Breaking through computational barriers to achieve chemical accuracy in molecular simulations
For decades, chemists have faced a fundamental limitation: the mathematical complexity of precisely modeling molecules quickly overwhelms even the most powerful supercomputers. Understanding exactly how molecules behave—crucial for developing new medicines, materials, and energy solutions—requires solving equations so complex that even modern supercomputers reach their limits with relatively small molecules. This isn't merely a computational speed issue; it's a fundamental barrier rooted in the exponential growth of possible interactions between electrons as molecular size increases .
Enter quantum computing—a revolutionary approach that harnesses the strange laws of quantum mechanics to solve problems that are practically impossible for classical computers. In a landmark 2020 study published in Science, researchers from Google AI Quantum and collaborators achieved what many had been working toward for years: they successfully performed quantum chemical simulations on a superconducting quantum processor, demonstrating the potential of quantum computers to transform theoretical chemistry and materials science 8 .
Traditional computers use bits that can be either 0 or 1. Quantum computers, however, use quantum bits (qubits) that can exist as 0, 1, or both simultaneously through a phenomenon called superposition 6 .
When multiple qubits become interconnected through quantum entanglement, they can represent and process vast amounts of information in parallel that would require enormous resources to simulate classically 4 .
The variational quantum eigensolver (VQE) has emerged as a particularly promising algorithm for near-term quantum devices, using a hybrid strategy that partners quantum processors with classical computers 8 .
Quantum computers are particularly well-suited to chemical simulation because molecules themselves are quantum systems. Their electrons don't follow deterministic paths but rather exist in clouds of probability—quantum states that conventional computers struggle to represent efficiently.
"A classical computer is a quantum computer... so we shouldn't be asking about 'where do quantum speedups come from?' We should say, 'well, all computers are quantum... Where do classical slowdowns come from?'" 6
Quantum computers can represent molecular states that would require exponentially growing classical resources
Using Google's Sycamore quantum processor—the same chip that had previously demonstrated quantum supremacy—researchers performed quantum simulations of two chemically significant systems: hydrogen chains (as large as H₁₂) and the isomerization process of diazene 8 . What made this achievement remarkable wasn't merely that these calculations were performed on a quantum computer, but that they reached chemical accuracy—the precision threshold required for meaningful predictions in chemistry and materials science 8 .
The precision needed for meaningful chemical predictions, typically around 1.6 kcal/mol or 40 milliHartree
12-qubit quantum simulation achieved this thresholdRepresent a fundamental test case that scales in complexity, allowing researchers to methodically test their methods from simple to more challenging systems.
With its nitrogen-nitrogen double bond, undergoes isomerization (structural rearrangement) that serves as a model for more complex chemical reactions.
Mastering these simulations provides a blueprint for tackling increasingly complex chemical systems, from medicinal compounds to advanced catalytic materials 8 .
The molecular structures were translated into mathematical representations compatible with quantum hardware using the Hartree-Fock method as a starting point 8 .
Each molecule required designing specific quantum circuits that could prepare and measure the relevant quantum states. For the largest systems, this involved up to 72 two-qubit gates—the fundamental operations that manipulate quantum information 8 .
Since current quantum processors are prone to errors, the team implemented advanced error suppression techniques, including post-selection and McWeeny purification, to enhance the reliability of their results 8 .
The VQE algorithm worked iteratively, with the quantum processor preparing and measuring quantum states while a classical computer analyzed those results and suggested improvements for the next quantum computation 8 .
The experimental results demonstrated that quantum computers could indeed achieve the precision required for practical chemistry applications. For the diazene isomerization pathway, the quantum simulation achieved an accuracy of 40 milliHartree—surpassing the chemical accuracy threshold needed for meaningful predictions of molecular behavior and reaction pathways 8 .
