Enabling Quantum Chemistry for Drug Discovery with Haiqu

The pharmaceutical industry seeks drugs with high potency and precise selectivity to improve efficacy, reduce side effects, and lower late-stage failure rates. Targeted covalent drugs are especially promising because they form a specific, irreversible bond with a target protein via a reactive chemical group called a warhead. When properly tuned, this mechanism delivers exceptional potency and durability—aspirin being one of the earliest examples. The challenge is predicting warhead reactivity: higher reactivity generally improves potency, but excessive reactivity undermines selectivity. Accurately balancing this trade-off remains a major bottleneck in drug discovery.

With R&D costs exceeding $2B per drug, pharma increasingly combines machine learning and computational chemistry to accelerate discovery. A powerful approach uses first-principles calculations to generate “quantum fingerprints”—physically grounded features that improve reactivity prediction. However, classical simulation methods scale poorly: they rely on approximations that are either too inaccurate to capture critical many-body effects or too expensive for practical screening.

Quantum computing offers a solution, but until now has been constrained by hardware noise, limiting usable circuit depth to a few hundred two-qubit gates. Haiqu, working with Capgemini, IBM, and GSK, broke this barrier by demonstrating one of the largest electronic-structure Hamiltonian simulations ever run on real quantum hardware for covalent drug warheads. Using advanced circuit compression and middleware execution, the team initially reduced circuit depth by 15.5× and further allowed end-to-end execution by running sub-circuits up to 371 gates.

Collectively, these results establish a scalable, hardware-realistic path for running Hamiltonian simulations on larger active spaces, while maintaining sufficient accuracy for molecular reactivity prediction.

For decision makers, the implications are clear and immediate: Haiqu dramatically lowers the cost and increases the performance of quantum chemistry workloads, transforming quantum computing from a long-term theoretical research bet into a near-term commercial piloting program on real quantum hardware. By making deep Hamiltonian simulations feasible on today’s quantum hardware, Haiqu enables pharmaceutical teams to:

Crucially, this is not a promise for the next decade. Haiqu makes high-value quantum workloads commercially viable today, allowing enterprises to capture competitive advantage years ahead of hardware-only roadmaps.

Quantum for business. Run more with Haiqu.

Explore the full research paper.

Folding mRNA on 120 qubits

Biology is governed by the relationship between molecular form and biological function. Cellular processes such as signaling, metabolism, and regulation depend on proteins that are precisely folded to perform specific tasks.

Fundamentally, protein folding is an optimization problem with exponential complexity, making accurate, physics-based simulations impractical at scale on classical computers. As proteins grow larger, computational costs rise rapidly, limiting the use of traditional methods in drug discovery and molecular design.

Quantum computing offers a new approach by mapping protein folding to a problem that quantum algorithms are naturally suited to solve. Techniques such as variational quantum algorithms can directly search for low-energy configurations (the biologically relevant folded states) within vast and complex solution spaces.
 

A common industry-wide bottleneck in applying quantum computing to real-world optimization problems is the mismatch between algorithm design and current hardware limits. Many promising algorithms assume ideal connectivity and require deep, noisy circuits, making them impractical on today’s quantum devices where errors accumulate before convergence. This keeps most demonstrations confined to small, non-industrial benchmarks.

Haiqu addressed this challenge by redefining folding at scale on today’s quantum hardware. This included applying Haiqu’s topology-aware quantum circuits, lightweight error-mitigation techniques, and integrated classical pre- and post-processing to stabilize training and improve results.

By reengineering how the algorithm runs on real quantum processors, Haiqu enabled execution at 120 qubits (51 nucleotides) while cutting circuit depth from 177 to 20 and reducing two-qubit gates from 479 to 127. Using this approach, Haiqu successfully trained and executed the algorithm directly on a quantum processor, achieving the optimal folding solution in approximately 50 minutes of QPU time—compared to roughly 12 hours on a classical simulator. This work scaled prior efforts of a partner to 120 qubits and established a credible path to ~200-qubit problem sizes, aligned with next-generation quantum hardware roadmaps.

For decision makers, the implications are immediate. Haiqu reduces cost and increases the practical performance of quantum folding workloads, shifting quantum computing from a long-term research investment to a hardware-backed piloting opportunity. By making large-scale energy minimization and folding simulations feasible on today’s quantum processors, Haiqu enables life-science teams to:

• Explore larger and more realistic folding landscapes than are accessible with classical physics-based methods
   
• Identify low-energy folding configurations that are difficult to obtain with existing optimization techniques
   
• Integrate quantum-derived folding results into existing computational biology and machine-learning workflows
   
• Accelerate early-stage discovery and design decisions while controlling computational cost and hardware usage

Crucially, this capability is available now. Haiqu enables meaningful protein and mRNA folding workloads on current quantum hardware, allowing organizations to build expertise, validate value, and establish early competitive advantage ahead of future hardware advances.

Quantum for business. Run more with Haiqu.