150 citations · 303 across the 12 of their papers we have counts for
13 papers
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…
Tensor Network Message Passing
Yijia Wang, Yuwen Ebony Zhang, Feng Pan +1
When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. How…
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…
Boltzmann machines as two-dimensional tensor networks
Sujie Li, Feng Pan, Pengfei Zhou +1
Restricted Boltzmann machines (RBM) and deep Boltzmann machines (DBM) are important models in machine learning, and recently found numerous applications in quantum many-body physic…
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…
Contracting Arbitrary Tensor Networks: General Approximate Algorithm and Applications in Graphical Models and Quantum Circuit Simulations
Feng Pan, Pengfei Zhou, Sujie Li +1
We present a general method for approximately contracting tensor networks with an arbitrary connectivity. This enables us to release the computational power of tensor networks to w…
Solving Statistical Mechanics on Sparse Graphs with Feedback Set Variational Autoregressive Networks
Feng Pan, Pengfei Zhou, Hai-Jun Zhou +1
We propose a method for solving statistical mechanics problems defined on sparse graphs. It extracts a small Feedback Vertex Set (FVS) from the sparse graph, converting the sparse…