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Gate-free state preparation for fast variational quantum eigensolver simulations: ctrl-VQE

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arxiv 2008.04302 v3 pith:VFSI74P3 submitted 2020-08-10 quant-ph cond-mat.str-elphysics.chem-ph

Gate-free state preparation for fast variational quantum eigensolver simulations: ctrl-VQE

classification quant-ph cond-mat.str-elphysics.chem-ph
keywords quantumstatealgorithmpreparationbondcircuitcoherencectrl-vqe
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The variational quantum eigensolver (VQE) is currently the flagship algorithm for solving electronic structure problems on near-term quantum computers. This hybrid quantum/classical algorithm involves implementing a sequence of parameterized gates on quantum hardware to generate a target quantum state, and then measuring the expectation value of the molecular Hamiltonian. Due to finite coherence times and frequent gate errors, the number of gates that can be implemented remains limited on current quantum devices, preventing accurate applications to systems with significant entanglement, such as strongly correlated molecules. In this work, we propose an alternative algorithm (which we refer to as ctrl-VQE) where the quantum circuit used for state preparation is removed entirely and replaced by a quantum control routine which variationally shapes a pulse to drive the initial Hartree-Fock state to the full CI target state. As with VQE, the objective function optimized is the expectation value of the qubit-mapped molecular Hamiltonian. However, by removing the quantum circuit, the coherence times required for state preparation can be drastically reduced by directly optimizing the pulses. We demonstrate the potential of this method numerically by directly optimizing pulse shapes which accurately model the dissociation curves of the hydrogen molecule (covalent bond) and helium hydride ion (ionic bond), and we compute the single point energy for LiH with four transmons.

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Cited by 3 Pith papers

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

  1. Software Between Quantum and Machine Learning -- And Down to Pulses

    quant-ph 2026-05 unverdicted novelty 5.0

    A JAX-based framework extending quantum machine learning to pulse-level control with composable ansatzes, end-to-end optimization, and Fourier diagnostics.

  2. Pulsed learning for quantum data re-uploading models

    quant-ph 2025-12 conditional novelty 5.0

    A pulse-level data re-uploading classifier outperforms its gate-based counterpart in noisy superconducting-qubit simulation.

  3. Software Between Quantum and Machine Learning -- And Down to Pulses

    quant-ph 2026-05 unverdicted novelty 4.0

    Introduces a JAX-based framework for pulse-level QML with composable ansatze, end-to-end pulse optimization, and Fourier-analytic diagnostics.