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Tangelo: An Open-source Python Package for End-to-end Chemistry Workflows on Quantum Computers

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arxiv 2206.12424 v1 pith:T4CU6B5L submitted 2022-06-24 quant-ph

classification quant-ph
keywords quantumtangelopackagechemistrycomputersdesigndevelopmentenables
verification ladder T0 review T1 audit T2 compute T3 formal
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Tangelo [link: https://github.com/goodchemistryco/Tangelo] is an open-source Python software package for the development of end-to-end chemistry workflows on quantum computers, released under Apache 2.0 license. It aims to support the design of successful experiments on quantum hardware, and to facilitate advances in quantum algorithm development. The software enables quick exploration of different approaches by assembling reusable building blocks and algorithms, with the flexibility to let users introduce their own. Tangelo is backend-agnostic and enables switching between various backends (Braket, Qiskit, Qulacs, Azure Quantum, QDK, Cirq...) with minimal changes in the code. The package can be used to explore quantum computing applications such as open-shell systems, excited states, or more industrially-relevant systems by leveraging problem decomposition at scale. This paper outlines the design choices, philosophy, and main features of Tangelo.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards quantum-centric simulations of extended molecules: sample-based quantum diagonalization enhanced with density matrix embedding theory

    quant-ph 2024-11 conditional novelty 7.0 of 10

    The first implementation of density matrix embedding theory with sample-based quantum diagonalization is demonstrated on quantum hardware for an 18-hydrogen ring and cyclohexane, yielding energies close to classical r...

  2. Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework

    quant-ph 2025-11 unverdicted novelty 6.0 of 10

    Using DMET to fragment molecules and SQD to solve the fragments on IBM hardware, the authors report ground-state energies for eight ligand-like molecules that agree with DMET-FCI to within about 10⁻⁶ Hartree.

  3. Reducing entanglement with a Hamiltonian derived Clifford transformation

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    A Hamiltonian-derived Clifford circuit gives a cheap classical approximation between MP2 and CISD accuracy and an entanglement-reduced qubit basis that improves DMRG and VQE resource counts.

  4. Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework

    quant-ph 2026-07 conditional novelty 4.0 of 10

    An RBM-guided selected-CI solver inside DMET reaches the DMET-CASCI energy within 1.6 mHa using ~4% of the symmetry-valid configuration subspace on an 11-fragment protein–ligand model.

  5. Quantum computing of magnetic-skyrmion-like patterns in Heisenberg ferromagnets

    quant-ph 2025-05 conditional novelty 4.0 of 10

    A simulator-based quantum eigensolver reveals field-driven discontinuities in a small Heisenberg ferromagnet, which the authors read as hints of zero-temperature skyrmion-like magnetic patterns.

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