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arXiv:2406.12130 · Physical Review A

Efficient particle-conserving symmetric quantum circuits

Babatunde M. Ayeni

Published Feb 25, 2025• 1 citation
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Abstract

The variational quantum simulation technique on gate-based quantum processors can be made more efficient if symmetries of the Hamiltonian system of interest are factored into the design construction of the underlying parameterized quantum circuits (PQC) needed by the technique. In the first part of this work, we present an easy and practical approach that can be used to construct symmetric quantum circuits. The method we present can directly be adapted for any symmetry, though in the present work we focus explicitly on the examples of Z 2 , Z 3 , and particle number conservation. We show how to construct symmetric gates that respects the aforementioned symmetries. In the second part, we focus on numerical analysis of parameterized particle-conserving gates. In variational quantum simulation, for example, the variational quantum eigensolver (VQE), the symmetric gates of the PQC are parameterized. Since there are often many possible inequivalent parametrizations, it is often not immediately obvious which gate parametrization would be most efficient in general for a wide range of Hamiltonians that respect the symmetry. We streamline our analysis to particle-conserving circuits and Hamiltonians. We numerically analyze the efficiency of different particle-conserving gate parametrizations by studying two examples, namely, a physics problem of learning the ground state of Heisenberg spin chains, and an artificial problem of learning the random arbitary states sampled within the particle number conserved subspace.

