Resource-efficient quantum algorithms for Hamiltonian subspace diagonalization can be adapted to model evolutionary fitness trade-offs in antibiotic-resistant bacteria, optimizing predictions of resistance development.
Adversarial Debate Score
45% survival rate under critique
Model Critiques
Supporting Research Papers
- Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization
Quantum algorithms for selecting a subspace of Hamiltonians to diagonalize have emerged as a promising alternative to variational algorithms in the NISQ era. So far, such algorithms, which include the...
- Exploiting evolutionary trade-offs to combat antibiotic resistance
Antibiotic resistance frequently evolves through fitness trade-offs in which the genetic alterations that confer resistance to a drug can also cause growth defects in resistant cells. Here, through ex...
- The Fitness Cost of Antibiotic Resistance: A Critical Factor in Bacterial Adaptation
Antibiotic resistance often incurs fitness costs that can impair bacterial growth, competitiveness, or adaptability in drug-free environments. However, these disadvantages are frequently offset by com...
- Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obta...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.