3 citations · 4 across the 12 of their papers we have counts for
8 papers · 1 filter
From Block-encoding to Generalized Quantum Signal Processing: Principles, Algorithms and Applications
Tal Gurfinkel, Kaushika De Silva, Anuradha Mahasinghe +9
Modern quantum algorithms are increasingly formulated as coherent procedures for implementing polynomial transformations of operators and singular values. This perspective provides…
Numerical Evaluation of ZX Calculus Optimization for Solovay Kitaev Quantum Circuit Synthesis
Dulari De Silva, Anuradha Mahasinghe, Chon-Fai Kam +3
Fault-tolerant architectures implement non-Clifford T gates through magic-state distillation, so the T-count of a synthesized circuit dominates its physical cost. The Solovay-Kitae…
An Exactness Barrier for ZX-Calculus Optimization of Synthesized Clifford+T Circuits
Chon-Fai Kam, Anuradha Mahasinghe, Kaushika De Silva +2
Gate synthesis and circuit optimization are usually studied separately, and evidence on their interaction is contradictory: ZX-calculus rewriting removes a stable fraction of Solov…
Assessing Cost Hamiltonian Reliability in Quantum Protein Structure Prediction
Mathieu Roget, Cedric Damour, Frederic Cadet +1
In variational quantum algorithms, QAOA, and quantum annealing, the cost Hamiltonian defines the optimization landscape explored by the quantum hardware; however, in many applicati…
Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability
Chon-Fai Kam, Xavier Cadet, Miloud Bessafi +1
While world models learn compact representations of complex environments, they lack a physics-grounded metric to assess the structural fidelity of their latent spaces. We identify…
Hermitian Matrix Function Synthesis without Block-Encoding
Anuradha Mahasinghe, Kaushika De Silva, Xavier Cadet +3
Implementing polynomial functions of Hermitian matrices on quantum hardware is a foundational task in quantum computing, critical for accurate Hamiltonian simulation, quantum linea…
Quantum Autoencoder: An efficient approach to quantum feature map generation
Shengxin Zhuang, Yusen Wu, Xavier F. Cadet +6
Quantum machine learning methods often rely on fixed, hand-crafted quantum encodings that may not capture optimal features for downstream tasks. In this work, we study the power of…
Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine
Shengxin Zhuang, John Tanner, Yusen Wu +7
Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a…