35 citations · 42 across the 16 of their papers we have counts for
19 papers
Structural Conditions for Distributed Quantum Advantage
Sabina Drăgoi, María Gragera Garcés, Lirandë Pira
Circuit cutting runs a quantum computation on processors smaller than the circuit, at the price of classical knitting whose cost grows exponentially with the number of cut gates. K…
Optimal query complexity for fractional quantum evolution
Anthony Yuezhang Liu, Adam Wesołowski, Jayne Thompson +2
Given oracle access to an unknown unitary , the fractional query problem asks how many queries are required to implement a noninteger power , , when…
Weighted Quantum Signal Processing: Low-Depth Polynomial Approximation with Applications to Kolmogorov-Arnold Networks
Rohit Sarma Sarkar, Rupayan Bhattacharjee, Elias F. Combarro +5
Quantum Signal Processing is a powerful quantum framework for generating and approximating univariate polynomials. However, QSP is often limited by circuit-depth bottlenecks and pa…
Quantum Hamiltonian Evolution for Coherent Quantum Learning
Ignacio B. Acedo, Javier Gonzalez-Conde, Pablo Rodriguez-Grasa +2
We introduce Coherent Quantum Learning (CQL), a training framework for quantum learning models in which the model parameters are quantum degrees of freedom evolved under a Hamilton…
Entanglement geometry separates circuit cutting, classical hardness, and trainability
Maria Gragera Garces, Sabina Drăgoi, Lirandë Pira
Circuit cutting promises to scale quantum computations beyond current hardware, but variational quantum advantage also requires low cutting overhead, classical hardness, and traina…
Quantum Topological Data Encoding
Adam Wesołowski, Dimitrios Thanos, Daniel Leykam +1
Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representatio…
The Cost of Removing Tunability in Quantum Data Re-Uploading
Anthony Yuezhang Liu, Lirandë Pira
Fixed encoding data re-uploading quantum circuits provide a striking example of universality emerging from a highly constrained architecture. However, universality alone is insuffi…
Quantum ring all-reduce: communication and privacy advantages for distributed learning
María Gragera Garcés, Lirandë Pira
Machine learning models have scaled to unprecedented sizes, making training across distributed devices the de facto standard in the field. In this work, we explore how quantum comm…
Fundamentals of Quantum Machine Learning and Robustness
Lirandë Pira, Patrick Rebentrost
Quantum machine learning (QML) sits at the intersection of quantum computing and classical machine learning, offering the prospect of new computational paradigms and advantages for…
Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Arthur G. Rattew, Po-Wei Huang, Naixu Guo +2
Fault-tolerant Quantum Processing Units (QPUs) promise to deliver exponential speed-ups in select computational tasks, yet their integration into modern deep learning pipelines rem…
Quantum Learning with Tunable Loss Functions
Yixian Qiu, Lirandë Pira, Patrick Rebentrost
Learning from quantum data presents new challenges to the paradigm of learning from data. This typically entails the use of quantum learning models to learn quantum processes that…
Expressivity Limits in Quantum Walk-based Optimization
Guilherme A. Bridi, Debbie Lim, Lirandë Pira +3
Quantum algorithms have emerged as a promising tool to solve combinatorial optimization problems. The quantum walk optimization algorithm (QWOA) is one such variational approach th…
Degree-Optimized Cumulative Polynomial Kolmogorov-Arnold Networks
Mathew Vanherreweghe, Lirandë Pira, Patrick Rebentrost
We introduce cumulative polynomial Kolmogorov-Arnold networks (CP-KAN), a neural architecture combining Chebyshev polynomial basis functions and quadratic unconstrained binary opti…
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques
Lirandë Pira, Airin Antony, Nayanthara Prathap +2
Photonic chip design has seen significant advancements with the adoption of inverse design methodologies, offering flexibility and efficiency in optimizing device performance. Howe…
Quantum Linear System Solvers: A Survey of Algorithms and Applications
Mauro E. S. Morales, Lirandë Pira, Philipp Schleich +7
Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve t…
QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
Petr Ivashkov, Po-Wei Huang, Kelvin Koor +2
We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compos…
Sub-universal variational circuits for combinatorial optimization problems
Gal Weitz, Lirandë Pira, Chris Ferrie +1
Quantum variational circuits have gained significant attention due to their applications in the quantum approximate optimization algorithm and quantum machine learning research. Th…
On the Interpretability of Quantum Neural Networks
Lirandë Pira, Chris Ferrie
Interpretability of artificial intelligence (AI) methods, particularly deep neural networks, is of great interest. This heightened focus stems from the widespread use of AI-backed…
An Invitation to Distributed Quantum Neural Networks
Lirandë Pira, Chris Ferrie
Deep neural networks have established themselves as one of the most promising machine learning techniques. Training such models at large scales is often parallelized, giving rise t…