Quantum Criticality Index: Understanding Critical Raw Materials Supply Chains in Quantum Technologies

The aim of this empirical Stanford Center for Responsible Quantum Technology (RQT) Project is to develop and apply a data-based analytic methodology, focused on discovering vulnerabilities in Critical Raw Materials (CRMs) supply chains. Utilizing cutting edge computational informatics analysis techniques such as machine learning and artificial neural networks (ANNs), the Quantum Criticality Index (QCI) allows us to understand and anticipate potential risks in the progress of quantum technologies. We implement a data-driven methodology to address the vulnerabilities of quantum technologies in supply chains and CRMs, and consider how existing choke points may pose future security risks vis a vis developing, manufacturing, and adopting quantum computers and sensors at scale. Finally, the Project will propose strategic recommendations to industry, law and policy makers about how to overcome these economic safety and national security risks.

Background, Goals and Impact
In 2023, IBM released its 1121-qubit quantum computer, making the idea of applying quantum computing technologies to our daily lives much more realistic. There are several major types of quantum computing systems, including superconducting, photonics, quantum dots, and ion traps. Each type requires specific materials, components, and equipment to optimize performance. Despite notable advancements, including predictions of market readiness of universal quantum machines before the end of this decade, second generation quantum technology (QT) remains an emerging field with limited practical and commercial applications—at least for now. For example, dozens of academic and industry teams are working on various approaches to qubit technologies — including superconducting, photonic, and silicon-based qubits — leading to a dilution of resources across multiple directions.

Development of new quantum computing systems relies heavily on advanced technological assessments, such as semiconductor processes, computer simulations, machine learning, and big data. These research achievements are followed by the synthesis of materials, components, and equipment in the laboratory for operational purposes. However, these advancements assume, as prerequisites, a guaranteed, uninterrupted supply chain and the availability of critical raw materials (CRMs) including responsible minerals (RMs), such as tantalum, tungsten, helium3, gallium, platinum, nickel, terbium, germanium, lithium, and cobalt. RMs refers to sourcing and obtaining raw minerals and rare earths while respecting human rights, protecting human and environmental health, and combating modern slavery, child labor, and human trafficking.

The Quantum Economic Development Consortium (QED-C) predicts that supply chain issues related to quantum computing, particularly key raw materials, will likely become problematic within the next three years. The biggest challenge is expected to be ensuring a stable supply of essential elements and parts for quantum manufacturing. From this perspective, the increasing amount of trade and export controls of quantum materials and devices, isn’t helpful. Such unstable resource markets could lead to significant concerns over resource security and supply, potentially exacerbating the supply-demand situation (a snowball effect). The vulnerability or risk refers to technological bottlenecks and typically involves factors such as the substitutability of technology, the adaptability of new technology (including the production of materials and components) to overcome current and future hurdles, and the extent to which performance depends on a small group of technologies and economies.

Therefore, anticipating and planning for critical points in the development of new quantum technologies or close to market application with high TRL (technology readiness levels) -such as nearby and long distance quantum sensing- is crucial to secure continued progress in scaling these systems for pilot applications. To maintain U.S. leadership in quantum technologies where the US is still leading, and to “maintain as large a lead as possible” over competitors such as China, it is worth considering political and technical (techno-political) options to identify choke points in critical parts of quantum technologies in advance. These include computing hardware (critical materials, components, and equipment), error correction software, and cloud services provision. As such, the Project implements concrete RQT principles to help address the risks, challenges, and opportunities associated with quantum technology, as proposed by the Quantum-SEA (safeguarding, engaging, and advancing) framework for responsible quantum innovation. In specific, the Project builds on two key components of a fit-for-purpose quantum technology regulatory framework: ‘safeguarding through advancing quantum technology, society, and humankind’, acknowledging the insight that safeguarding can at times be better achieved by advancing QT instead of merely seeking to optimize safety and security alone.

Technical Approach
The typical analytic method for investigating critical raw materials (CRMs) and supply chains of quantum technologies (QTs) is a rule-based model, which relies on a decision flow model to identify critical parameters. Although this method has its limitations, the decision flow model demonstrates how we can evaluate the vulnerability of CRMs, components, and equipment using specific vulnerability indicators.

Firstly, to assess the overall criticality of QT, each parameter (such as CRMs, components, and equipment) is determined through a combinatorial technique that considers both vulnerability and supply chain risk, aligned with national strategy. The results obtained from the decision flow model will be categorized based on the level of criticality of the raw materials currently used in quantum technologies, with the number of parameters being approximately ~102.

Secondly, to manage a large number of parameters, the introduction of a physical model based on mathematical calculations and governed by specific equations is optimized to handle these parameters while minimizing the workload. For this reason, mathematical calculations, which are based on the physical modeling of methodological approaches, are commonly introduced to anticipate the effects of additional parameters. However, the governing equations used in physical modeling have limitations in verifying real-world scenarios due to the assumptions inherent in these equations. Recently, advanced computational technologies have been applied in the design and prediction of uncertainties. One of the useful methods for estimating results is the use of artificial neural networks (ANNs) through machine learning. To overcome the limitations of conventional physical modeling or rule-based approaches, we can introduce the ANN method with various parameters, including industrial demands (such as material compositions, processing conditions, resource biases, growth rates, market volume, price inflation), national strategy (including supply risk, vulnerability, rarity, technical difficulty, criticality), and crisis urgency (such as resource anxiety, trend stability/instability, reverse crisis). As a result of this evaluation, if we can accurately estimate the highly threatened CRMs or potential risks in new quantum technologies (QTs), it would offer a highly efficient assessment method to industry, law and policy makers, enabling the oversight of possible choke points in the next generation of QTs.

Therefore, as the third stage, we will introduce an ANN model to identify CRMs or potential risks in QTs. This will be achieved by optimizing a combination of parameters from national strategy, industrial demand, to global catastrophe, such as COVID pandemic, wars and natural disasters. Through this RQT Project, by using data-based machine learning models to analyze a large number of indicators (ranging from 103 to 106), such as the concentration of essential materials, components, equipment by region, country, and industry, as well as material properties, production and consumption statuses, availability of substitutes, and trade flows, we aim to develop a Quantum Criticality Index (QCI).

The QCI represents a combined index among techno-political parameters. For instance, since CRMs are tracked internationally through the world trade market, this index introduces a quantitative measure of risks in resource markets, inter alia categorized into critical market verticals such as Defense, Healthcare, Agriculture, Energy, Transportation and regional sectors: geopolitics, environment, and responsibility. The QCI is a dynamic effort that requires up to date datasets. It can be measured and computed by combining resource price increase rates and volatility for each quarter. This allows us to describe a more realistically representative and forecastable index to monitor resource fluctuations. Ultimately, this methodology represents a reliable guideline for designing new, secure platforms for QTs, taking into account potential risks pertaining to Critical Raw Materials Supply Chains.