Encoding of Matrix Product States into Quantum Circuits of One- and Two-Qubit Gates
arXiv:1908.07958 · doi:10.1103/PhysRevA.101.032310
Abstract
The matrix product state (MPS) belongs to the most important mathematical models in, for example, condensed matter physics and quantum information sciences. However, to realize an -qubit MPS with large and large entanglement on a quantum platform is extremely challenging, since it requires high-level qudits or multi-body gates of two-level qubits to carry the entanglement. In this work, an efficient method that accurately encodes a given MPS into a quantum circuit with only one- and two-qubit gates is proposed. The idea is to construct the unitary matrix product operators that optimally disentangle the MPS to a product state. These matrix product operators form the quantum circuit that evolves a product state to the targeted MPS with a high fidelity. Our benchmark on the ground-state MPS's of the strongly-correlated spin models show that the constructed quantum circuits can encode the MPS's with much fewer qubits than the sizes of the MPS's themselves. This method paves a feasible and efficient path to realizing quantum many-body states and other MPS-based models as quantum circuits on the near-term quantum platforms.
7 pages, 5 figures
References in corpus (16)
- The density-matrix renormalization group in the age of matrix product states
- Matrix Product Density Operators: Simulation of finite-T and dissipative systems
- Classical simulation of infinite-size quantum lattice systems in one spatial dimension
- Matrix product states represent ground states faithfully
- Classical simulation of infinite-size quantum lattice systems in two spatial dimensions
- Efficient quantum state tomography
- DMRG and periodic boundary conditions: a quantum information perspective
- Diverging Entanglement Length in Gapped Quantum Spin Systems
- Scaling of entanglement support for Matrix Product States
- Continuous Matrix Product States for Quantum Fields
- Novel schemes for measurement-based quantum computation
- Measurement-based quantum computation beyond the one-way model
- Solving nonequilibrium dynamical mean-field theory using matrix product states
- Linearized Tensor Renormalization Group Algorithm for Thermodynamics of Quantum Lattice Models
- Dynamical simulations of classical stochastic systems using matrix product states
- Quantum field tomography
Cited by in corpus (91)
- Quantum Machine Learning for Chemistry and Physics
- Parallel Quantum Simulation of Large Systems on Small Quantum Computers
- Variational Quantum Reinforcement Learning via Evolutionary Optimization
- Preparation of matrix product states with log-depth quantum circuits
- Developments in the Tensor Network -- from Statistical Mechanics to Quantum Entanglement
- Does provable absence of barren plateaus imply classical simulability?
- Towards adiabatic quantum computing using compressed quantum circuits
- Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage
- Improved thermal area law and quasi-linear time algorithm for quantum Gibbs states
- Data compression for quantum machine learning
- Sequential generation of projected entangled-pair states
- Constant-depth preparation of matrix product states with adaptive quantum circuits
- Initial state preparation for quantum chemistry on quantum computers
- Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
- Probabilistic Nonunitary Gate in Imaginary Time Evolution
- Tensor networks for quantum machine learning
- Absence of barren plateaus in finite local-depth circuits with long-range entanglement
- Efficient quantum amplitude encoding of polynomial functions
- Algebraic Bethe Circuits
- High-fidelity realization of the AKLT state on a NISQ-era quantum processor
- Quantum State Preparation of Normal Distributions using Matrix Product States
- Rapid initial state preparation for the quantum simulation of strongly correlated molecules
- Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions
- Holographic quantum simulation of entanglement renormalization circuits
- Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning
- Preparing Valence-Bond-Solid states on noisy intermediate-scale quantum computers
- Automatically Differentiable Quantum Circuit for Many-qubit State Preparation
- Augmenting Density Matrix Renormalization Group with Disentanglers
- Preparing quantum many-body scar states on quantum computers
- Tensor networks for interpretable and efficient quantum-inspired machine learning
- Tensor networks for quantum computing
- Approximate encoding of quantum states using shallow circuits
