Simulating the Sycamore quantum supremacy circuits
arXiv:2103.03074
Abstract
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 and probabilities than existing methods. As an application, we study the sampling problem of Google's Sycamore circuits, which are believed to be beyond the reach of classical supercomputers and have been used to demonstrate quantum supremacy. Using our method, employing a small computational cluster containing 60 graphical processing units (GPUs), we have generated one million correlated bitstrings with some entries fixed, from the Sycamore circuit with 53 qubits and 20 cycles, with linear cross-entropy benchmark (XEB) fidelity equals 0.739, which is much higher than those in Google's quantum supremacy experiments.
9 pages, 6 figures, comments are welcome
References in corpus (12)
- Characterizing Quantum Supremacy in Near-Term Devices
- Spectral redemption: clustering sparse networks
- Simulating quantum computation by contracting tensor networks
- Quantum Supremacy and the Complexity of Random Circuit Sampling
- A flexible high-performance simulator for verifying and benchmarking quantum circuits implemented on real hardware
- Quantum computing and the entanglement frontier
- Simulation of low-depth quantum circuits as complex undirected graphical models
- Classical Simulation of Intermediate-Size Quantum Circuits
- Contracting Arbitrary Tensor Networks: General Approximate Algorithm and Applications in Graphical Models and Quantum Circuit Simulations
- Boundaries of quantum supremacy via random circuit sampling
- Quantum Supremacy Is Both Closer and Farther than It Appears
- Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation
Cited by in corpus (21)
- Strong quantum computational advantage using a superconducting quantum processor
- Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light
- Hybrid Quantum-Classical Convolutional Neural Networks
- Closing the "Quantum Supremacy" Gap: Achieving Real-Time Simulation of a Random Quantum Circuit Using a New Sunway Supercomputer
- Simulating quantum circuits with ZX-calculus reduced stabiliser decompositions
- GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm
- Tensor networks for unsupervised machine learning
- How to simulate quantum measurement without computing marginals
- Benchmarking near-term quantum computers via random circuit sampling
- Special Session: Noisy Intermediate-Scale Quantum (NISQ) Computers -- How They Work, How They Fail, How to Test Them?
- Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval
- Estimating the randomness of quantum circuit ensembles up to 50 qubits
- Gaussian boson sampling with partial distinguishability
- Simulating Quantum Computations with Tutte Polynomials
- The Argument against Quantum Computers, the Quantum Laws of Nature, and Google's Supremacy Claims
- Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
- Efficient approximation of experimental Gaussian boson sampling
- Comment on the Quantum Supremacy Claim by Google
- Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
- Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
- Towards a NISQ Algorithm to Simulate Hermitian Matrix Exponentiation