Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
Quantum Circuit Born Machines (QCBMs) offer a natural approach to generative machine learning by leveraging the Born rule. Recent work has provided a method to classically train QCBMs with Instantaneous Quantum Polynomial (IQP) circuits via the Maximum Mean Discrepancy (MMD) loss. Despite the assumed intractability of sampling from IQP circuits classically, their expectation values can be computed classically, enabling training of these IQP QCBMs. However, quantum machine learning (QML) models have various other challenges, including trainability issues caused by exponential concentration or barren plateaus. While these issues have been explored for parameters sampled from a uniform distr...
| Authors | Gennaro De Luca, Vinayak Sharma, Aviral Shrivastava |
|---|---|
| Primary category | quant-ph |
| Published | 8 June 2026 |
| Last updated at source | 27 September 2026 |
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