Quick Navigation
Topics
Quantum Simulation
On Commutative Penalty Functions in Parent-Hamiltonian Constructions
arXiv
Authors: Jacob Biamonte
Year
2023
Paper ID
6498
Status
Preprint
Abstract Read
~2 min
Abstract Words
163
Citations
N/A
Abstract
There are several known techniques to construct a Hamiltonian with an expected value that is minimized uniquely by a given quantum state. Common approaches include the parent Hamiltonian construction from matrix product states, building approximate ground state projectors, and, in a common case, developing penalty functions from the generalized Ising model. Here we consider the framework that enables one to engineer exact parent Hamiltonians from commuting polynomials. We derive elementary classification results of quadratic Ising parent Hamiltonians and to generally derive a non-injective parent Hamiltonian construction. We also consider that any n-qubit stabilizer state has a commutative parent Hamiltonian with n+1 terms and we develop an approach that allows the derivation of parent Hamiltonians by composition of network elements that embed the truth tables of discrete functions into a kernel space. This work presents a unifying framework that captures components of what is known about exact parent Hamiltonians and bridges a few techniques across the domains that are concerned with such constructions.
Why This Paper Matters
- This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- There are several known techniques to construct a Hamiltonian with an expected value that is minimized uniquely by a given quantum state.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.