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Case Studies Capgemini

Capgemini, GSK & IBM

Case Studies

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.

Problem: Quantum chemistry workloads exceed today’s hardware limits in circuit depth, noise, and cost.

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.

Solution: Decomposed prohibitive quantum runs into hardware-friendly, separable blocks.

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.

Blog Website Graphics 1
Haiqu decomposes quantum circuits into hardware-friendly blocks, enabling quantum chemistry workloads (left panel). Executions with Haiqu middleware (blue squares, lower right panel) retain coherent signals and closely track ideal trajectories (grey crosses), while runs using Qiskit’s built-in error mitigation (orange squares) collapse toward a noise-dominated baseline (black dashed line).

Impact: With Haiqu, chemists build expertise ahead of broader hardware advances

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:

  1. Explore larger and more realistic molecular spaces
  2. Generate predictive quantum features unavailable to classical methods
  3. Integrate quantum simulations directly into machine-learning-driven discovery pipelines
  4. Accelerate early-stage drug discovery while reducing computational cost and increasing the success of the pilot experiments

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.