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20242026
most citedDynamic parameterized quantum circuits: expressive and barren-plateau free

3 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

quant-ph2026

Sampling hard circuits with verifiably high fidelity

Simon Martiel, Jay-U Chung, Alireza Seif +9

Sampling-based proposals are prominent candidates for demonstrating quantum computations beyond the reach of classical supercomputers. However, it has been difficult to combine the…

quant-ph2026

Observation of Improved Accuracy over Classical Sparse Ground-State Solvers using a Quantum Computer

William Kirby, Bibek Pokharel, Javier Robledo Moreno +25

Demonstrating quantum advantage over classical algorithms for ground state energy problems is an outstanding open problem in quantum computation. We experimentally demonstrate that…

quant-ph2025

Efficiently verifiable quantum advantage using error correction

Abhinav Deshpande, Bill Fefferman, Soumik Ghosh +3

A key issue with existing quantum advantage experiments is that their verification requires exponential classical time. In this work, we address this challenge by designing a new p…

quant-ph2025

Order from chaos with adaptive circuits on quantum hardware

Bibek Pokharel, Haining Pan, Kemal Aziz +8

Programmable quantum devices provide a platform to control the coherent dynamics of quantum wavefunctions. Here we experimentally realize adaptive monitored quantum circuits, which…

quant-ph2025

Efficient Quantum Gibbs Sampling with Local Circuits

Dominik Hahn, Ryan Sweke, Abhinav Deshpande +1

The problem of simulating the thermal behavior of quantum systems remains a central open challenge in quantum computing. Unlike well-established quantum algorithms for unitary dyna…

quant-ph2024★ 3 cited

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…