arXiv subjects

Dikshant Dulal

Publications and source records attributed to Dikshant Dulal.

7 recordsLinked to original sources

arXiv2026

Reducing quantum and classical resources for quantum-centric supercomputing workloads on near-term hardware

Sample-based Krylov quantum diagonalization (SKQD) is a paradigmatic example of a quantum-centric supercomputing workflow that combines the convergence structure of Krylov quantum diagonalization with classical sampling-based post-processing. It provides convergence guarantees assuming that the important computational-basis configurations can be sampled from a set of Krylov states with sufficient probability. We analyze this assumption under depolarizing noise, deriving shot-count resource estimates that expose an exponential depth penalty, and perform experiments on current noisy hardware with the one-dimensional single-impurity Anderson model on a 20-site (40-qubit) instance. Device noise breaks the practical convergence predicted by the noiseless SKQD analysis, but approximate compilation techniques can compress the Krylov time-evolution circuits before execution. The compressed circuits accumulate less hardware noise, recover the expected energy convergence, and reduce both the quantum shot budget and the classical subspace dimension, remaining beneficial even when classical configuration recovery is applied.

quant-ph↗

arXiv2026

Enhancing Quantum Diffusion Models for Complex Image Generation

Quantum generative models offer a novel approach to exploring high-dimensional Hilbert spaces but face significant challenges in scalability and expressibility when applied to multi-modal distributions. In this study, we explore a Hybrid Quantum-Classical U-Net architecture integrated with Adaptive Non-Local Observables (ANO) as a potential solution to these hurdles. By compressing classical data into a dense quantum latent space and utilizing trainable observables, our model aims to extract non-local features that complement classical processing. We also investigate the role of Skip Connections in preserving semantic information during the reverse diffusion process. Experimental results on the full MNIST dataset (digits 0-9) demonstrate that the proposed architecture is capable of generating structurally coherent and recognizable images for all digit classes. While hardware constraints still impose limitations on resolution, our findings suggest that hybrid architectures with adaptive measurements provide a feasible pathway for mitigating mode collapse and enhancing generative capabilities in the NISQ era.

quant-ph↗

arXiv2025

Accelerating Transpilation in Quantum Machine Learning with Haiqu's Rivet-transpiler

Transpilation is a crucial process in preparing quantum circuits for execution on hardware, transforming virtual gates to match device-specific topology by introducing swap gates and basis gates, and applying optimizations that reduce circuit depth and gate count, particularly for two-qubit gates. As the number of qubits increases, the cost of transpilation escalates significantly, especially when trying to find the optimal layout with minimal noise under the qubit connectivity constraints imposed by device topology. In this work, we use the Rivet transpiler, which accelerates transpilation by reusing previously transpiled circuits. This approach is relevant for cases such as quantum chemistry, where multiple Pauli terms need to be measured by appending a series of rotation gates at the end for non-commuting Paulis, and for more complex cases when quantum circuits need to be modified iteratively, as occurs in quantum layerwise learning. We demonstrate up to 600% improvement in transpilation time for quantum layerwise learning using the Rivet transpiler compared to standard transpilation without reuse.

quant-ph↗

arXiv2024

Efficient Quantum Gradient and Higher-order Derivative Estimation via Generalized Hadamard Test

