quantum-signal-processing

Quantum signal processing simulates Hamiltonian evolution by applying phase-optimized single-qubit circuits to approximate target functions like e^{-iτx}, allowing you to transform eigenvalues using computed phase sequences.

Quantum signal processing is a technique that implements polynomial transformations on a quantum signal by interleaving W(x) = Rx(2 arccos(x)) rotations with Rz phase gates to achieve targeted function approximations.

You optimize phase sequences by using classical L-BFGS-B optimization to approximate your target function, such as e^{-iτx}, for a specified degree and evolution time parameter τ.

Yes, the end-to-end workflow includes statevector simulation, error analysis, and SVG circuit export, enabling you to visualize the constructed gate sequences and evaluate approximation accuracy.

The approach focuses on single-qubit circuits using W(x) = Rx(2 arccos(x)) interleaved with Rz gates, which is primarily suited for educational demonstration and analyzing polynomial approximations rather than large-scale multi-qubit systems.