Quantum Computing for Photon-Drug Interactions in Cancer Treatment
summary
A full-stack pipeline targets photodynamic therapy by simulating the ruthenium photosensitizer TLD 1433 with hardware experiments on IBM Quantum at 50+ qubits and clinical input from Cleveland Clinic. A new classical circuit optimizer called mayorana propagation replaces days of tuning with hours or minutes, while post-processing combines quantum outputs with tensor-network DMRG to mitigate noise and drive energies toward a high-quality reference. Deep circuits on current devices show noisy, useless energies; shallow circuits plus tensor-network updates recover predictive ground and excited state gaps.
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chapters (10)
- 0:00Quantum chemistry in cancer
- 2:31Photodynamic therapy challenges
- 5:48Quantum computing for states
- 7:55Classical methods scaling
- 9:19Majorana propagation circuits
- 11:07Hybrid tensor networks
- 12:52Quantum drug discovery pipeline
- 14:41Noise mitigation results
- 15:44Quantum with AI pipelines
- 16:26Path to impact
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