Insider Brief
- A new study demonstrates that hybrid quantum-classical methods can accurately simulate complex molecules using today’s quantum computers, marking a step forward for practical quantum chemistry.
- Researchers combined Density Matrix Embedding Theory (DMET) and Sample-Based Quantum Diagonalization (SQD) to simulate molecular systems like hydrogen rings and cyclohexane conformers using as few as 27 to 32 qubits on Cleveland Clinic’s IBM-managed quantum device.
- The DMET-SQD method produced results within 1 kcal/mol of classical benchmarks, showing potential for simulating biologically relevant molecules despite current hardware limitations.
- Image: IBM System One
Even the most advanced supercomputers struggle to simulate the stability and behavior of large molecules, phenomena with important implications for health and medicine.
A team of researchers has demonstrated a new way to simulate complex molecules using current-generation quantum computers, offering a viable path forward for quantum-centric scientific computing. According to a study published in the Journal of Chemical Theory and Computation, the researchers successfully combined two advanced techniques — Density Matrix Embedding Theory (DMET) and Sample-Based Quantum Diagonalization (SQD) — to simulate molecules with unprecedented accuracy using a hybrid classical-quantum approach.
The study, conducted by scientists from The Cleveland Clinic, Michigan State University, and IBM Quantum, appears to be the first time that the SQD algorithm has been integrated into the DMET framework and executed on actual quantum hardware. The work shows that it is now possible to use quantum computers to tackle larger and more chemically realistic molecules, such as the conformers of cyclohexane, without requiring fault-tolerant quantum systems.
Kenneth Merz, PhD, a researcher at the Center for Computational Life Sciences, the Cleveland Clinic and a lead author of the study, said the findings could open up new paths to medical advances.
“Current state quantum computers are extremely powerful, but they do not yet have error correction capabilities,” Dr. Merz said in a statement from the Clinic. “By combining the power of a quantum computer with the error correction capabilities of a supercomputer, we can start to simulate and predict how molecules behave enhancing our ability to understand and treat disease.”
Background on How Hybrid Computing Tackles Quantum Chemistry
Quantum chemistry aims to simulate how electrons interact within molecules—a task that demands massive computational power. Traditional classical methods often fall short when molecules grow large. Simulating insulin, for instance, would require tracking more than 33,000 molecular orbitals, which is beyond the reach of today’s high-performance computers. The challenge is especially acute for mean-field approximations, which treat electron interactions as averaged effects and ignore key correlations. Fragmentation-based techniques like DMET offer a workaround by breaking molecules into smaller, more manageable subsystems and embedding them within an approximate electronic environment.
In this study, the team enhanced this approach using the SQD algorithm, which relies on sampling quantum circuits and projecting results into a subspace for solving the Schrödinger equation. Instead of simulating the entire molecule directly on the quantum computer — a process that would require thousands of qubits — the DMET-SQD approach simulated only chemically relevant fragments, using between 27 and 32 qubits on IBM’s ibm_cleveland device, which is an on‑site, private, IBM‑managed quantum computer at Cleveland Clinic and, reportedly, the first of its kind dedicated to healthcare research in the U.S.
This division of labor between quantum and classical resources is emblematic of quantum-centric supercomputing (QCSC), in which the quantum processor focuses on the most computationally intensive parts while classical high-performance computers handle the rest.
Benchmarking Quantum Accuracy
To test the accuracy of their hybrid method, the researchers applied DMET-SQD to two test cases: a ring of 18 hydrogen atoms and various conformers of cyclohexane. The hydrogen ring serves as a standard benchmark in computational chemistry due to its high electron correlation effects at stretched bond lengths. Cyclohexane conformers — chair, boat, half-chair, and twist-boat — are a common testbed in organic chemistry because their relative energies lie within a narrow range of a few kilocalories per mole, making the problem sensitive to small computational errors.
The study compared the quantum-classical results with several classical methods, including Coupled Cluster Singles and Doubles with perturbative triples [CCSD(T)] and Heat-Bath Configuration Interaction (HCI), a method close to exact solutions. The DMET-SQD method produced energy differences between cyclohexane conformers that were within 1 kcal/mol of the best classical reference methods, a threshold considered acceptable for chemical accuracy.
The researchers reported that for sufficiently large samples of quantum configurations (on the order of 8,000 to 10,000), the DMET-SQD approach not only preserved the correct energy ordering of cyclohexane conformers but also matched HCI benchmarks for the hydrogen ring with minimal deviation.
Engineering and Error Mitigation
While the concept of embedding a fragment within a molecule is not new, what sets this study apart is the practical implementation on real quantum hardware. The SQD method, known for its tolerance to noise, helped mitigate common errors associated with today’s non-fault-tolerant quantum devices, according to the study. The authors used error mitigation techniques such as gate twirling and dynamical decoupling to further stabilize computations on IBM’s Eagle processor.
The research also relied on an interface that connected Qiskit’s implementation of SQD with the Tangelo library for DMET, requiring custom development by the team. Each quantum circuit used in the simulation encoded configurations derived from Hartree-Fock calculations and refined iteratively through a procedure called S-CORE to maintain the correct number of particles and spin characteristics.
Limitations and Forward Outlook
Work is just beginning for the method as the DMET-SQD method is not without limitations. The accuracy of the simulation depends on the size of the fragment and the quality of the quantum sampling. In systems with strong electronic correlation or subtle energy differences, insufficient sampling could lead to incorrect energy ordering. The current study also used a minimal basis set, which could be another limitation. Future chemically relevant applications will require more sophisticated basis sets, which in turn demand more qubits and better error control.
The authors of the study emphasize the need for further work to refine the sampling process and reduce the computational burden of classical post-processing. Improvements in quantum hardware — particularly in error rates and gate fidelity — will also play a significant role in making these simulations more robust and scalable.
Implications for Molecular Design
This study is an early indication that hybrid quantum-classical methods can handle realistic molecular systems beyond toy models. By reducing full-molecule simulations into smaller, tractable subproblems suitable for today’s quantum computers, researchers can start to explore problems in materials science and drug discovery that were previously out of reach.
The implications are significant, according to the team: if DMET-SQD or similar hybrid methods continue to scale, they could enable predictive simulations of protein-drug interactions, reaction mechanisms, or novel materials—all of which hinge on accurate descriptions of quantum effects in large molecules.
“This is a groundbreaking step in computational research that demonstrates how near-term quantum computers can advance biomedical research,” Dr. Merz said.
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