Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2508.06159

Diagonalizing large-scale quantum many-body Hamiltonians using variational quantum circuit and tensor network

Pengfei Zhou, Qiao, Shuang, An-Chun Ji, Shi-Ju Ran

Published Aug 8, 2025• 0 citations
arXivHTMLPDF

Abstract

Exact diagonalization (ED) provides complete access to many-body eigenenergies and eigenstates, yet its exponential cost confines it to small systems. We propose tensor network variational diagonalization (TNVD), which encodes the full eigenenergy spectrum in a matrix product state (MPS) while representing the corresponding eigenstates via a finite depth variational quantum circuit (VQC) acting on product states. TNVD thereby reduces diagonalization complexity from exponential to polynomial in system size $N$. For the quantum Ising chain, TNVD accurately reproduces eigenenergies for $N\leq 16$ and, at sizes inaccessible to ED such as $N=100$, directly samples them from a single MPS encoding all $2^N$ levels. A random label control shows that this compact $N$-site representation depends on how the eigenenergies are organized in label space. TNVD further reveals that, at their integrable limits, the random field Ising and XXZ chains show comparable level spacing ratios and mean eigenstate entanglement entropies but markedly different Schmidt spectrum decay. This difference exposes distinct eigenstate entanglement structures that govern their classical simulability and the difficulty of finite depth quantum circuit preparation. Our work establishes TNVD as a scalable full spectrum diagonalization framework and its VQC as a quantum route to volume-law-entangled eigenstates that challenge efficient classical simulation.

DOI
10.48550/arxiv.2508.06159

Related papers

Tensor Network Efficiently Representing Schmidt Decomposition of Quantum Many-Body States

Peng-Fei Zhou, Ying Lu, Jiahao Wang +1 more

Efficient methods to access the entanglement of a quantum many-body state, where the complexity generally scales exponentially with the system size N, have long been a concern. Here we propose the Schmidt tensor network state (Schmidt TNS) that efficiently represents the Schmidt decomposition of finite- and even infinite-size quantum states with nontrivial bipartition boundary. The key idea is to represent the Schmidt coefficients (i.e., entanglement spectrum) and transformations in the decomposition to tensor networks (TNs) with linearly scaled complexity versus N. Specifically, the transformations are written as the TNs formed by local unitary tensors, and the Schmidt coefficients are encoded in a positive-definite matrix product state (MPS). Translational invariance can be imposed on the TNs and MPS for the infinite-size cases. The validity of Schmidt TNS is demonstrated by simulating the ground state of the quasi-one-dimensional spin model with geometrical frustration. Our results show that the MPS encoding the Schmidt coefficients is weakly entangled even when the entanglement entropy of the decomposed state is strong. This justifies the efficiency of using MPS to encode the Schmidt coefficients, and promises an exponential speedup on the full-state sampling tasks.

arXiv:2210.08166•Physical Review Letters•Jul 13, 2023•1 citation

Efficient Representation of Fully Many-Body Localized Systems Using Tensor Networks

Wahl, TB, Arijeet Pal, Steven H. Simon

We propose a tensor network encoding the set of all eigenstates of a fully many-body localized system in one dimension. Our construction, conceptually based on the ansatz introduced in Phys. Rev. B 94, 041116(R) (2016), is built from two layers of unitary matrices which act on blocks of \nℓ \n contiguous sites. We argue that this yields an exponential reduction in computational time and memory requirement as compared to all previous approaches for finding a representation of the complete eigenspectrum of large many-body localized systems with a given accuracy. Concretely, we optimize the unitaries by minimizing the magnitude of the commutator of the approximate integrals of motion and the Hamiltonian, which can be done in a local fashion. This further reduces the computational complexity of the tensor networks arising in the minimization process compared to previous work. We test the accuracy of our method by comparing the approximate energy spectrum to exact diagonalization results for the random-field Heisenberg model on 16 sites. We find that the technique is highly accurate deep in the localized regime and maintains a surprising degree of accuracy in predicting certain local quantities even in the vicinity of the predicted dynamical phase transition. To demonstrate the power of our technique, we study a system of 72 sites, and we are able to see clear signatures of the phase transition. Our work opens a new avenue to study properties of the many-body localization transition in large systems.

