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Shallow-circuit Supervised Learning on a Quantum Processor
Luca Candelori, Swarnadeep Majumder, Antonio Mezzacapo +6
Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a ste…
Quantum chemistry with provable convergence via randomized sample-based Krylov quantum diagonalization
Samuele Piccinelli, Alberto Baiardi, Stefano Barison +12
Quantum algorithms based on classical processing of individual samples have recently emerged as the most effective and robust methods to approximate ground-state wave functions of…
Improved Quantum Computation using Operator Backpropagation
Bryce Fuller, Minh C. Tran, Danylo Lykov +10
Decoherence of quantum hardware is currently limiting its practical applications. At the same time, classical algorithms for simulating quantum circuits have progressed substantial…
Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization
Jeffery Yu, Javier Robledo Moreno, Joseph T. Iosue +17
Approximating the ground state of many-body systems is a key computational bottleneck underlying important applications in physics and chemistry. The most widely known quantum algo…
Dynamic parameterized quantum circuits: expressive and barren-plateau free
Abhinav Deshpande, Marcel Hinsche, Khadijeh Najafi +3
Classical optimization of parameterized quantum circuits is a widely studied methodology for the preparation of complex quantum states, as well as the solution of machine learning…