivers, enablers, and barriers that could affect quantum sector technology development and adoption, focusing on those selected in the previous step. The contributions were mapped using an adapted “Futures Triangle” framework. See next Figure.
Shown is an example used with the participants.
AI for Quantum | Quantum for AI
The project’s Outcome results in these quantum sectors highlights the two-sided question.
AI for Quantum
- Error correction
- Quantum middleware
- Emerging automation tools
- Error Correction protocols and hardware
AI for quantum describes how AI can benefit the quantum field. AI’s influence on quantum computing is pushing developments in resource management, algorithm design, and optimization. Recent developments demonstrate how AI and quantum machine learning can be used to revolutionize optimization methods and circuit design for improved performance. In order to maximize computational capacity, while minimizing resource consumption, novel approaches use AI for resource-efficient algorithm development and smart qubit allocation. Furthermore, AI-powered quantum simulation speeds up the investigation of quantum systems and makes it easier to find and improve algorithms.
Quantum for AI
- Quantum machine learning
- Quantum artificial intelligence
- Quantum neural networks
- Automatic classification of quantum states
Quantum for AI describes how the quantum field can aid in the advancement of AI. By introducing methods like parametrized quantum circuits (PQC) and Noise-Adaptive Search (QuantumNAS), quantum machine learning (QML) makes quantum computing noise-resilient for applications like stability assessment and image classification. With hybrid models and quantum algorithms, quantum artificial intelligence (QAI) offers greater computational capacity, supporting efforts in sustainability and fault detection. Quantum Neural Networks (QNNs) exhibit potential for analyzing language and power grids, and they also make quantum information tasks easier by automatically classifying quantum states.
The FUTURINNOV project’s Outcome results raise the important two-sided question:
- What can AI do for quantum technologies?
- What can quantum technologies do for AI?
with one side of the question: ‘AI for quantum’ possibly being more useful in today’s NISQ era than the other side ‘Quantum for AI’
(*) GQI has Frameworks ready to examine several of the listed Sectors, as well as trackers to monitor their evolution. If you need to be prepared for these industries but don’t know where to start, GQI offers a number of frameworks for analyzing them. Several examples:
- Quantum as a Service (metacloud):
Cloud Services for Quantum Focus Report - Quantum computers:
Quantum Computing Outlook and Quantum Computing Hardware State-of-Play - Quantum sensing:
Quantum Sensing Outlook and Quantum Sensing State-of-Play
GQI’s Outlook or Focus Reports, Playbooks, or Trackers contain a plethora of decision-making information. As shown in the picture below, the quantum sensing sector is well-established. GQI’s framework examines each of these components. If you are interested, please don’t hesitate to contact [email protected].
September 27, 2024
