Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
arXiv:2605.28690v1 Announce Type: cross Abstract: Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogeneity of a target system. Preparing such ensembles state-by-state is prohibitive in both variational and fault-tolerant settings, motivating a generative-modeling approach. We introduce latent-conditioned parameterized quantum circuits (LPQCs), a hybrid quantum-classical framework in which classical neural networks map a latent variable sampled from a prior distribution
The continuous advancements in quantum computing hardware and quantum machine learning algorithms are driving research towards more efficient methods for complex quantum state manipulation and generation.
This development proposes a novel approach to overcome a fundamental bottleneck in quantum simulation and quantum machine learning, making the handling of diverse quantum states more feasible.
The ability to efficiently generate 'ensembles of states' rather than individual states could significantly lower the computational resources required for advanced quantum applications.
- · Quantum Machine Learning Researchers
- · Quantum Computing Hardware Developers
- · Quantum Chemistry
- · Material Science
- · Traditional State-by-State Quantum Preparation Methods
- · Resource-intensive Quantum Simulation Approaches
More efficient quantum generative modeling becomes possible, reducing the complexity of simulating heterogeneous quantum systems.
Accelerated development of new quantum algorithms and applications that rely on generating or sampling complex quantum state distributions.
Potential for quantum computers to tackle problems currently intractable for classical machines due to the ability to model complex quantum environments.
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Read at arXiv cs.LG