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Life Sciences Giant

Case Studies

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.
 

Problem

Despite strong theoretical promise, most quantum folding algorithms fail to scale in practice because they are incompatible with the noise, connectivity, and depth constraints of today’s quantum hardware.

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.

Solution:

Haiqu redesigned quantum folding algorithms to run efficiently on real hardware by aligning algorithm structure with device constraints rather than idealized assumptions.

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.

Result:

Haiqu scaled quantum protein folding workloads to 120 qubits, cut circuit depth (by 89%) and two-qubit gates (by 73%), and identified optimal low-energy solutions using ~50 minutes of QPU time vs. ~12 hours classically.

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.

Screenshot 2026 01 13 at 13 49 03
With Haiqu's hardware-efficient algorithm tailored to the QPU topology, we can solve the mRNA folding problem using all of the available qubits of the device (up to 159 on Heron). Running the iterations of the underlying optimisation problem takes 50 min of QPU time, whereas performing the same training on the tensor-network-based quantum simulator would require more than 12 hours.
Impact:

Haiqu transforms quantum protein and mRNA folding from experimental research into a practical, near-term capability for life-science organizations.

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.