- Combining Matrix Product States and Noisy Quantum Computers for Quantum Simulation
- Quantum algorithm for partial differential equations of non-conservative systems with spatially varying parameters
- Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval
- Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
- Automatic quantum circuit encoding of a given arbitrary quantum state
- Deterministic and Entanglement-Efficient Preparation of Amplitude-Encoded Quantum Registers
- High ground state overlap via quantum embedding methods
- Spin coupling is all you need: Encoding strong electron correlation in molecules on quantum computers
- Variational quantum state preparation via quantum data buses
- Efficient Quantum Circuits for Accurate State Preparation of Smooth, Differentiable Functions
- Quantum state preparation for multivariate functions
- Early Fault-Tolerant Quantum Algorithms in Practice: Application to Ground-State Energy Estimation
- Hybrid Tree Tensor Networks for quantum simulation
- Approximate Quantum Compiling for Quantum Simulation: A Tensor Network based approach
- Variational adiabatic transport of tensor networks
- Tensor-based quantum phase difference estimation for large-scale demonstration
- Quantum Dynamics Simulation of the Advection-Diffusion Equation
- Variational Optimization for Quantum Problems using Deep Generative Networks
- Variational quantum eigensolver with embedded entanglement using a tensor-network ansatz
- Matrix product state ansatz for the variational quantum solution of the Heisenberg model on Kagome geometries
- Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity
- The State Preparation of Multivariate Normal Distributions using Tree Tensor Network
- Multireference error mitigation for quantum computation of chemistry
- Holographic Gaussian Boson Sampling with Matrix Product States on 3D cQED Processors
- Efficient preparation of the AKLT State with Measurement-based Imaginary Time Evolution
- Dual-VQE: A quantum algorithm to lower bound the ground-state energy
- Variational Quantum Imaginary Time Evolution for Matrix Product State Ansatz with Tests on Transcorrelated Hamiltonians
- Spectral Gap Optimization for Enhanced Adiabatic State Preparation
- Tensor-Programmable Quantum Circuits for Solving Differential Equations
- Macroproperties vs. Microstates in the Classical Simulation of Critical Phenomena in Quench Dynamics of 1D Ising Models
- Detecting Measurement-Induced Entanglement Transitions With Unitary Mirror Circuits
- Quantum State Preparation for Probability Distributions with Reflection Symmetry Using Matrix Product States
- Cost of Locally Approximating High-Dimensional Ground States of Contextual Quantum Models
- Optimal Qubit Mapping Search for Encoding Classical Data into Matrix Product State Representation with Minimal Loss
- Quantum state preparation via piecewise QSVT
- Preparation Circuits for Matrix Product States by Classical Variational Disentanglement
- Typical Machine Learning Datasets as Low-Depth Quantum Circuits
- Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
- Bridging Quantum Computing and Nuclear Structure: Atomic Nuclei on a Trapped-Ion Quantum Computer
- Simulating Quantum Turbulence with Matrix Product States
- Exploiting many-body localization for scalable variational quantum simulation
- Tensor Network Efficiently Representing Schmidt Decomposition of Quantum Many-Body States
- A coherent approach to quantum-classical optimization
- Fast Tensor Disentangling Algorithm
- Time series generation for option pricing on quantum computers using tensor network
- Tucker iterative quantum state preparation
- Matrix Product State on a Quantum Computer
- High-expressibility Quantum Neural Networks using only classical resources
- Matrix-product entanglement characterizing the optimality of state-preparation quantum circuits
- Imaginary Time Spectral Transforms for Excited State Preparation
- Transfer entropy and O-information to detect grokking in tensor network multi-class classification problems
- Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
- A quantum eigenvalue solver based on tensor networks
- Variational decision diagrams for quantum-inspired machine learning applications
- Quantum Encoding of Structured Data with Matrix Product States
- Quantum Algorithms for State Preparation and Data Classification based on Stabilizer Codes
- Trainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks
- Preparing the Gutzwiller wave function for attractive SU(3) fermions on a quantum computer
- Tensor-based phase difference estimation on time series analysis