Quantum Technology in Chemicals and Materials
We see the strongest case for quantum advantage in chemistry. Catalysts, battery materials and many industrial reactions depend on strongly correlated electrons, which classical methods model poorly. In eight years, resource estimates for FeMoco, the catalyst behind biological nitrogen fixation, fell by about five orders of magnitude. A 2025 Google estimate puts it at about 1,500 logical qubits, within the 2,000-logical-qubit range IBM targets for its Blue Jay system, dated 2033 or later.
The advantage is real and narrow. Most chemistry will keep running on classical computers and AI, and we expect the first quantum results on specific problems: catalysts, battery cathodes, photosensitizers and correlated materials. We estimate that building the skills and workflows to use those results takes two to five years. In this course, we cover what quantum computing, sensing and networking can do for chemicals and materials, how to judge each claim, and what to prepare now.
Course Outline
Module 1. Judging Quantum Claims (45 minutes)
Quantum computing, sensing and networking mature at different speeds, and each needs its own tests. We separate results demonstrated on hardware from company announcements and from resource estimates. We also separate the physical qubits in current machines from the logical, error-corrected qubits required for useful computation. You learn to ask which classical method a result was compared with, and which roadmap dates are commitments and which are targets. We test each question on a recent chemistry result, the 234-fold speedup reported for a catalyst simulation, which compares against earlier quantum methods.
Module 2. Quantum Simulation of Molecules and Materials (75 minutes)
We work through the problems with published resource estimates: nitrogen-fixation and CO₂ catalysts, lithium-excess and nickel-rich battery cathodes, photosensitizers, and correlated materials such as Hubbard models. We explain phase estimation, and the tensor factorizations researchers used to cut resource estimates by orders of magnitude. We explain why a calculation’s feasibility often depends more on its gate count, which determines the runtime, than on its qubit count. We cover the hybrid methods running on hardware today, including sample-based quantum diagonalization, and what their results do and do not prove. You learn to read a resource estimate: logical qubits, Toffoli gates, runtime and the accuracy target.
Module 3. Hybrid Workflows, Sensing and Data (45 minutes)
We cover embedding methods that combine quantum solvers with classical HPC, and why the error in the fragmentation can exceed the error in the solver. We compare quantum simulation with the AI and HPC methods improving in parallel. Microsoft and PNNL, for example, screened 32 million candidates with AI and HPC to find a new solid electrolyte. We also cover quantum sensors for materials characterization, such as NV-diamond microscopy, and the protection of R&D data that must stay confidential for many years.
Module 4. What Chemical and Materials Companies Are Doing (30 minutes)
We review named programs and what each result shows. They include BASF with Google on battery cathodes, BMW, Airbus and Quantinuum on fuel-cell catalysts, and Boehringer Ingelheim with PsiQuantum. We also cover Oak Ridge National Laboratory’s fusion-materials calculation with IBM, Johnson Matthey’s catalyst work with Riverlane, and Mitsubishi Chemical and JSR with IBM on OLED materials. We close with the US$30 million ARPA-E program for chemistry and materials. For each, you learn whether it reports a hardware result, an announcement or an estimate.
Module 5. Preparing Now (45 minutes)
We show you how to list the problems where strong electron correlation blocks a business decision, then score each on economic value, classical difficulty and quantum resources. You learn to work back from the decision to what must be computed and validated. You also learn to connect quantum, computational and bench teams. We cover hardware partnerships without single-vendor lock-in, and the skills to build first. In a working exercise, you draft a one-page quantum brief for your R&D leadership.
Prerequisites
None.
Who this course is for
R&D directors, heads of computational chemistry and materials modelling, innovation leads, executives and program managers at chemical, materials, battery and specialty-chemicals companies. Also useful for investors and consultants who assess materials technologies.
What you’ll be able to do afterward
- Explain why chemistry has the strongest case for quantum advantage, and where that case ends
- Read a resource estimate and judge when a target molecule becomes feasible
- Tell a gain over earlier quantum methods from a gain over the best classical method
- Identify the problems in your portfolio that are candidates for quantum simulation
- Compare quantum simulation with the AI and HPC methods advancing in parallel
- Plan the skills, workflows and partnerships to have in place by the end of the decade
What you leave with
When you finish, we email you the course handbook, a PDF of every slide with notes that explain it in full. It includes a table of the use cases covered, each labelled as demonstrated, announced or estimated, with its sources. We also include a PDF copy of Quantum Systems Integration. The handbook is yours to keep.
Enrollment includes 180 days of access to the online on-demand course. Where that course is not yet published, the 180 days start on the day it is.
Where this course fits
You can take this course on its own. No technical background is assumed. Computational teams can continue with Quantum Computing Fundamentals and Hands-On Quantum Programming. Battery and energy-materials teams may also want Quantum Technology for Energy and Utilities. Chemical plants still have to move their own cryptography to post-quantum standards, and Quantum-Safe OT and Critical Infrastructure covers plant systems.
Why we teach this
The material is based on Quantum Systems Integration, the reference text on quantum computing, networking, and sensing written by the course author. It also draws on faculty who work with quantum engineering and integration teams, and on the published resource estimates for each use case. We label every result in the course as demonstrated on hardware, announced by a company or estimated, so you can see how far each one has progressed.
Certificate of Completion
Upon completion, you receive a Quantum Academy certificate of completion. It confirms that you completed the course and is separate from Quantum Academy’s professional certifications.
About this program
Quantum Academy credentials are private professional credentials issued by Quantum Academy, a trade name of Post-Quantum Institute. They are not government-issued licenses, accredited degrees, or academic credit, and earning one does not guarantee employment, promotion, regulatory approval, or any other specific outcome.
Quantum Academy programs are educational and informational only, and are not legal, compliance, or engineering advice.