Similarly, for the H₁₂ hydrogen chain, the team successfully modeled the binding energy curve, capturing how the energy changes as the hydrogen atoms move relative to each other. This capability to model potential energy surfaces is fundamental to predicting how molecules will interact, react, and transform—the very essence of chemistry 8 .
| Molecular System | Size (Qubits Used) | Number of Quantum Gates | Key Chemical Property Studied |
|---|---|---|---|
| Hydrogen Chains (H₁₂) | 12 qubits | Up to 72 two-qubit gates | Binding energy curve |
| Diazene Isomerization | 12 qubits | Up to 72 two-qubit gates | Reaction pathway energy profile |
| Error Mitigation Method | Key Principle | Impact on Simulation Fidelity |
|---|---|---|
| Post-selection | Filtering out measurements that indicate errors occurred | Significant improvement in result reliability |
| McWeeny purification | Mathematical technique to improve wavefunction quality | Enhanced accuracy of energy measurements |
| Combined approach | Using multiple mitigation strategies together | Over 98% fidelity for most simulations |
The VQE algorithm showed superior performance for near-term quantum devices, achieving chemical accuracy with noisy hardware.
| Component Category | Specific Tools/Methods | Function in the Experiment |
|---|---|---|
| Hardware | Google Sycamore processor (12 qubits) | Physical execution of quantum circuits |
| Quantum Algorithms | VQE (Variational Quantum Eigensolver) | Hybrid quantum-classical approach to find molecular energies |
| Error Mitigation | Post-selection, McWeeny purification | Improving result quality from noisy quantum hardware |
| Classical Support | Conventional computing cluster | Analysis, optimization, and coordination with quantum processor |
The Sycamore processor represents the superconducting qubit approach to quantum computing, where tiny circuits cooled to extremely low temperatures exhibit quantum behavior. Unlike alternative approaches using trapped ions or neutral atoms, superconducting processors can be manufactured using techniques adapted from conventional chip fabrication, potentially offering a path to scaling 1 8 .
The experiment relied on several sophisticated software components:
Rather than waiting for perfect quantum hardware, the team employed practical error mitigation techniques specifically designed for the Noisy Intermediate-Scale Quantum (NISQ) era.
"The Quantum Error Correction (QEC) Era is here. We've seen increased global alignment on the necessity of QEC to remove faults in quantum computing and help achieve useful scale" 3
While the 12-qubit experiments marked a significant milestone, the field has progressed rapidly. By 2025, companies like IBM had developed processors with over 1,000 qubits, while Rigetti achieved 99.5% fidelity on 84-qubit systems 1 7 . This progress enables simulations of increasingly complex molecular systems, from protein folding to novel material design 7 .
The implications extend across multiple industries:
Researchers are exploring quantum computing for designing corrosion-resistant materials, with global corrosion costs estimated at $2.5 trillion annually .
From improving battery technology to optimizing power grids, quantum chemistry simulations offer pathways to more efficient energy systems 7 .
Quantum simulations could help design more efficient catalysts for carbon capture and conversion, addressing climate change challenges.
"The era of the unknown in quantum is over, and the race is kicking off. For the first time, quantum computing's 'ChatGPT' moment is within fighting distance" 3
The successful demonstration of chemically accurate simulations on a 12-qubit quantum processor represents more than a technical achievement—it validates a new paradigm for computational chemistry. While traditional computers will continue to handle many chemical calculations, quantum computers now offer a viable path for tackling problems that have remained stubbornly out of reach.
As quantum hardware continues to advance, with companies like Google, IBM, and Rigetti pushing the boundaries of scale and fidelity, the lessons learned from these early 12-qubit experiments provide the foundation for increasingly sophisticated applications 1 7 . The transition from theoretical potential to practical utility is underway, opening a new chapter in our ability to understand and design the molecular world.
"A classical computer is a quantum computer... so we shouldn't be asking about 'where do quantum speedups come from?' We should say, 'well, all computers are quantum... Where do classical slowdowns come from?'" 6
In harnessing the quantum nature of matter to simulate quantum systems themselves, we are finally speaking chemistry's native language.