Non-Linear Transformations of Quantum Amplitudes: Exponential Improvement, Generalization, and Applications

• URL: http://arxiv.org/abs/2309.09839v1
• Date: Mon, 18 Sep 2023 14:57:21 GMT
• Title: Non-Linear Transformations of Quantum Amplitudes: Exponential Improvement, Generalization, and Applications
• Authors: Arthur G. Rattew, Patrick Rebentrost
• Abstract summary: Quantum algorithms manipulate the amplitudes of quantum states to find solutions to computational problems. We present a framework for applying a general class of non-linear functions to the amplitudes of quantum states. Our work provides an important and efficient building block with potentially numerous applications in areas such as optimization, state preparation, quantum chemistry, and machine learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum algorithms manipulate the amplitudes of quantum states to find solutions to computational problems. In this work, we present a framework for applying a general class of non-linear functions to the amplitudes of quantum states, with up-to an exponential improvement over the previous work. Our framework accepts a state preparation unitary (or block-encoding), specified as a quantum circuit, defining an $N$-dimensional quantum state. We then construct a diagonal block-encoding of the amplitudes of the quantum state, building on and simplifying previous work. Techniques from the QSVT literature are then used to process this block-encoding. The source of our exponential speedup comes from the quantum analog of importance sampling. We then derive new error-bounds relevant for end-to-end applications, giving the error in terms of $\ell_2$-norm error. We demonstrate the power of this framework with four key applications. First, our algorithm can apply the important function $\tanh(x)$ to the amplitudes of an arbitrary quantum state with at most an $\ell_2$-norm error of $\epsilon$, with worst-case query complexity of $O(\log(N/\epsilon))$, in comparison to the $O(\sqrt{N}\log(N/\epsilon))$ of prior work. Second, we present an algorithm solving a new formulation of maximum finding in the unitary input model. Third, we prove efficient end-to-end complexities in applying a number of common non-linear functions to arbitrary quantum states. Finally, we generalize and unify existing quantum arithmetic-free state-preparation techniques. Our work provides an important and efficient algorithmic building block with potentially numerous applications in areas such as optimization, state preparation, quantum chemistry, and machine learning.

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