Jaime Park | Searcharxiv

arXiv2025

Bloch oscillation with a diatomic tight-binding model on quantum computers

We aim to explore a more efficient way to simulate few-body dynamics on quantum computers. Instead of mapping the second quantization of the system Hamiltonian to qubit Pauli gates representation via the Jordan-Wigner transform, we propose to use the few-body Hamiltonian matrix under the statevector basis representation which is more economical on the required number of quantum registers. For a single-particle excitation state on a one-dimensional chain, $\Gamma$ qubits can simulate $N=2^\Gamma$ number of sites, in comparison to $N$ qubits for $N$ sites via the Jordan-Wigner approach. A two-band diatomic tight-binding model is used to demonstrate the effectiveness of the statevector basis representation. Both one-particle and two-particle quantum circuits are constructed and some numerical tests on IBM hardware are presented.

quant-ph↗

arXiv2023

Fixed-point Grover Adaptive Search for Quadratic Binary Optimization Problems

We study a Grover-type method for Quadratic Unconstrained Binary Optimization (QUBO) problems. For an $n$-dimensional QUBO problem with $m$ nonzero terms, we construct a marker oracle for such problems with a tuneable parameter, $\Lambda \in \left[ 1, m \right] \cap \mathbb{Z}$. At $d \in \mathbb{Z}_+$ precision, the oracle uses $O (n + \Lambda d)$ qubits, has total depth of $O \left( \tfrac{m}{\Lambda} \log_2 (n) + \log_2 (d) \right)$, and non-Clifford depth of $O \left( \tfrac{m}{\Lambda} \right)$. Moreover, each qubit required to be connected to at most $O \left( \log_2 (\Lambda + d) \right)$ other qubits. In the case of a maximum graph cuts, as $d = 2 \left\lceil \log_2 (n) \right\rceil$ always suffices, the depth of the marker oracle can be made as shallow as $O (\log_2 (n))$. For all values of $\Lambda$, the non-Clifford gate count of these oracles is strictly lower (at least by a factor of $\sim 2$) than previous constructions. Furthermore, we introduce a novel \textit{Fixed-point Grover Adaptive Search for QUBO Problems}, using our oracle design and a hybrid Fixed-point Grover Search, motivated by the works of Boyer et al. and Li et al. This method has better performance guarantees than previous Grover Adaptive Search methods. Some of our results are novel and useful for any method based on Fixed-point Grover Search. Finally, we give a heuristic argument that, with high probability and in $O \left( \tfrac{\log_2 (n)}{\sqrt{\epsilon}} \right)$ time, this adaptive method finds a configuration that is among the best $\epsilon 2^n$ ones.

quant-ph↗