QML Suite: Quantum Machine Learning for Large-Scale Applications

In this talk, we introduce QML Suite: Zapata Computing's library to support large-scale quantum machine learning tasks in application domains ranging from quantum generative modeling, combinatorial optimization, and the optimization and visualization of parametrized quantum circuits. We demonstrate composability, highlighting how components in hybrid architectures can be swapped for other implementations or to compare against classical benchmarks. We present three applications that leverage quantum-inspired or hybrid quantum-classical architectures, highlighting the potential regimes in which an advantage is achieved by means of our techniques and the flexibility of our platform. We present results obtained with QML Suite to assist the solution of industrial-scale combinatorial optimization problems with tensor-network-based generative models, the design of quantum-assisted generative adversarial networks and a strategy to mitigate barren plateaus with a flexible meta-learning initialization of parameters in variational quantum algorithms.