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arXiv:2409.04510 · The European Physical Journal A

Entropy-driven entanglement forging

Axel Pérez-Obiol, Sergi Masot-Llima, A. M. Romero, J. Fernández Menéndez, Arnau Rios, Artur García-Sáez, Bruno Juliá-Díaz

Published May 5, 2026• 2 citations
arXivHTMLPDF

Abstract

Abstract Simulating physical systems with variational quantum algorithms is a well-studied approach, but it is challenging to implement in current devices due to demands in qubit number and circuit depth. However, limited knowledge of a system can reduce the cost of these algorithms. We demonstrate that the computational overhead in simulating many-body systems can be systematically decreased by leveraging the system’s symmetries, which guide the choice of iterative entanglement forging partitions aligned with the underlying entropy structure. To do so, we simulate a Fermi-Hubbard one-dimensional chain with a parametrized hopping term, as well as atomic nuclei $${}^{28}$$ 28 Ne and $${}^{60}$$ 60 Ti with the nuclear shell model. Within an adaptive variational quantum eigensolver setting, we propose an algorithm that employs partitions and subpartitions of the quantum circuit to reduce significantly the quantum resources compared to a fully-fledged simulation. Entropy-driven entanglement forging (EDEF) does not require access to the full statevector or exact entropies; in hardware it is implemented via standard EF expectation-reconstruction rules with crossed statistics, while statevector access is used here only as a classical simulation shortcut. We find that the maximum number of qubits can be reduced by up to a factor of four and we observe an order of magnitude decrease in the number of two-qubit gates compared to regular simulations. Our findings indicate that our method, entropy-driven entanglement forging, can be used to adjust quantum simulations to the limitations of noisy intermediate-scale quantum devices.

