Resource-efficient quantum algorithms for Hamiltonian subspace diagonalization can be applied to optimize the computational modeling of evolutionary trade-offs in antibiotic-resistant bacterial populations.
Adversarial Debate Score
42% 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...
- Optimizing and Comparing Quantum Resources of Statistical Phase Estimation and Krylov Subspace Diagonalization
We develop a framework that enables direct and meaningful comparison of two early fault-tolerant methods for the computation of eigenenergies, namely \gls{qksd} and \gls{spe}, within which both method...
- 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...
- Energy gap of quantum spin glasses: a projection quantum Monte Carlo study
The performance of quantum annealing for combinatorial optimization is fundamentally limited by the minimum energy gap \Delta encountered at quantum phase transitions. We investigate the scaling of \D...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.