•May 8, 2017•57 citations

Open source matrix product states: exact diagonalization and other entanglement-accurate methods revisited in quantum systems

Daniel Jaschke, Lincoln D. Carr

Abstract Tensor network methods as presented in our open source matrix product states software have opened up the possibility to study many-body quantum physics in one and quasi-one-dimensional systems in an easily accessible package similar to density functional theory codes but for strongly correlated dynamics. Here, we address methods which allow one to capture the full entanglement without truncation of the Hilbert space. Such methods are suitable for validation of and comparisons to tensor network algorithms, but especially useful in the case of new kinds of quantum states with high entanglement violating the truncation in tensor networks. Quantum cellular automata are one example for such a system, characterized by tunable complexity, entanglement, and a large spread over the Hilbert space. Beyond the evolution of pure states as a closed system, we adapt the techniques for open quantum systems simulated via the Lindblad master equation. We present three algorithms for solving closed-system many-body time evolution without truncation of the Hilbert space. Exact diagonalization methods have the advantage that they not only keep the full entanglement but also require no approximations to the propagator. Seeking the limits of a maximal number of qubits on a single core, we use Trotter decompositions or Krylov approximation to the exponential of the Hamiltonian. All three methods are also implemented for open systems represented via the Lindblad master equation built from local channels. We show their convergence parameters and focus on efficient schemes for their implementations including Abelian symmetries, e.g. symmetry used for number conservation in the Bose–Hubbard model or discrete symmetries in the quantum Ising model. We present the thermalization timescale in the long-range quantum Ising model as a key example of how exact diagonalization contributes to novel physics.

arXiv:1802.10052•Journal of Physics A Mathematical and Theoretical•Sep 27, 2018•13 citations

Efficient variational diagonalization of fully many-body localized Hamiltonians

Frank Pollmann, Vedika Khemani, J. I. Cirac +1 more

The phenomenon of many-body localization generalizes Anderson localization to interacting systems. Understanding this conceptually novel phenomenon requires a study of many-body eigenstates at finite energy densities. This is a very challenging task since the most efficient numerical methods such as, e.g., the density matrix renormalization group method, can only access the ground state and low lying excitations. In this work, the authors introduce a unitary tensor network based variational method that approximately finds all many-body eigenstates of fully localized Hamiltonians and scales polynomially with system size. The usefulness of their approach is demonstrated by considering the Heisenberg chain in a strongly disordered magnetic field.

arXiv:1506.07179•Physical review. B./Physical review. B•Jul 28, 2016•90 citations

Scalable Preparation of Matrix Product States with Sequential and Brick Wall Quantum Circuits

Tomasz Szołdra, Rick Mukherjee, Peter Schmelcher

Preparing arbitrary quantum states requires exponential resources. Matrix Product States (MPS) admit more efficient constructions, particularly when accuracy is traded for circuit complexity. Existing approaches to MPS preparation mostly rely on heuristic circuits that are deterministic but quickly saturate in accuracy, or on variational optimization methods that reach high fidelities but scale poorly. This work introduces an end-to-end MPS preparation framework that combines the strengths of both strategies within a single pipeline. Heuristic staircase-like and brick wall disentangler circuits provide warm-start initializations for variational optimization, enabling high-fidelity state preparation for large systems. Target MPSs are either specified as physical quantum states or constructed from classical datasets via amplitude encoding, using step-by-step singular value decompositions or tensor cross interpolation. The framework incorporates entanglement-based qubit reordering, reformulated as a quadratic assignment problem, and low-level optimizations that reduce depths by up to 50% and CNOT counts by 33%. We evaluate the full pipeline on datasets of varying complexity across systems of 19-50 qubits and identify trade-offs between fidelity, gate count, and circuit depth. Optimized brick wall circuits typically achieve the lowest depths, while the optimized staircase-like circuits minimize gate counts. Overall, our results provide principled and scalable protocols for preparing MPSs as quantum circuits, supporting utility-scale applications on near-term quantum devices.