DOI
10.1140/epja/s10050-026-01853-0

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Abstract Simulating physical systems with variational quantum algorithms is a well-studied approach, but it is challenging to implement in current devices due to demands in qubit number and circuit depth. However, limited knowledge of a system can reduce the cost of these algorithms. We demonstrate that the computational overhead in simulating many-body systems can be systematically decreased by leveraging the system’s symmetries, which guide the choice of iterative entanglement forging partitions aligned with the underlying entropy structure. To do so, we simulate a Fermi-Hubbard one-dimensional chain with a parametrized hopping term, as well as atomic nuclei 28{}^{28}28 28 Ne and 60{}^{60}60 60 Ti with the nuclear shell model. Within an adaptive variational quantum eigensolver setting, we propose an algorithm that employs partitions and subpartitions of the quantum circuit to reduce significantly the quantum resources compared to a fully-fledged simulation. Entropy-driven entanglement forging (EDEF) does not require access to the full statevector or exact entropies; in hardware it is implemented via standard EF expectation-reconstruction rules with crossed statistics, while statevector access is used here only as a classical simulation shortcut. We find that the maximum number of qubits can be reduced by up to a factor of four and we observe an order of magnitude decrease in the number of two-qubit gates compared to regular simulations. Our findings indicate that our method, entropy-driven entanglement forging, can be used to adjust quantum simulations to the limitations of noisy intermediate-scale quantum devices.
Background: Quantum simulations offer the potential to predict the structure and dynamics of nuclear many-body systems that are beyond the capabilities of classical computing. Generally, preparing the ground state of strongly interacting many-body systems relevant to nuclear physics is, however, inefficient, even using ideal quantum computers. In addition, currently available noisy intermediate-scale quantum (NISQ) era quantum devices possess modest numbers of qubits, limiting the size of quantum many-body systems that can be simulated. In this context, a reformulation of the quantum many-body problems using truncated model spaces and Hamiltonians is desirable to make them more amenable to near-term quantum computers. The importance of symmetries in low-energy theories, including effective field theories (EFTs), lattice quantum chromodynamics (QCD), and effective model spaces for nuclear systems, in particular their interplay with the reduction of active Hilbert spaces, is well known. Lesser known is the fact that the noncommutivity of some symmetries and truncations of the model space can be profitably combined with variational calculations to rearrange the entanglement into localized structures and enable more efficient simulations.Purpose: The goal of the present study is to explore and utilize the noncommutivity of symmetries and model-space truncations of quantum many-body systems important to nuclear physics, particularly in combination with variational algorithms for quantum simulations and effective Hamiltonian learning.Method: We introduce an iterative hybrid classical-quantum algorithm, the Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wave function. Quantum simulations, using classical computers and IBM's superconducting-qubit quantum computers, are performed to demonstrate the HL-VQE algorithm, in the context of the Lipkin-Meshkov-Glick (LMG) model of interacting fermions, where the Hamiltonian transformation corresponds to an orbital rotation. We use a mapping where the number of qubits scales with the logarithm\mathrm{logarithm}logarithm of the size of the effective model space, rather than the particle number.Results: HL-VQE is found to provide an exponential improvement in LMG-model calculations of the ground-state energy and wave function, compared to naive truncations without Hamiltonian learning, throughout a significant fraction of the Hilbert space. In the context of EFT, this corresponds to counterterms scaling exponentially with the cutoff as opposed to power law. Implementations on IBM's QExperience quantum computers and simulators for one- and two-qubit effective model spaces are shown to provide accurate and precise results, reproducing classical predictions.Conclusions: For a range of parameters defining the LMG model, the HL-VQE algorithm is found to have better scaling of quantum resources requirements than previously explored algorithms. In particular, the HL-VQE scales efficiently over a large fraction of the model space, in contrast to VQE alone. This work constitutes a step in the development of entanglement-driven quantum algorithms for descriptions of nuclear many-body systems. This, in part, leverages the potential of noisy intermediate-scale quantum (NISQ) devices. The exponential scaling of counterterms observed in this study suggests the possibility of more general applicability to other nonperturbative EFTs.
Entanglement forging based variational algorithms leverage the bipartition of quantum systems for addressing ground-state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis states, or bitstrings, when performing the Schmidt decomposition of the whole system. To overcome this challenge, we propose a method for entanglement forging employing generative neural networks to identify the most pertinent bitstrings, eliminating the need for the exponential sum. Through empirical demonstrations on systems of increasing complexity, we show that the proposed algorithm achieves comparable or superior performance compared to the existing standard implementation of entanglement forging. Moreover, by controlling the amount of required resources, this scheme can be applied to larger as well as non-permutation-invariant systems, where the latter constraint is associated with the Heisenberg forging procedure. We substantiate our findings through numerical simulations conducted on spin models exhibiting one-dimensional rings, two-dimensional triangular lattice topologies, and nuclear shell model configurations. Published by the American Physical Society 2024
Variational algorithms have received significant attention in recent years due to their potential to solve practical problems using noisy intermediate-scale quantum (NISQ) devices. A fundamental step of these algorithms is the evaluation of the expected value of Hamiltonians, and hence efficient schemes to perform this task are required. The standard approach employs local measurements of Pauli operators and requires a large number of circuits. An alternative is to make use of entangled measurements, which might introduce additional gates between physically disconnected qubits that harm the performance. As a solution to this problem, we propose hardware-efficient entangled measurements (HEEM), that is, measurements that permit only entanglement between physically connected qubits. We show that this strategy enhances the evaluation of molecular Hamiltonians in NISQ devices by reducing the number of circuits required without increasing their depth. We provide quantitative metrics of how this approach offers better results than local measurements and arbitrarily entangled measurements. We estimate the ground-state energy of the H2_22​O molecule with classical simulators and quantum hardware using the variational quantum eigensolver with HEEM.
Variational algorithms have received significant attention in recent years due to their potential to solve practical problems using noisy intermediate-scale quantum (NISQ) devices. A fundamental step of these algorithms is the evaluation of the expected value of Hamiltonians, and hence efficient schemes to perform this task are required. The standard approach employs local measurements of Pauli operators and requires a large number of circuits. An alternative is to use entangled measurements, which might introduce additional gates between physically disconnected qubits that harm the performance. To solve this problem, we propose hardware-efficient entangled measurements (HEEMs), that is, measurements that permit only entanglement between physically connected qubits. We show that this strategy enhances the evaluation of molecular Hamiltonians in NISQ devices by reducing the required circuits without increasing their depth. We provide quantitative metrics of how this approach offers better results than local measurements and arbitrarily entangled measurements. We estimate the ground-state energy of the H2O{\mathrm{H}}_{2}\mathrm{O}H2​O molecule with classical simulators and quantum hardware using the variational quantum eigensolver with HEEM.
Quantum computers promise an exciting path to the scalable simulation of quantum matter. However, in their nascent state, the accessible gate depth of quantum computation on available hardware remains highly constrained. In this thesis, I present a suite of hybrid quantum-classical algorithms that aid Hamiltonian simulation across both the Noisy Intermediate-Scale Quantum era and future fault-tolerant architectures. By treating classical computation as an active resource to refine quantum algorithms, I develop automated, variational methods to learn hardware-efficient circuits. This work aims to overcome traditional trainability barriers, such as barren plateaus, by embedding physical symmetries directly into the circuit design. Central to the thesis is the synergistic use of tensor networks to classically pre-compute weakly entangled stages of a quantum simulation. This enables the compression of deep circuits, drastically reducing both the accumulation of gate errors on near-term physical qubits and the prohibitive overhead of operations like magic state distillation on error-corrected hardware, reserving the quantum processor strictly for the most challenging, highly entangled dynamics. These methodologies are ultimately directed toward the ab-initio simulation of subatomic physics, providing a scalable approach for the first-principles study of strongly correlated systems, including nuclear structure and lattice gauge theories.
In noisy intermediate-scale quantum (NISQ) era, distributed quantum computing (DQC) has emerged as a promising direction to address limitations in qubit scalability and noise resilience. Entanglement forging is proposed as an important DQC protocol that enables the integration of the quantum computing resources and exhibits significant efficiency in realizing quantum states and dynamics with shallow entanglement. Here, we experimentally demonstrate the entanglement forging process for solving an eight-vertex Max-Cut problem. We execute parametrized quantum circuits on a four-qubit nuclear magnetic resonance quantum processor and successfully achieve the entanglement forging of the ground state for the eight-qubit Max-Cut Hamiltonian by using variational quantum eigensolver. The ground state reconstructed in experiment achieves a fidelity of 95.88% compared with theoretical prediction, which provides three correct solutions to the Max-Cut problem in parallel. Our work demonstrates the feasibility and efficiency of entanglement forging protocol in solving combinatorial optimization problems on NISQ devices, while also advancing the development of distributed quantum computing.