arXiv subjects

Justin Elenewski

Publications and source records attributed to Justin Elenewski.

5 recordsLinked to original sources

arXiv2026

Harmonic sequence state-preparation

We demonstrate an efficient circuit to prepare a quantum state with amplitudes proportional to a harmonic sequence. We do this by first preparing a large quantum state with linearly related amplitudes and then applying a quantum Fourier transform; this has a direct analogy to the fact that the Fourier coefficients of a sawtooth wave follow a harmonic sequence. We then consider an extension of this problem by block-encoding a matrix with a harmonic sequence along its diagonal. The cost of both circuits is dominated by the costs associated with the quantum Fourier transform.

quant-ph↗

arXiv2025

Efficient block-encodings require structure

Block-encodings are ubiquitous in quantum computing as a way to represent data within a unitary operator. While several unstructured methods are applicable to arbitrary data, these techniques are burdened by hidden costs and poor accuracy. In this paper, we demonstrate that, even for a small 6-qubit encoding, these structure-agnostic techniques require wildly intractable resources. We compare these resources with an encoding method which respects a mathematical representation of the block, leading to the conclusion that unstructured encoding mehtods should only be used in the most extenuating circumstances. This finding runs contrary to existing literature on quantum algorithms which often employ structure-agnostic methods.

quant-ph↗

arXiv2025

A simpler Gaussian state-preparation

The ability to efficiently state-prepare Gaussian distributions is critical to the success of numerous quantum algorithms. The most popular algorithm for this subroutine (Kitaev-Webb) has favorable polynomial resource scaling, however it faces enormous resource overheads making it functionally impractical. In this paper, we present a new, more intuitive method which uses exactly $n-1$ rotations, $(n-1)(n-2)/2$ two-qubit controlled rotations, and $\lfloor(n-1)/2\rfloor$ ancilla to state-prepare an $n$-qubit Gaussian state. We then apply optimizations to the circuit to render it linear in T-depth. This method can be extended to state-preparations of complex functions with polynomial phase.

quant-ph↗

arXiv2024

Quantum computing for corrosion-resistant materials and anti-corrosive coatings design

Corrosion is a pervasive issue that impacts the structural integrity and performance of materials across various industries, imposing a significant economic impact globally. In fields like aerospace and defense, developing corrosion-resistant materials is critical, but progress is often hindered by the complexities of material-environment interactions. While computational methods have advanced in designing corrosion inhibitors and corrosion-resistant materials, they fall short in understanding the fundamental corrosion mechanisms due to the highly correlated nature of the systems involved. This paper explores the potential of leveraging quantum computing to accelerate the design of corrosion inhibitors and corrosion-resistant materials, with a particular focus on magnesium and niobium alloys. We investigate the quantum computing resources required for high-fidelity electronic ground-state energy estimation (GSEE), which will be used in our hybrid classical-quantum workflow. Representative computational models for magnesium and niobium alloys show that 2292 to 38598 logical qubits and $(1.04$ to $1962) \times 10^{13}$ T-gates are required for simulating the ground-state energy of these systems under the first quantization encoding using plane waves basis.

quant-ph↗

arXiv2019

Optimal transport and colossal ionic mechano-conductance in graphene crown ethers

Biological ion channels balance electrostatic and dehydration effects to yield large ion selectivities alongside high transport rates. These macromolecular systems are often interrogated through point mutations of their pore domain, limiting the scope of mechanistic studies. In contrast, we demonstrate that graphene crown ether pores afford a simple platform to directly investigate optimal ion transport conditions, i.e., maximum current densities and selectivity. Crown ethers are known for selective ion adsorption. When embedded in graphene, however, transport rates lie below the drift-diffusion limit. We show that small pore strains -- 1 % -- give rise to a colossal -- 100 % -- change in conductance. This process is electromechanically tunable, with optimal transport in a primarily diffusive regime, tending toward barrierless transport, as opposed to a knock-on mechanism. Measurements of mechanical current modulation will yield direct information on the local electrostatic conditions of the pore. These observations suggest a novel setup for nanofluidic devices while giving insight into the physical foundation of evolutionarily--optimized ion transport in biological pores.

cond-mat.mes-hall↗