---

name: tensor-ops-and-execution description: "Route TFQ tensor conversion, raw execution-op getters, backend choice, parameter resolution, noisy execution, math helpers, and util-helper questions here." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tensor Ops and Execution

Use this sub-skill when the user is working directly with TFQ tensors or raw execution helpers:

• tfq.convert_to_tensor and tfq.from_tensor
• tfq.get_expectation_op, tfq.get_sampled_expectation_op, tfq.get_sampling_op, and tfq.get_state_op
• tfq.get_unitary_op / tfq.core.ops.tfq_unitary_op.get_unitary_op
• tfq.append_circuit, tfq.resolve_parameters
• tfq.padded_to_ragged and tfq.padded_to_ragged2d
• tfq.noise.expectation, tfq.noise.sampled_expectation, and tfq.noise.samples
• tfq.math.inner_product, tfq.math.fidelity, tfq.math.mps_1d_expectation, tfq.math.mps_1d_sample, and tfq.math.mps_1d_sampled_expectation
• tfq.get_quantum_concurrent_op_mode and tfq.set_quantum_concurrent_op_mode
• tfq.util.get_supported_gates, tfq.util.get_supported_channels, tfq.util.get_circuit_symbols, and tfq.util.exponential

Typical user prompts here ask how to:

• turn Cirq circuits or Pauli sums into TFQ tensors and back
• choose the right backend for expectation, sampling, state, or unitary ops
• resolve symbols or append circuits before execution
• understand tensor rank, batch-size, symbol, or operator mismatches
• decide between noiseless and noisy execution paths

Do not route here for Keras layer assembly, differentiators or optimizers, or dataset/tutorial workflows.

For exact signatures and behavior, read references/api-reference.md. For usage patterns, read references/workflows.md. For error recovery, read references/troubleshooting.md.