quant-pharXiv:2602.12042•Feb 13, 2026•0 citations

Quantum-inspired classical simulation through randomized time evolution

Fredrik Hasselgren, Bálint Koczor

Tensor-network simulations of quantum many-body dynamics are fundamentally limited by entanglement build-up, which leads to exponentially growing computational costs. Furthermore, these classical simulation algorithms are inherently sequential as typically a tensor network representation of the quantum state is updated incrementally at each time step. We build on recently introduced randomized quantum algorithms for time evolution (TE-PAI), and adapt them to the classical simulation context with the purpose of enabling massive parallelisation. Our MPS TE-PAI approach achieves exact time evolution on average (unbiased estimator) and proceeds by representing an ensemble of randomized shallow Trotter-variant circuits as tensor networks. As each circuit instance yields a deterministic quantum state (or observable expecation value), the only source of randomness is the sampling of circuit variants; the absence of shot noise therefore yields a reduced estimator variance relative to quantum hardware implementations of TE-PAI. We simulate representative disordered one-dimensional spin-ring Hamiltonians, and numerically observe reductions in the per-sample gate-count by a factor of up to $10^3$ relative to Trotterized MPS evolution, yielding orders of magnitude reduction in the time-to-solution under realistic levels of parallelisation. Finally, we numerically observe that MPS TE-PAI is substantially more robust against severe bond-dimension truncation than product formulas, potentially making it useful for the simulation of strongly correlated systems where truncation is necessary in practice. We also demonstrate that the approach can be used naturally in combination with existing time evolution algorithms, effectively extending their time depth via parallelisation.

