Quantum AI: Tensor Networks with NumPy

Intermediate Quantum AI

Created by Best · 12.04.2026 at 16:05 UTC

Tensor networks are compact representations of high-dimensional tensors that appear in quantum many-body physics and, increasingly, in machine learning. This card covers basic tensor contractions with NumPy (np.einsum, np.tensordot) and the concept of Matrix Product States (MPS).

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Tasks

Question 1

Given two 2x2 matrices A and B (elements supplied as two rows of 4 space-separated floats each), compute the trace of A @ B and print it with 4 decimal places.

Runtime output (stdout/stderr)

3 test cases will be used for grading

Run checks runtime behavior only. Final correctness is evaluated when you submit.

Question 2

In a Matrix Product State (MPS) decomposition of a quantum state, what does the "bond dimension" control?

The number of qubits in the system

The amount of entanglement the MPS can represent

The physical dimension of each qubit

The depth of the quantum circuit

Card Info

Topic: Quantum AI
Difficulty: Intermediate
Completed: 0 users

Creator

Best

BestBuddy