credit: IBM
In a new paper published in The Journal of Chemical Theory and Computation, researchers from IBM Quantum and Lockheed Martin demonstrate how a quantum computer can help accurately model the electronic structure of certain molecules, IBM announced in a blog today.
These so-called, “open-shell” molecules contain one or more unpaired electrons, making them difficult to simulate with classical methods alone.
IBM said the new research marks the first application of the sample-based quantum diagonalization (SQD) technique to open-shell systems — an important milestone for quantum chemistry and its applications in aerospace, sensing, and materials design. IBM researchers believe that SQD is a prime candidate for near-term demonstrations of quantum advantage, as it allows researchers to combine the best of high-performance quantum computers and high-performance classical computers in tackling interesting simulation problems.
Quantum for Chemistry: Why Now?
Quantum chemistry has long stood out as one of the most promising applications for quantum computing. Many chemical systems — particularly those involving strong electron correlation, e.g., transition metals, radical species, transition states, or excited states — are exceptionally hard to simulate using classical high-performance computing.
The computational cost of exactly modeling these systems with classical computers grows exponentially with the number of interacting electrons, making exact solutions practically inaccessible for even relatively small molecules. While there are some classical approximation methods that can simulate chemical systems with strong electronic correlation, these methods are computationally expensive.
This is where quantum computers could come in. Quantum chemistry simulations run on quantum computers can accurately compute the electronic structure and energies of these systems as an alternative to classical methods. As quantum computers progress toward fault-tolerance, it is possible that they will be able to simulate strongly correlated systems more accurately and at much larger scales than any classical approximation method.
One especially important quantity in computational chemistry is the energy difference between the electronic states of a molecule, such as the gap between a singlet and a triplet state — more on those later. These electronic transitions play a central role in everything from chemical reactivity and photochemistry to material properties and molecular sensing.
Accurately computing transition energies enables scientists to:
- Predict reactivity and mechanisms in catalytic and combustion reactions
- Design molecules with tailored optical or electronic properties, such as fluorescent probes or solar absorbers
- Model excited-state behavior in materials used for sensing, display technologies, or aerospace applications
- Benchmark and validate experiments in cases where measurements are difficult, dangerous, or expensive
But accurate calculation of transition energies is also extremely challenging. As molecules become more complex, the number of possible interactions between electrons (i.e., the electron correlation) grows exponentially. Capturing these interactions accurately, especially when electrons are strongly correlated or unpaired (as in radical species), pushes classical computing methods to their limits.
Things become even more challenging when we’re dealing with open-shell molecules, i.e., molecules that have unpaired electrons in their electronic configuration. Closed-shell molecules tend to have relatively simple, stable wavefunctions because all electrons are paired in orbitals. Open-shell systems are more complex. They can exhibit magnetic properties, high reactivity, and complex “multi-reference” electronic structures where we must rely on multiple wave functions to capture the full complexity. These characteristics make open-shell systems incredibly important in fields like combustion chemistry, catalysis, and atmospheric science — but also make them difficult to model accurately.
Open-shell species include radicals, transition metal complexes, and intermediate states in chemical reactions. Because their unpaired electrons create a rich but difficult-to-capture correlation structure, traditional classical methods often struggle with them — either oversimplifying the physics or requiring extensive computational resources.
This is precisely where quantum computing shows promise. The ability of quantum algorithms to directly encode and process the electron entanglement gives them an edge for tackling open-shell systems. When integrated into IBM’s quantum-centric supercomputing architecture — which combines quantum processors with powerful classical resources — these methods can help researchers simulate open-shell molecules with higher accuracy and scalability than ever before.
Ultimately, this progress supports a wide range of applications, from understanding reaction mechanisms in combustion engines to designing novel materials and molecular sensors. And it’s exactly this type of challenge that Lockheed Martin and IBM set out to address with the recent CH2 study.
…Why it Matters
Radical molecules are key players in aerospace, combustion chemistry, and sensor design. Modeling their behavior accurately can lead to better predictive models, more efficient chemical engines, and new sensing technologies capable of detecting minute traces of reactive species.
This study shows that quantum computers are starting to deliver value in real chemical simulations — not just toy problems or idealized systems. As quantum hardware continues to improve and methods like SQD mature, we’re opening the door to modeling complex reaction dynamics and designing better materials with the help of quantum tools.
To see the paper in The Journal of Chemical Theory and Computions go here, to see the full IBM blog on this topic go here.