quant-pharXiv:2604.13144•Apr 15, 2026•0 citations
Exact diagonalization (ED) provides complete access to many-body eigenenergies and eigenstates, yet its exponential cost confines it to small systems. We propose tensor network variational diagonalization (TNVD), which encodes the full eigenenergy spectrum in a matrix product state (MPS) while representing the corresponding eigenstates via a finite depth variational quantum circuit (VQC) acting on product states. TNVD thereby reduces diagonalization complexity from exponential to polynomial in system size NNN. For the quantum Ising chain, TNVD accurately reproduces eigenenergies for N≤16N\leq 16N≤16 and, at sizes inaccessible to ED such as N=100N=100N=100, directly samples them from a single MPS encoding all 2N2^N2N levels. A random label control shows that this compact NNN-site representation depends on how the eigenenergies are organized in label space. TNVD further reveals that, at their integrable limits, the random field Ising and XXZ chains show comparable level spacing ratios and mean eigenstate entanglement entropies but markedly different Schmidt spectrum decay. This difference exposes distinct eigenstate entanglement structures that govern their classical simulability and the difficulty of finite depth quantum circuit preparation. Our work establishes TNVD as a scalable full spectrum diagonalization framework and its VQC as a quantum route to volume-law-entangled eigenstates that challenge efficient classical simulation.
Efficient methods to access the entanglement of a quantum many-body state, where the complexity generally scales exponentially with the system size N, have long been a concern. Here we propose the Schmidt tensor network state (Schmidt TNS) that efficiently represents the Schmidt decomposition of finite- and even infinite-size quantum states with nontrivial bipartition boundary. The key idea is to represent the Schmidt coefficients (i.e., entanglement spectrum) and transformations in the decomposition to tensor networks (TNs) with linearly scaled complexity versus N. Specifically, the transformations are written as the TNs formed by local unitary tensors, and the Schmidt coefficients are encoded in a positive-definite matrix product state (MPS). Translational invariance can be imposed on the TNs and MPS for the infinite-size cases. The validity of Schmidt TNS is demonstrated by simulating the ground state of the quasi-one-dimensional spin model with geometrical frustration. Our results show that the MPS encoding the Schmidt coefficients is weakly entangled even when the entanglement entropy of the decomposed state is strong. This justifies the efficiency of using MPS to encode the Schmidt coefficients, and promises an exponential speedup on the full-state sampling tasks.
We propose a tensor network encoding the set of all eigenstates of a fully many-body localized system in one dimension. Our construction, conceptually based on the ansatz introduced in Phys. Rev. B 94, 041116(R) (2016), is built from two layers of unitary matrices which act on blocks of \nℓ \n contiguous sites. We argue that this yields an exponential reduction in computational time and memory requirement as compared to all previous approaches for finding a representation of the complete eigenspectrum of large many-body localized systems with a given accuracy. Concretely, we optimize the unitaries by minimizing the magnitude of the commutator of the approximate integrals of motion and the Hamiltonian, which can be done in a local fashion. This further reduces the computational complexity of the tensor networks arising in the minimization process compared to previous work. We test the accuracy of our method by comparing the approximate energy spectrum to exact diagonalization results for the random-field Heisenberg model on 16 sites. We find that the technique is highly accurate deep in the localized regime and maintains a surprising degree of accuracy in predicting certain local quantities even in the vicinity of the predicted dynamical phase transition. To demonstrate the power of our technique, we study a system of 72 sites, and we are able to see clear signatures of the phase transition. Our work opens a new avenue to study properties of the many-body localization transition in large systems.
Abstract Tensor network methods as presented in our open source matrix product states software have opened up the possibility to study many-body quantum physics in one and quasi-one-dimensional systems in an easily accessible package similar to density functional theory codes but for strongly correlated dynamics. Here, we address methods which allow one to capture the full entanglement without truncation of the Hilbert space. Such methods are suitable for validation of and comparisons to tensor network algorithms, but especially useful in the case of new kinds of quantum states with high entanglement violating the truncation in tensor networks. Quantum cellular automata are one example for such a system, characterized by tunable complexity, entanglement, and a large spread over the Hilbert space. Beyond the evolution of pure states as a closed system, we adapt the techniques for open quantum systems simulated via the Lindblad master equation. We present three algorithms for solving closed-system many-body time evolution without truncation of the Hilbert space. Exact diagonalization methods have the advantage that they not only keep the full entanglement but also require no approximations to the propagator. Seeking the limits of a maximal number of qubits on a single core, we use Trotter decompositions or Krylov approximation to the exponential of the Hamiltonian. All three methods are also implemented for open systems represented via the Lindblad master equation built from local channels. We show their convergence parameters and focus on efficient schemes for their implementations including Abelian symmetries, e.g. symmetry used for number conservation in the Bose–Hubbard model or discrete symmetries in the quantum Ising model. We present the thermalization timescale in the long-range quantum Ising model as a key example of how exact diagonalization contributes to novel physics.
The phenomenon of many-body localization generalizes Anderson localization to interacting systems. Understanding this conceptually novel phenomenon requires a study of many-body eigenstates at finite energy densities. This is a very challenging task since the most efficient numerical methods such as, e.g., the density matrix renormalization group method, can only access the ground state and low lying excitations. In this work, the authors introduce a unitary tensor network based variational method that approximately finds all many-body eigenstates of fully localized Hamiltonians and scales polynomially with system size. The usefulness of their approach is demonstrated by considering the Heisenberg chain in a strongly disordered magnetic field.
Preparing arbitrary quantum states requires exponential resources. Matrix Product States (MPS) admit more efficient constructions, particularly when accuracy is traded for circuit complexity. Existing approaches to MPS preparation mostly rely on heuristic circuits that are deterministic but quickly saturate in accuracy, or on variational optimization methods that reach high fidelities but scale poorly. This work introduces an end-to-end MPS preparation framework that combines the strengths of both strategies within a single pipeline. Heuristic staircase-like and brick wall disentangler circuits provide warm-start initializations for variational optimization, enabling high-fidelity state preparation for large systems. Target MPSs are either specified as physical quantum states or constructed from classical datasets via amplitude encoding, using step-by-step singular value decompositions or tensor cross interpolation. The framework incorporates entanglement-based qubit reordering, reformulated as a quadratic assignment problem, and low-level optimizations that reduce depths by up to 50% and CNOT counts by 33%. We evaluate the full pipeline on datasets of varying complexity across systems of 19-50 qubits and identify trade-offs between fidelity, gate count, and circuit depth. Optimized brick wall circuits typically achieve the lowest depths, while the optimized staircase-like circuits minimize gate counts. Overall, our results provide principled and scalable protocols for preparing MPSs as quantum circuits, supporting utility-scale applications on near-term quantum devices.
Tensor-network simulations of quantum many-body dynamics are fundamentally limited by entanglement build-up, which leads to exponentially growing computational costs. Furthermore, these classical simulation algorithms are inherently sequential as typically a tensor network representation of the quantum state is updated incrementally at each time step. We build on recently introduced randomized quantum algorithms for time evolution (TE-PAI), and adapt them to the classical simulation context with the purpose of enabling massive parallelisation. Our MPS TE-PAI approach achieves exact time evolution on average (unbiased estimator) and proceeds by representing an ensemble of randomized shallow Trotter-variant circuits as tensor networks. As each circuit instance yields a deterministic quantum state (or observable expecation value), the only source of randomness is the sampling of circuit variants; the absence of shot noise therefore yields a reduced estimator variance relative to quantum hardware implementations of TE-PAI. We simulate representative disordered one-dimensional spin-ring Hamiltonians, and numerically observe reductions in the per-sample gate-count by a factor of up to 10310^3103 relative to Trotterized MPS evolution, yielding orders of magnitude reduction in the time-to-solution under realistic levels of parallelisation. Finally, we numerically observe that MPS TE-PAI is substantially more robust against severe bond-dimension truncation than product formulas, potentially making it useful for the simulation of strongly correlated systems where truncation is necessary in practice. We also demonstrate that the approach can be used naturally in combination with existing time evolution algorithms, effectively extending their time depth via parallelisation.