To study QOS on current quantum computing devices, an offline realization is developed that compiles sample blocks into empirical phase oracles and evaluates it using DNA fingerprinting.
Abstract
Classical-to-quantum I/O is a critical bottleneck for data-intensive quantum workloads. Quantum Oracle Sketching (QOS) has been proposed to address this problem. We identify its practical requirements, including the representation of data as a probability distribution over oracle addresses, the relation between sample count, approximation error, and update cost, and the coherence and runtime support required for online execution. To study QOS on current quantum computing devices, we develop an offline realization that compiles sample blocks into empirical phase oracles and evaluate it using DNA fingerprinting. The application converges with relatively few samples, while exact phase fusion combines repeated updates to the same address. However, the compiled circuits remain too deep for current quantum computing devices. Practical QOS therefore requires lower depth oracle synthesis and interactive quantum runtimes.
Sample-based Krylov quantum diagonalization is analyzed under depolarizing noise, deriving shot-count resource estimates that expose an exponential depth penalty, and experiments on current noisy hardware with the one-dimensional single-impurity Anderson model on a 20-site (40-qubit) instance are performed.
M. Grandadam, V. Bohun, Dikshant Dulal et al.· 0 citations
Primitive quantum speedups are interface-relative: they depend on the input access used to run the primitive and on the output contract used to consume its state or samples. This paper introduces a transcript-level admissibility relation \(A_M\preceq_{\mathrm{int}}A_Q\), defined relative to the declared implementation...
Pablo Herrero Gómez, A. Morenilla, David Muñoz Hernández et al.· 0 citations
Encoding classical data into quantum systems is a foundational step in the execution of nearly all quantum algorithms, and a critical bottleneck in realizing practical quantum advantage. This review provides a comprehensive account of the concepts, algorithms, and practical considerations associated with quantum data e...
Xiao-Ming Zhang, Arthur G. Rattew, Bu-Jiao Wu et al.· 2 citations
LLQM (Low-Level Quantum Machine), a profiling-driven meta-framework for quantum-classical pipelines that decomposes pipelines into fine-grained tasks and continuously profiles their CPU/GPU, memory, QPU, and queue dependencies alongside real-time hardware states, is presented.
Ayush Bansal, Owen Cochell, Santiago Núñez-Corrales et al.· 0 citations
The Quantum Execution Locality Framework is introduced, a qualitative framework for characterizing hybrid quantum-classical workflows according to recurring dataflow structures and quantum execution locality, the extent to which computation remains resident on the Quantum Processing Unit (QPU) before host intervention...
Ryan Landfield, Jordan J. Winetrout, Michael A. Sandoval· 0 citations
Results show that FL enables high-performing, privacy-preserving quantum-classical collaboration without centralizing raw data, and achieves this with substantially fewer trainable parameters than the classical neural networks and random forest alternatives.
C. Cano, Daniel M. Jimenez-Gutierrez, Diego Sal et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
