Wave-based dispatch strategies can be optimized using machine learning to improve the efficiency of quantum algorithms for Hamiltonian subspace diagonalization.
Adversarial Debate Score
55% 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...
- Wave-Based Dispatch for Circuit Cutting in Hybrid HPC--Quantum Systems
Hybrid High-performance Computing (HPC)-quantum workloads based on circuit cutting decompose large quantum circuits into independent fragments, but existing frameworks tightly couple cutting logic to ...
- Machine Learning Approaches to Building Quantum Circuits for Sets of Matrices
Machine learning nowadays becomes a useful instrument in many subjects. In this paper we use interpretable machine learning to build quantum algorithm. By studying the parameters of the machine learni...
- Towards High Performance Quantum Computing (HPQ): Parallelisation of the Hamiltonian Auto Decomposition Optimisation Framework (HADOF)
Practical applicability of quantum optimisation on near term devices is constrained by limited qubit counts and hardware noise, which restricts the scalability of quantum optimisation algorithms for c...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.