Configurable sublinear circuits for quantum state preparation

Abstract

The theory of quantum algorithms promises unprecedented benefits of harnessing the laws of quantum mechanics for solving certain computational problems. A persistent obstacle to using such algorithms for solving a wide range of real-world problems is the cost of loading classical data to a quantum state. Several quantum circuit-based methods have been proposed for encoding classical data as probability amplitudes of a quantum state. However, they require either quantum circuit depth or width to grow linearly with the data size, even though the other dimension of the quantum circuit grows logarithmically. In this paper, we present a configurable bidirectional procedure that addresses this problem by tailoring the resource trade-off between quantum circuit width and depth. In particular, we show a configuration that encodes an $N$-dimensional state by a quantum circuit with $O(\sqrt{N})$ width and depth and entangled information in ancillary qubits. We show a proof-of-principle on five quantum computers and compare the results.

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• An Overview of Quantum Software Engineering in Latin America
• Quantum Adjoint Convolutional Layers for Effective Data Representation
• Quantum Multiplexer Simplification for State Preparation
• Efficient explicit circuit for quantum state preparation of piecewise continuous functions
• Quadratic speed-ups in quantum kernelized binary classification
• Sublinear Classical-to-Quantum Data Encoding using $n$-Toffoli Gates
• Tucker iterative quantum state preparation
• Schmidt quantum compressor
• Quantum Framework for Simulating Linear PDEs with Robin Boundary Conditions