Evaluating analytic gradients on quantum hardware

2018/11/27 by Maria Schuld, Ville Bergholm, Christian Gogolin +2 · 1,088 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Computation #Computer engineering #Computer science #Electronic circuit #Function (biology) #Mathematics #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum algorithm #Quantum and electron transport phenomena #Quantum circuit #Quantum computer #Quantum error correction #Quantum gate #Quantum mechanics #Qubit #Theoretical computer science #Topology (electrical circuits) #quant-ph

paper · pdf · doi:10.1103/physreva.99.032331

published in Physical Review A 99(3) (American Physical Society)

arxiv created 2018/11/27 · openalex publication_date 2019/03/21 · arxiv updated 2019/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

An important application for near-term quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning. In many settings, most prominently in so-called parametrized or variational algorithms, the objective function is a result of hybrid quantum-classical processing. To optimize the objective, it is useful to have access to exact gradients of quantum circuits with respect to gate parameters. This paper shows how gradients of expectation values of quantum measurements can be estimated using the same, or almost the same, architecture that executes the original circuit. It generalizes previous results for qubit-based platforms, and proposes recipes for the computation of gradients of continuous-variable circuits. Interestingly, in many important instances it is sufficient to run the original quantum circuit twice while shifting a single gate parameter to obtain the corresponding component of the gradient. More general cases can be solved by conditioning a single gate on an ancilla.

Citations

• Gaussian quantum information
• A simple all-microwave entangling gate for fixed-frequency superconducting qubits
• A variational eigenvalue solver on a photonic quantum processor

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