In the context of Noisy Intermediate-Scale Quantum (NISQ) computing, parameterized quantum circuits (PQCs) represent a promising paradigm for tackling challenges in quantum sensing, optimal control, optimization, and machine learning on near-term quantum hardware. Gradient-based methods are crucial for understanding the behavior of PQCs and have demonstrated substantial advantages in the convergence rates of Variational Quantum Algorithms (VQAs) compared to gradient-free methods. However, existing gradient estimation methods, such as Finite Difference, Parameter Shift Rule, Hadamard Test, and Direct Hadamard Test, often yield suboptimal gradient circuits for certain PQCs. To address these limitations, we introduce the Flexible Hadamard Test, which, when applied to first-order gradient estimation methods, can invert the roles of ansatz generators and observables. This inversion facilitates the use of measurement optimization techniques to efficiently compute PQC gradients. Additionally, to overcome the exponential cost of evaluating higher-order partial derivatives, we propose the $k$-fold Hadamard Test, which computes the $k^{th}$-order partial derivative using a single circuit. Furthermore, we introduce Quantum Automatic Differentiation (QAD), a unified gradient method that adaptively selects the best gradient estimation technique for individual parameters within a PQC. This represents the first implementation, to our knowledge, that departs from the conventional practice of uniformly applying a single method to all parameters. Through rigorous numerical experiments, we demonstrate the effectiveness of our proposed first-order gradient methods, showing up to an $O(N)$ factor improvement in circuit execution count for real PQC applications. Our research contributes to the acceleration of VQA computations, offering practical utility in the NISQ era of quantum computing.

quant-ph↗

arXiv2023

Atomically-precise Vacancy-assembled Quantum Antidots

Patterning antidots ("voids") into well-defined antidot lattices creates an intriguing class of artificial structures for the periodic modulation of 2D electron systems, leading to anomalous transport properties and exotic quantum phenomena as well as enabling the precise bandgap engineering of 2D materials to address technological bottleneck issues. However, realizing such atomic-scale quantum antidots (QADs) is infeasible by current nanolithographic techniques. Here, we report an atomically-precise bottom-up fabrication of a series of atomic-scale QADs with elegantly engineered quantum states through a controllable assembly of a chalcogenide single vacancy (SV) in 2D PtTe2, a type-II Dirac semimetal. Te SVs as atomic-scale "antidots" undergo thermal migration and assembly into highly-ordered SV lattices spaced by a single Te atom, reaching the ultimate downscaling limit of antidot lattices. Increasing the number of SVs in QADs strengthens the cumulative repulsive potential and consequently enhances collective interference of multiple-pocket scattered quasiparticles inside QADs, creating multi-level quantum hole states with tunable gap from telecom to far-infrared regime. Moreover, precisely engineered quantum hole states of QADs are symmetry-protected and thus survive upon atom-by-atom oxygen substitutional doping. Therefore, SV-assembled QADs exhibit unprecedented robustness and property tunability, which not only holds the key to their future applications but also embody a wide variety of material technologies.

cond-mat.mes-hall↗

arXiv2021

Gate-tunable artificial nucleus in graphene

We report an atomically-precise integration of individual nitrogen (N) dopant as an in-plane artificial nucleus in a graphene device by atomic implantation to probe its gate-tunable quantum states and correlation effects. The N dopant creates the characteristic resonance state in the conduction band, revealing a giant carrier-dependent energetic renormalization up to 350 meV with respect to the Dirac point, accompanied by the observation of long-range screening effects. Joint density functional theory and tight-binding calculations with modified perturbation potential corroborate experimental findings and highlight the short-range character of N-induced perturbation.

cond-mat.mtrl-sci↗

arXiv2021

Electronic Self-passivation of Single Vacancy in Black Phosphorus via a Controlled Ionization

We report that mono-elemental black phosphorus presents a new electronic self-passivation scheme of single vacancy (SV). By means of low-temperature scanning tunneling microscopy and bond-resolved non-contact atomic force microscopy, we demonstrate that the local reconstruction and ionization of SV into negatively charged $\mathrm{SV}^-$ leads to the passivation of dangling bonds and thus the quenching of in-gap states, which can be achieved by mild thermal annealing or STM tip manipulation. SV exhibits a strong and symmetric Friedel oscillation (FO) pattern, while $\mathrm{SV}^-$ shows an asymmetric FO pattern with local perturbation amplitude reduced by one order of magnitude and a faster decay rate. The enhanced passivation by forming $\mathrm{SV}^-$ can be attributed to its weak dipole-like perturbation, consistent with density-functional theory and numerical calculations. Therefore, self-passivated $\mathrm{SV}^-$ is electronically benign and acts as a much weaker scattering center, which may hold the key to further enhance the charge mobility of BP and its analogs.

cond-mat.mtrl-sci↗