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arXiv:2408.04729 · Physical Review A

Dicke states as matrix product states

David Raveh, Rafael I. Nepomechie

Published Nov 26, 2024• 7 citations
arXivHTMLPDF

Abstract

We derive an exact canonical matrix product state (MPS) representation for Dicke states $|{D}_{k}^{n}\ensuremath{\rangle}$ with a minimal bond dimension $\ensuremath{\chi}=k+1$, for general values of $n$ and $k$, for which the $W$ state is the simplest case $k=1$. We use this MPS to formulate a quantum circuit for sequentially preparing Dicke states deterministically, relating it to the recursive algorithm of B\"artschi and Eidenbenz. We also find exact canonical MPS representations with a minimal bond dimension for higher-spin and qudit Dicke states.

DOI
10.1103/physreva.110.052438

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We derive an exact canonical matrix product state (MPS) representation for Dicke states ∣Dkn\ensuremath⟩|{D}_{k}^{n}\ensuremath{\rangle}∣Dkn​\ensuremath⟩ with a minimal bond dimension \ensuremathχ=k+1\ensuremath{\chi}=k+1\ensuremathχ=k+1, for general values of nnn and kkk, for which the WWW state is the simplest case k=1k=1k=1. We use this MPS to formulate a quantum circuit for sequentially preparing Dicke states deterministically, relating it to the recursive algorithm of B"artschi and Eidenbenz. We also find exact canonical MPS representations with a minimal bond dimension for higher-spin and qudit Dicke states.
Abstract Dicke states are permutation-invariant superpositions of qubit computational basis states, which play a prominent role in quantum information science. We consider here two higher-dimensional generalizations of these states: SU ( d ) spin- s Dicke states and SU ( 2 ) Dicke states. We present various ways of preparing both types of qudit Dicke states on a qudit quantum computer, using two main approaches: a deterministic approach, based on exact canonical matrix product state representations; and a probabilistic approach, based on quantum phase estimation. The quantum circuits are explicit and straightforward, and are arguably simpler than those previously reported.
Quantum states with conserved total spins, or spin eigenfunctions, are important for studying quantum chemistry and quantum manybody physics problems. A typical class of spin eigenfunctions are Dicke states, which attain maximal spins. While we already have many efficient quantum algorithms to prepare Dicke states, it is not yet clear if we could do so for arbitrary spin eigenfunctions deterministically. Generalizing Bärtschi and Eidenbenz's elegant algorithms for Dicke state preparation, we successfully prepare arbitrary spin eigenfunctions characterized by branching paths and binary spin trees. As a byproduct, we also develop the corresponding classical algorithms to reconstruct all these spin states.
Abstract The notion of spin‐ Dicke states is introduced, which are higher‐spin generalizations of usual (spin‐1/2) Dicke states. These multi‐qudit states can be expressed as superpositions of qudit Dicke states. They satisfy a recursion formula, which is used to formulate an efficient quantum circuit for their preparation, whose size scales as , where is the number of qudits and is the number of times the total spin‐lowering operator is applied to the highest‐weight state. The algorithm is deterministic and does not require ancillary qudits.
Preparing matrix product states (MPS) on a quantum device is a key subroutine in many quantum algorithms. The most competitive methods, based on the renormalisation group, prepare translationally invariant MPS of size LLL and bond dimension χχχ, up to an error ε\varepsilonε, in circuit depth O~(χ4log⁡(L/ε))\tilde O(χ^{4}\log(L/\varepsilon))O~(χ4log(L/ε)) or O~(χ6log⁡log⁡(L/ε))\tilde O(χ^{6}\log\log(L/\varepsilon))O~(χ6loglog(L/ε)). We improve multiple aspects of these methods. First, using block-encoded correction maps, whose post-selection succeeds with constant probability, we render the preparation exact without sacrificing the scaling in LLL. Second, through a generalisation of oblivious amplitude amplification to isometries, we reduce the bond-dimension dependence, improving the depth to O~(χ2log⁡L+χ4)\tilde O(χ^{2}\log L + χ^{4})O~(χ2logL+χ4) or O~(χ2log⁡log⁡L+χ4)\tilde O(χ^{2}\log\log L + χ^{4})O~(χ2loglogL+χ4), and even to O~(χ3log⁡L)\tilde O(χ^{3}\log L)O~(χ3logL) for incoherent preparations. Finally, we extend the framework to non-translationally invariant MPS and prove logarithmic-depth exact preparation for independent and identically distributed random tensor sequences. Confirmed by numerical studies, these results constitute, to the best of our knowledge, the most efficient exact MPS preparation protocols in the relevant parameter regimes.
We study the classical compilation of quantum circuits for the preparation of matrix product states (MPS), which are quantum states of low entanglement with an efficient classical description. Our algorithm represents a near-term alternative to previous sequential approaches by reverse application of a disentangler, which can be found by minimizing bipartite entanglement measures after the application of a layer of parametrized disentangling gates. Since a successful disentangler is expected to decrease the bond dimension on average, such a layer-by-layer optimization remains classically efficient even for deep circuits. Additionally, as the Schmidt coefficients of all bonds are locally accessible through the canonical Γ − Λ form of an MPS, the optimization algorithm can be heavily parallelized. We discuss guarantees and limitations to trainability and show numerical results for ground states of one-dimensional, local Hamiltonians as well as artificially spread out entanglement among multiple qubits using error-correcting codes.
These notes provide an introduction to tensor-network methods in quantum many-body physics, with an emphasis on matrix-product states (MPS). They develop the basic tensor-network language, including graphical notation, virtual indices, bond dimensions, gauge freedom, canonical forms, QR and singular-value decompositions, and the role of entanglement in controlling the efficiency of the representation. The main MPS algorithms are then introduced, including contractions, correlation functions, matrix-product operators, DMRG, and time-evolution methods. The notes also briefly discuss projected entangled-pair states (PEPS) as a higher-dimensional generalization of MPS, together with the basic ideas behind approximate PEPS contraction. Finally, tensor-network representations of mixed states, quantum channels, and Lindblad dynamics are presented, with applications to thermal states and open quantum systems. The presentation is accompanied by short Julia code examples based on ITensor, ITensorMPS, and TensorMixedStates. These notes were written for the 9th Les Houches Summer School on Computational Physics: Open Quantum Systems, held in June 2026.