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IBM Identifies Business Readiness as Key to Quantum Advantage

IBM's report stresses that business readiness is essential for achieving quantum advantage, highlighting practical applications over theoretical benchmarks.

Mercation Admin
June 22, 2026 2 min read
IBM Identifies Business Readiness as Key to Quantum Advantage

The IBM Institute for Business Value has released a report titled "Journey to Quantum Advantage," emphasizing that quantum advantage should not be viewed as a universal milestone but as a capability to address specific challenges more effectively than traditional computing.

Key Insights from the Report

The findings stem from interviews with industry leaders across various sectors, including aerospace, finance, biomedicine, energy, automotive, education, and public service. The report reveals that early adopters of quantum technology are focusing on practical applications rather than theoretical benchmarks to assess its potential.

IBM identified three primary motivations driving companies towards quantum computing:

  • To find solutions for complex challenges — 60% of respondents;
  • To prepare for future computational needs — 55%;
  • To enhance innovation — 54%.

The report underscores that achieving quantum readiness is linked not only to technological advancements but also to the necessity of practical applications, skills development, and collaborative efforts. The timeline for realizing quantum advantage varies by industry and specific use cases.

However, several barriers hinder implementation, including:

  • A shortage of quantum expertise — 61%;
  • Technology still in its infancy — 56%;
  • Uncertain timelines for practical applications — 46%.

IBM indicated that organizations must navigate technical challenges as well as concerns over return on investment, workforce capabilities, and identifying tasks where quantum computing can produce tangible outcomes.

Examples of Quantum Applications

Boeing is investigating quantum computing's potential in quantum chemistry and materials science, focusing on its applications in the design and certification of aircraft. The company emphasizes the importance of explainability and validation in operational environments.

Vanguard has initiated quantum experiments since 2022, exploring hybrid quantum-classical methods for issues like money laundering and risk modeling. Bimal Mehta, Vanguard's chief researcher, noted that quantum advantage must address client challenges rather than merely serve as a technological showcase.

E.ON has tested quantum techniques for optimization and machine learning, identifying both potential and limitations of current hardware, prompting a strategic shift towards long-term planning. Similarly, Bosch is focusing on algorithm development for future fault-tolerant quantum systems after facing challenges with current technology.

Advancements in Biomedicine

In the realm of biomedicine, IBM highlighted the work at Yonsei University, where quantum resources complement classical supercomputing for biosciences, particularly in drug development. Jae Ho Chung from the Yonsei Quantum Initiative emphasized that even minor reductions in drug development timelines could have a substantial impact.

The report also mentions efforts by the Wellcome Sanger Institute and the University of Oxford in quantum pangenomics, including a significant achievement in 2026 with the upload of the hepatitis D virus genome to a quantum processor.

The Importance of Collaborative Ecosystems

IBM stressed the significance of partnerships among businesses, governments, universities, and research institutions. The report notes that half of quantum-ready organizations are engaged in at least one collaborative effort. Notable examples include Volkswagen and Bradesco, reflecting a market phase characterized by experimentation and preparation for practical applications.

In June, researchers conducted tests on the IBM Nighthawk quantum processor, exploring its capabilities in particle physics and cybersecurity. There have also been discussions surrounding new approaches to quantum error correction using large language models.