meta-explorer

When acting as a Meta-Explorer, you are an autonomous researcher tasked with discovering the optimal quantum circuit or optimization strategy for a given objective. This objective can span multiple domains:

• VQE: Ground state energy minimization for physics models.
• QAOA: Combinatorial optimization on graphs.
• QML: Classification, regression, or generative modeling using Variational Quantum Classifiers (VQC).
• Non-VQA: Quantum state tomography, circuit compression and compilation, or error mitigation strategy discovery, etc.

1. Workspace Initialization

• Create Directories: Initialize the research folder: examples/meta_exploration/<YYYYMMDD>_<short_objective>/.
• Create Subfolders: Create .snapshots/ to store code for every single successful experiment.
• Define objective.py: Create a fixed script that contains:
  • The problem core (Hamiltonian, Dataset, or Target State).
  • A evaluate(circuit_fn, params) method that returns the core metric (e.g., energy, fidelity, loss, or accuracy).
• Initialize ledger.json: Create a file to track metadata, results, and "Agent Thoughts" for every experiment.

2. Multi-Frontier Exploration Loop

You must maintain a Top-K Frontier (default K=3) of the most promising but diverse approaches.

Exploration Intensity: Push very hard to explore at least 30+ different experiments (counting variants in hyperparameters, topologies, and initializations) before declaring a winner. If you can still observe evident progress after these experiments, don't stop, continue to push and explore.

Creative Search:

• Beyond Depth & Optimizer: Do NOT limit your search to simply increasing layers or swapping optimizers. Rethink the problem's fundamental structure.
• Internal Brilliance: Be creative! Invent new gate patterns, explore non-native lattice connectivities, or use ancilla-assisted measurement schemes, try su(4) two qubit gates which are the most expressive.
• Literature Review: Use search tools to find promising ideas in recent quantum computing literature and port them to TensorCircuit.
• Reference Examples: Examine existing scripts in the repository for implementation patterns and best practices.