197 citations · 341 across the 10 of their papers we have counts for
10 papers
Towards Scaling Quantum Fine-Tuning of Foundational Time Series Models for Classification
Sang Hyub Kim, Julien Baglio, Rajiv Krishnakumar +7
Time-series foundation models produce rich embeddings, but whether quantum models can exploit them, and how far hybrid classical-quantum architectures scale, remains unclear. We ad…
Comment on 'Supervised quantum machine learning models are kernel methods'
Rajiv Krishnakumar
We identify a few small errors in the proof of Theorem 1 in M. Schuld, 'Supervised quantum machine learning models are kernel methods' (arXiv:2101.11020v2), Appendix A. These do no…
Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification
Rajiv Krishnakumar, Julien Baglio, Frederik F. Flöther +3
Whole-slide image classification represents a key challenge in computational pathology and medicine. Attention-based multiple instance learning (MIL) has emerged as an effective ap…
Feedback Connections in Quantum Reservoir Computing with Mid-Circuit Measurements
Jakob Murauer, Rajiv Krishnakumar, Sabine Tornow +1
Existing approaches to quantum reservoir computing can be broadly categorized into restart-based and continuous protocols. Restart-based methods require reinitializing the quantum…
How quantum computing can enhance biomarker discovery
Frederik F. Flöther, Daniel Blankenberg, Maria Demidik +7
Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very…
A Q# Implementation of a Quantum Lookup Table for Quantum Arithmetic Functions
Rajiv Krishnakumar, Mathias Soeken, Martin Roetteler +1
In this paper, we present Q# implementations for arbitrary single-variabled fixed-point arithmetic operations for a gate-based quantum computer based on lookup tables (LUTs). In ge…
Quantum Rounding
Rajiv Krishnakumar, William Zeng
We introduce new rounding methods to improve the accuracy of finite precision quantum arithmetic. These quantum rounding methods are applicable when multiple samples are being take…
A Threshold for Quantum Advantage in Derivative Pricing
Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola +3
We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum d…
Retrieval of cavity-generated atomic spin-squeezing after free-space release
Yunfan Wu, Rajiv Krishnakumar, Julián Martínez-Rincón +3
The compatibility of cavity-generated spin-squeezed atomic states with atom-interferometric sensors that require freely falling atoms is demonstrated. An ensemble of spin…
Quantum state magnification
O. Hosten, R. Krishnakumar, N. J. Engelsen +1
Quantum metrology exploits entangled states of particles to improve sensing precision beyond the limit achievable with uncorrelated particles. All previous methods required detecti…