arXiv:2606.09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale. In this work, we propose a framework of forward gradient estimators for PQCs, based on the forward mode of automatic differentiation, that yields an unbiased estimator of the gradient by averaging a freely tunable number of random directional derivatives and recovers SP
Advances in quantum computing research are consistently seeking to overcome fundamental limitations in hardware and algorithms, with gradient estimation being a key bottleneck for training quantum circuits.
This development represents a significant step towards practical and scalable training of parameterised quantum circuits, which are crucial for developing quantum algorithms and applications.
The proposed adaptive directional gradients could substantially reduce the computational cost of training quantum machine learning models, making quantum hardware more efficient to utilize.
- · Quantum computing hardware developers
- · Quantum machine learning researchers
- · High-performance computing sector
- · Current gradient estimation methods
- · Organizations with heavy reliance on classical optimization for quantum tasks
More efficient training of quantum machine learning models on existing quantum hardware.
Accelerated development and adoption of quantum algorithms across various industries.
Potential for quantum advantage in new computational domains currently limited by classical methods and quantum hardware constraints.
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