12 citations · 53 across the 37 of their papers we have counts for
6 papers · 1 filter
Identity-Paired Progressive Depth Training: When Trainability Persists Beyond Expressibility
Athanasios Hadjidimoulas, Tirthak Patel, Anastasios Kyrillidis
Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, yet their training suffers from sensitivity to circuit depth, initialization, and land…
Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest
Yuqian Huo, David Quiroga, Anastasios Kyrillidis +1
Variational quantum algorithms (VQAs) have the potential to demonstrate quantum utility on near-term quantum computers. However, these algorithms often get executed on the highest-…
Quantum EigenGame for excited state calculation
David Quiroga, Jason Han, Anastasios Kyrillidis
Computing the excited states of a given Hamiltonian is computationally hard for large systems, but methods that do so using quantum computers scale tractably. This problem is equiv…
A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm
Junhyung Lyle Kim, Nai-Hui Chia, Anastasios Kyrillidis
Solving systems of linear equations is a fundamental problem, but it can be computationally intensive for classical algorithms in high dimensions. Existing quantum algorithms can a…
Grover-QAOA for 3-SAT: Quadratic Speedup, Fair-Sampling, and Parameter Clustering
Zewen Zhang, Roger Paredes, Bhuvanesh Sundar +5
The SAT problem is a prototypical NP-complete problem of fundamental importance in computational complexity theory with many applications in science and engineering; as such, it ha…
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
Junhyung Lyle Kim, Mohammad Taha Toghani, César A. Uribe +1
We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix ov…