arXiv subjects

Renata Wong

Publications and source records attributed to Renata Wong.

8 recordsLinked to original sources

arXiv2026

Quantum circuit synthesis for fermionic excitations in coupled cluster theory using the Jordan-Wigner mapping

This work provides a quantum-computing-first derivation of the Unitary Coupled Cluster ansatz, showing that its structure emerges naturally from fermionic algebra under unitary constraints. By explicitly connecting second quantization, Jordan-Wigner mapping, and circuit synthesis, we clarify conceptual gaps between quantum chemistry and quantum computing implementations, particularly regarding operator locality, commutation structure, and hardware realization.

quant-ph↗

arXiv2025

A Quantum Bluestein's Algorithm for Arbitrary-Size Quantum Fourier Transform

We propose a quantum analogue of Bluestein's algorithm (QBA) that implements an exact $N$-point Quantum Fourier Transform (QFT) for arbitrary $N$. Our construction factors the $N$-dimensional QFT unitary into three diagonal quadratic-phase gates and two standard radix-2 QFT subcircuits of size $M = 2^m$ (with $M \ge 2N - 1$). This achieves asymptotic gate complexity $O((\log N)^2)$ and uses $O(\log N)$ qubits, matching the performance of a power-of-two QFT on $m$ qubits while avoiding the need to embed into a larger Hilbert space. We validate the correctness of the algorithm through a concrete implementation in Qiskit and classical simulation, confirming that QBA produces the exact $N$-point discrete Fourier transform on arbitrary-length inputs.

quant-ph↗

arXiv2025

Support Vector Machine Kernels as Quantum Propagators

Selecting optimal kernels for regression in physical systems remains a challenge, often relying on trial-and-error with standard functions. In this work, we establish a mathematical correspondence between support vector machine kernels and quantum propagators, demonstrating that kernel efficacy is determined by its spectral alignment with the system's Green's function. Based on this isomorphism, we propose a unified, physics-informed framework for kernel selection and design. For systems with known propagator forms, we derive analytical selection rules that map standard kernels to physical operators. For complex systems where the Green's function is analytically intractable, we introduce a constructive numerical method using the Kernel Polynomial Method with Jackson smoothing to generate custom, physics-aligned kernels. Numerical experiments spanning electrical conductivity, electronic band structure, anharmonic oscillators, and photonic crystals demonstrate that this framework consistently performs well as long as there is an alignment with a Green's function.

quant-ph↗

arXiv2023

Making the zeroth-order process fidelity independent of state preparation and measurement errors

In this work, we demonstrate that the zero-fidelity, an approximation to the process fidelity, when combined with randomized benchmarking, becomes robust to state preparation and measurement (SPAM) errors. However, as randomized benchmarking requires randomly choosing an increasingly large number of Clifford elements from the Clifford group when the qubit number increases, this combination is also limited to quantum systems with up to three qubits. To make the zero-fidelity independent of SPAM errors and, at the same time, applicable to multi-qubit systems, we employ a channel noise scaling method similar to the method of global unitary folding, or identity scaling, used for quantum error mitigation.

quant-ph↗

arXiv2023

Quantum Speedup for the Maximum Cut Problem

Given an undirected, unweighted graph with $n$ vertices and $m$ edges, the maximum cut problem is to find a partition of the $n$ vertices into disjoint subsets $V_1$ and $V_2$ such that the number of edges between them is as large as possible. Classically, it is an NP-complete problem, which has potential applications ranging from circuit layout design, statistical physics, computer vision, machine learning and network science to clustering. In this paper, we propose a quantum algorithm to solve the maximum cut problem for any graph $G$ with a quadratic speedup over its classical counterparts, where the temporal and spatial complexities are reduced to, respectively, $O(\sqrt{2^n/r})$ and $O(m^2)$. With respect to oracle-related quantum algorithms for NP-complete problems, we identify our algorithm as optimal. Furthermore, to justify the feasibility of the proposed algorithm, we successfully solve a typical maximum cut problem for a graph with three vertices and two edges by carrying out experiments on IBM's quantum computer.

quant-ph↗

arXiv2020

Reference Frame Transformations and Quantization

It has been said that Maxwell's theory of electromagnetic field is relativistic as Einstein showed that these axioms of Maxwell are all Lorentz invariant. We investigate some issues regarding these results.

physics.gen-ph↗

arXiv2020

Logical Analysis of Relativity Theory

We review the special theory of relativity kinematics and dynamics as well as the general theory of relativity under the aspect of logic.

physics.hist-ph↗

arXiv2018

An Implementation, Empirical Evaluation and Proposed Improvement for Bidirectional Splitting Method for Argumentation Frameworks under Stable Semantics

Abstract argumentation frameworks are formal systems that facilitate obtaining conclusions from non-monotonic knowledge systems. Within such a system, an argumentation semantics is defined as a set of arguments with some desired qualities, for example, that the elements are not in conflict with each other. Splitting an argumentation framework can efficiently speed up the computation of argumentation semantics. With respect to stable semantics, two methods have been proposed to split an argumentation framework either in a unidirectional or bidirectional fashion. The advantage of bidirectional splitting is that it is not structure-dependent and, unlike unidirectional splitting, it can be used for frameworks consisting of a single strongly connected component. Bidirectional splitting makes use of a minimum cut. In this paper, we implement and test the performance of the bidirectional splitting method, along with two types of graph cut algorithms. Experimental data suggest that using a minimum cut will not improve the performance of computing stable semantics in most cases. Hence, instead of a minimum cut, we propose to use a balanced cut, where the framework is split into two sub-frameworks of equal size. Experimental results conducted on bidirectional splitting using the balanced cut show a significant improvement in the performance of computing semantics.

cs.AI↗