mlsanityData to 14 Oct 2026

Differentiable Analog Quantum Computing for Optimization and Control

arXiv 2210.15812 ↗First posted 28 Oct 2022quant-ph cs.LGcited by 4

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Abstract (arXiv)

We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which leads to a quantum stochastic gradient descent algorithm for scalable gradient-based training in our framework. Applying our framework to quantum optimization and control, we observe a significant advantage of differentiable analog quantum computing against SOTAs based on parameterized digital quantum circuits by orders of magnitude.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 73 references that are in the map. First posted on arXiv 28 Oct 2022.

Builds on

23 of 73 references.

References · 23

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11 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2022QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learningcited by 7
  2. 2023Efficient Quantum Algorithms for Quantum Optimal Controlcited by 1
  3. 2025ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computerscited by 1
  4. 2024Differentiable Quantum Computing for Large-scale Linear Controlcited by 0