Low $T$-count preparation of nuclear eigenstates with tensor networks

• URL: http://arxiv.org/abs/2603.11156v1
• Date: Wed, 11 Mar 2026 18:00:00 GMT
• Title: Low $T$-count preparation of nuclear eigenstates with tensor networks
• Abstract summary: We present an efficient protocol to support Initial State Preparation for strongly correlated fermionic systems.<n>We first demonstrate that the Density Matrix Renormalization Group algorithm can efficiently approximate target states as Matrix Product States.<n>These high-fidelity approximations are then leveraged as a classical resource in a variational circuit optimization scheme to compile shallow quantum circuits.
• Score: 0.11083289076967894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an efficient protocol leveraging classical computation to support Initial State Preparation for strongly correlated fermionic systems, a critical bottleneck for fault-tolerant quantum simulation. Focusing on nuclear shell model eigenstates, we first demonstrate that the Density Matrix Renormalization Group algorithm can efficiently approximate target states as Matrix Product States, capitalizing on the favourable entanglement structure of these fermionic systems. These high-fidelity approximations are then leveraged as a classical resource in a variational circuit optimization scheme to compile shallow quantum circuits. We establish concrete resource estimates by decomposing the resulting circuits into the industry-standard Clifford$+T$ gateset, exploring the benefits of specialized $U3$ synthesis techniques. For all nuclear systems tested, on up to 76 qubit Hamiltonians, we consistently find low $T$-count circuits preparing the nuclear eigenstates to high fidelity with $\sim 2\times 10^4$ total $T$ gates. This low number gives confidence these eigenstates can be prepared on early fault-tolerant quantum computers. Our work establishes a viable path toward practical ground state preparation for nuclear structure and other fermionic applications.

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