Evaluation of Parameterized Quantum Circuits with Cross-Resonance Pulse-Driven Entanglers

Abstract

Variational Quantum Algorithms (VQAs) have emerged as a powerful class of algorithms that is highly suitable for noisy quantum devices. Therefore, investigating their design has become key in quantum computing research. Previous works have shown that choosing an effective parameterized quantum circuit (PQC) or ansatz for VQAs is crucial to their overall performance, especially on near-term devices. In this paper, we utilize pulse-level access to quantum machines and our understanding of their two-qubit interactions to optimize the design of two-qubit entanglers in a manner suitable for VQAs. Our analysis results show that pulse-optimized ansatze reduce state preparation times by more than half, maintain expressibility relative to standard PQCs, and are more trainable through local cost function analysis. Our algorithm performance results show that in three cases, our PQC configuration outperforms the base implementation. Our algorithm performance results, executed on IBM Quantum hardware, demonstrate that our pulse-optimized PQC configurations are more capable of solving MaxCut and Chemistry problems compared to a standard configuration.

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• Pulse-efficient quantum machine learning
• Here comes the SU(N): multivariate quantum gates and gradients
• Scalable Quantum Simulations of Scattering in Scalar Field Theory on 120 Qubits
• Robustness of Variational Quantum Algorithms against stochastic parameter perturbation
• Optimal realization of Yang-Baxter gate on quantum computers
• State preparation of lattice field theories using quantum optimal control
• Framework for Learning and Control in the Classical and Quantum Domains
• Minimal evolution times for fast, pulse-based state preparation in silicon spin qubits
• Crosstalk-Based Parameterized Quantum Circuit Approximation
• High-expressibility Quantum Neural Networks using only classical resources