Quantum state tomography (QST) is a method used to reconstruct the complete quantum state of a system by performing measurements on many copies of that state. In quantum mechanics, a quantum state is described by a mathematical object called a density matrix, which encodes all measurable properties of the system. QST works by repeatedly preparing the same quantum state, measuring it in different bases (e.g., X, Y, or Z axes for qubits), and statistically analyzing the outcomes to estimate the density matrix. For example, to reconstruct a single qubit’s state, you’d measure it in the Pauli-X, Pauli-Y, and Pauli-Z bases, collect outcome probabilities, and solve linear equations to build the matrix. This process scales exponentially with the number of qubits—a two-qubit system requires 15 independent measurements (3^2 - 1), making QST resource-intensive for larger systems.
QST is used to validate quantum algorithms by verifying that the actual output state of a quantum circuit matches the theoretically expected state. For instance, suppose you design a quantum algorithm to generate a Bell state (a maximally entangled two-qubit state). Running the algorithm on hardware will produce a noisy version of the ideal state due to imperfections. By applying QST, you can reconstruct the experimental density matrix and compare it to the ideal Bell state’s matrix using metrics like fidelity (a measure of similarity). If the fidelity is high, the algorithm likely works as intended. This is critical for debugging: discrepancies might reveal errors in gate operations, qubit decoherence, or calibration issues. For example, IBM’s Quantum experiments often use QST to benchmark small-scale algorithms like Grover’s search or quantum teleportation before scaling them up.
However, QST has practical limitations. The exponential scaling of measurements makes it infeasible for systems with more than a few qubits. To address this, developers often combine QST with error mitigation techniques or use compressed sensing to reduce measurement overhead. For example, in a variational quantum algorithm, QST might validate only specific subsystems rather than the full state. Additionally, QST results are sensitive to measurement errors, so calibration and statistical post-processing (like maximum likelihood estimation) are essential. While not a daily tool for large-scale systems, QST remains a cornerstone for testing foundational components of quantum algorithms, ensuring that individual gates, small circuits, or entanglement-generating operations behave as expected before integrating them into more complex workflows.