PyEncode: An Open-Source Library for Structured Quantum State Preparation
This provides a practical tool for quantum computing researchers and engineers working with structured data, though it is incremental as it implements existing theoretical work into a deployable framework.
The authors tackled the problem of inefficient quantum state preparation for structured vectors by developing PyEncode, an open-source library that implements theoretical closed-form circuits, achieving exponential gate reductions from O(2^m) to O(m) or O(m^2) for various patterns like sparse, step, and Fourier vectors.
Quantum algorithms require encoding classical vectors as quantum states, a step known as amplitude encoding. General-purpose state preparation routines accept any input vector of length $N = 2^m$ and produce circuits with $\bigO{2^m}$ gates. However, vectors arising in scientific and engineering applications often exhibit mathematical structure that admits far more efficient encoding. Recent theoretical work has established closed-form circuits for several structured vector classes, but without open-source implementations. We present PyEncode, an open-source Python library that implements this body of theory in a unified, immediately deployable framework. The library covers sparse, step, square (general interval), Walsh, geometric, and Fourier patterns, and supports weighted superpositions of pattern states via the linear combination of unitaries (LCU) protocol, enabling exact preparation of piecewise-structured vectors such as multi-interval Hamiltonians. PyEncode exposes a single function encode(VectorObj, N) that maps a typed parameter declaration directly to a verified Qiskit circuit, with no vector materialization and no approximation. Sparse, step, and Walsh vectors require only $\bigO{m}$ gates; geometric (exponential-decay) vectors require $\bigO{m}$ gates with zero two-qubit gates; square (general interval) vectors require $\bigO{m^2}$ gates via a QFT-based constant adder, with $\bigO{m}$ special cases; Fourier (sinusoidal) vectors require $\bigO{m^2}$ gates via the inverse Quantum Fourier Transform -- all exponentially fewer than the $\bigO{2^m}$ cost of general state preparation. LCU combines $r$ component circuits whose total gate cost is the sum of individual component costs, with success probability $p \in (0,1]$ determined analytically. The library is available at https://github.com/UW-ERSL/PyEncode.