DOI
10.1103/physreva.111.022629

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The variational quantum simulation technique on gate-based quantum processors can be made more efficient if symmetries of the Hamiltonian system of interest are factored into the design construction of the underlying parameterized quantum circuits (PQC) needed by the technique. In the first part of this work, we present an easy and practical approach that can be used to construct symmetric quantum circuits. The method we present can directly be adapted for any symmetry, though in the present work we focus explicitly on the examples of Z 2 , Z 3 , and particle number conservation. We show how to construct symmetric gates that respects the aforementioned symmetries. In the second part, we focus on numerical analysis of parameterized particle-conserving gates. In variational quantum simulation, for example, the variational quantum eigensolver (VQE), the symmetric gates of the PQC are parameterized. Since there are often many possible inequivalent parametrizations, it is often not immediately obvious which gate parametrization would be most efficient in general for a wide range of Hamiltonians that respect the symmetry. We streamline our analysis to particle-conserving circuits and Hamiltonians. We numerically analyze the efficiency of different particle-conserving gate parametrizations by studying two examples, namely, a physics problem of learning the ground state of Heisenberg spin chains, and an artificial problem of learning the random arbitary states sampled within the particle number conserved subspace.
In the age of noisy quantum processors, the exploitation of quantum symmetries can be quite beneficial in the efficient preparation of trial states, an important part of the variational quantum eigensolver algorithm. The benefits include building quantum circuits which are more compact, with lesser number of paramaters, and more robust to noise, than their non-symmetric counterparts. Leveraging on ideas from representation theory we show how to construct symmetric quantum circuits. Similar ideas have been previously used in the field of tensor networks to construct symmetric tensor networks. We focus on the specific case of particle number conservation, that is systems with U(1) symmetry. Based on the representation theory of U(1), we show how to derive the particle-conserving exchange gates, which are commonly used in constructing hardware-efficient quantum circuits for fermionic systems, like in quantum chemistry, material science, and condensed-matter physics. We tested the effectiveness of our circuits with the Heisenberg XXZ model.
Abstract The variational quantum eigensolver is one of the most promising approaches for performing chemistry simulations using noisy intermediate-scale quantum (NISQ) processors. The efficiency of this algorithm depends crucially on the ability to prepare multi-qubit trial states on the quantum processor that either include, or at least closely approximate, the actual energy eigenstates of the problem being simulated while avoiding states that have little overlap with them. Symmetries play a central role in determining the best trial states. Here, we present efficient state preparation circuits that respect particle number, total spin, spin projection, and time-reversal symmetries. These circuits contain the minimal number of variational parameters needed to fully span the appropriate symmetry subspace dictated by the chemistry problem while avoiding all irrelevant sectors of Hilbert space. We show how to construct these circuits for arbitrary numbers of orbitals, electrons, and spin quantum numbers, and we provide explicit decompositions and gate counts in terms of standard gate sets in each case. We test our circuits in quantum simulations of the H2{H}_{2}H2​ H 2 and LiHLiHLiH L i H molecules and find that they outperform standard state preparation methods in terms of both accuracy and circuit depth.
Variational quantum algorithms are among the most promising approaches for simulating interacting quantum many-body systems on noisy intermediate-scale quantum (NISQ) devices. However, the practical success of variational quantum eigensolvers (VQE) critically depends on the structure of the chosen variational ansatz. In this work, we investigate the ground-state properties of the one-dimensional antiferromagnetic Heisenberg spin-1/2 chain using both generic hardware-efficient ansatz and physics-informed, symmetry-preserving variational circuits. We benchmark variational results against exact diagonalization and noiseless simulations, and subsequently validate the approach on real IQM Garnet quantum hardware. Our results demonstrate that incorporating physical symmetries into the circuit design leads to significantly improved energy estimates, enhanced robustness against hardware noise, and clearer convergence behavior when compared to hardware-efficient ansatz under identical resource constraints. These findings highlight the importance of problem specific ansatz construction for reliable quantum simulations in the NISQ era.
In the current framework of geometric quantum machine learning, the canonical method for constructing a variational ansatz that respects the symmetry of some group action is by forcing the circuit to be equivariant, i.e., to commute with the action of the group. This can, however, be an overzealous constraint that greatly limits the expressivity of the circuit, especially in the case of continuous symmetries. We propose an alternative paradigm for the symmetry-informed construction of variational quantum circuits, based on homogeneous spaces, relaxing the overly stringent requirement of equivariance. We achieve this by introducing horizontal quantum gates, which only transform the state with respect to the directions orthogonal to those of the symmetry. We show that horizontal quantum gates are much more expressive than equivariant gates, and thus can solve problems that equivariant circuits cannot. For instance, a circuit comprised of horizontal gates can find the ground state of an SU ( 2 ) -symmetric model where the ground state spin sector is unknown—a task where equivariant circuits fall short. Moreover, for a particular subclass of horizontal gates based on symmetric spaces, we can obtain efficient circuit decompositions for our gates through the KAK theorem. Finally, we highlight a particular class of horizontal quantum gates that behave similarly to general SU ( 4 ) gates, while achieving a quadratic reduction in the number of parameters for a generic problem.
Abstract Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates employed and by the correlations of their parameters. While much research has been devoted to devising adequate gate-sets, typically respecting some symmetries of the problem, very little is known about how their parameters should be structured. In this work, we show that an ideal parameter structure naturally emerges when carefully considering spatial symmetries (i.e. the symmetries that are permutations of parts of the system under study). Namely, we consider the automorphism group of the problem Hamiltonian, leading us to develop a circuit construction that is equivariant under this symmetry group. The benefits of our novel circuitstructure, called ORB, are numerically probed in several ground-state problems. We find a consistent improvement (in terms of circuit depth, number of parameters required, and gradient magnitudes) compared to literature circuit constructions.
Simulating physical systems on near-term quantum computers often requires preparing states within constrained subspaces, like those with fixed particle number or spin. We use Lie algebraic techniques to prove that hardware-efficient gates are universal for state preparation in these subspaces. The key mechanism is Pauli ZZZ dressing: commutators of overlapping gates produce Pauli ZZZ operators on shared qubits, acting as spectator projectors that decompose multi-plane rotations into single-plane generators spanning the full so(w)\mathfrak{so}(w)so(w) algebra, where www is the dimension of the constrained subspace, thereby guaranteeing universality for real state preparation. Adding independent complex phases extends this to su(w)\mathfrak{su}(w)su(w), enabling arbitrary complex state preparation. We provide a computationally efficient Jacobian criterion for verifying that a circuit can explore any direction on the target manifold from almost any parameter configuration. Our findings are applicable to many problem areas, including Fermi-Hubbard models, Bose-Hubbard models, and molecular electronic structure. We apply our framework to two physical settings: we prove the completeness of the binary encoded multi-level particles ansatz on the conserved-particle-number subspace, and we construct symmetry-preserving circuits for the fuzzy sphere regularisation of the 3D Ising conformal field theory (CFT). For the latter, we variationally prepare the ground and excited states and extract some CFT data.