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arXiv:2410.19286 · Lecture notes in networks and systems

An Evidence of Addressing Coherence Errors in VQE Observables by Pulse-Level VQE Approach

Xiaoan Lin

Published Jan 1, 2025• 0 citations
arXivHTMLPDF
DOI
10.1007/978-3-031-84460-7_6

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Abstract We present a method to improve the convergence of variational algorithms based on hidden inverses (HIs) to mitigate coherent errors. In the context of error mitigation, this means replacing the hardware implementation of certain Hermitian gates with their inverses. Doing so results in noise cancellation and a more resilient quantum circuit. This approach improves performance in a variety of two-qubit error models where the noise operator also inverts with the gate inversion. We apply the mitigation scheme on superconducting quantum processors running the variational quantum eigensolver (VQE) algorithm to find the H 2 ground-state energy. When implemented on superconducting hardware we find that the mitigation scheme effectively reduces the energy fluctuations in the parameter learning path in VQE, reducing the number of iterations for a converged value. We also provide a detailed numerical simulation of VQE performance under different noise models and explore how HIs and randomized compiling affect the underlying loss landscape of the learning problem. These simulations help explain our experimental hardware outcomes, helping to connect lower-level gate performance to application-specific behavior in contrast to metrics like fidelity which often do not provide an intuitive insight into observed high level performance.
Variational quantum algorithms (VQAs) are key enablers of practical computations on near-term quantum computing hardware. However, gate-based ansatz suffer from decoherence and control errors, limiting scalability. Pulse-level approaches, such as control variational quantum eigen solver (ctrl-VQE), address these challenges by directly optimizing analog control fields and enabling time-optimal state preparation. Building on this paradigm, we present a reinforcement-learning (RL) framework that autonomously discovers high-fidelity, time-efficient control pulses. Using a hydrogen dimer testbed, our RL agent achieves state-fidelity above 0.995 and ground state energy errors near 0.005 Hartree, demonstrating the promise of AI-driven, hardware-efficient quantum algorithms.