The Search for Quantum Utility in Chemistry

The window for defining the first practical uses of quantum computing is closing. With the first practical quantum computers expected to arrive in about two years’ time, the industry is shifting from theoretical exploration to identifying specific deployment requirements for scientific impact [PNNL recently convened experts](https://thequantumins

insider.com/2026/06/16/pnnl-discusses-how-quantum-computing-could-impact-chemistry-research/) to determine how these systems can achieve utility in chemistry and materials science.

At the second annual Quantum Computing for Chemistry workshop, organized by the PNNL QuAADS initiative, the conversation centered on moving past hype toward verifiable applications. The challenge presented to the group was clear: develop scalable and adaptive algorithms that function across varying system sizes and qubit counts to solve practical problems.

The technical requirements for this transition are significant. Discussions at the workshop highlighted a need for more than 100 logical qubits, alongside the development of scalable algorithms and experimentally verifiable applications to demonstrate meaningful quantum advantage. Participants focused on hybrid quantum-classical approaches and AI-assisted algorithm development as pathways to deliver early demonstrations of utility for complex chemistry and materials science problems.

This shift represents an inflection point in computing. As noted by leadership within the DOE’s Office of Science, the objective is now to define the exact parameters required to make a quantum computer useful to the scientific community. The focus has moved from asking if these machines will work to determining what they must be able to do to demonstrate utility in quantum chemistry.

The path forward requires bridging the gap between near-term hardware and the long-term goal of fault-tolerant quantum computers. For industry and research leaders, the priority is no longer just building qubits, but ensuring the software architecture can handle the complexity of real-world molecular simulations.

Identify which specific chemical processes in your pipeline are most sensitive to the limitations of classical computing.

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