150 citations · 303 across the 12 of their papers we have counts for
9 papers · 1 filter
CT-bMPS: Approximate Tensor Network Contraction with Boundary MPS on Contraction Trees
Hidetaka Manabe, Feng Pan
We introduce contraction tree boundary matrix product states (CT-bMPS), a scalable approximate contraction method for general closed tensor networks. CT-bMPS represents the boundar…
Classical Simulation and Design Frontiers for IBM's Doped Clifford Sampling Experiment
Hidetaka Manabe, Hanfeng Gu, Feng Pan
We classically simulate the IBM doped Clifford random circuit sampling experiment, comprising qubits, entangling layers, and inserted gates. A deterministic tem…
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…
Achieving Energetic Superiority Through System-Level Quantum Circuit Simulation
Rong Fu, Zhongling Su, Han-Sen Zhong +11
Quantum Computational Superiority boasts rapid computation and high energy efficiency. Despite recent advances in classical algorithms aimed at refuting the milestone claim of Goog…
Leapfrogging Sycamore: Harnessing 1432 GPUs for 7 Faster Quantum Random Circuit Sampling
Xian-He Zhao, Han-Sen Zhong, Feng Pan +10
Random quantum circuit sampling serves as a benchmark to demonstrate quantum computational advantage. Recent progress in classical algorithms, especially those based on tensor netw…
Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs
Feng Pan, Hanfeng Gu, Lvlin Kuang +2
Efficient simulation of quantum circuits has become indispensable with the rapid development of quantum hardware. The primary simulation methods are based on state vectors and tens…
qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers
Hanyan Cao, Feng Pan, Yijia Wang +1
We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, t…
Solving the sampling problem of the Sycamore quantum circuits
Feng Pan, Keyang Chen, Pan Zhang
We study the problem of generating independent samples from the output distribution of Google's Sycamore quantum circuits with a target fidelity, which is believed to be beyond the…
Simulating the Sycamore quantum supremacy circuits
Feng Pan, Pan Zhang
We propose a general tensor network method for simulating quantum circuits. The method is massively more efficient in computing a large number of correlated bitstring amplitudes an…