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Quantum linear system algorithm with optimal queries to initial state preparation

Guang Hao Low, Yuan Su

TL;DR

A new Variable Time Amplitude Amplification algorithm with Tunable thresholds (Tunable VTAA), which fully characterizes generic nested amplitude amplifications, improves the $\ell_1$-norm input cost scaling of Ambainis to an $\ell_{\frac{2}{3}}$-quasinorm scaling, and admits a deterministic amplification schedule for the quantum linear system problem.

Abstract

Quantum algorithms for linear systems produce the solution state $A^{-1}|b\rangle$ by querying two oracles: $O_A$ that block encodes the coefficient matrix and $O_b$ that prepares the initial state. We present a quantum linear system algorithm making $\mathbfΘ\left(1/\sqrt{p}\right)$ queries to $O_b$, which is optimal in the success probability, and $\mathbf{O}\left(κ\log\left(1/p\right)\left(\log\log\left(1/p\right)+\log\left({1}/ε\right)\right)\right)$ queries to $O_A$, nearly optimal in all parameters including the condition number and accuracy. Notably, our complexity scaling of initial state preparation holds even when $p$ is not known $\textit{a priori}$. This contrasts with recent results achieving $\mathbf{O}\left(κ\log\left({1}/ε\right)\right)$ complexity to both oracles, which, while optimal in $O_A$, is highly suboptimal in $O_b$ as $κ$ can be arbitrarily larger than $1/\sqrt{p}$. In various applications such as solving differential equations, preparing ground states of operators with real spectra, and estimating and transforming eigenvalues of non-normal matrices, we can further improve the dependence on $p$ using a block preconditioning scheme to nearly match or outperform best previous results based on other methods, which also furnishes an extremely simple quantum linear system algorithm with an optimal query complexity to $O_A$. Underlying our results is a new Variable Time Amplitude Amplification algorithm with Tunable thresholds (Tunable VTAA), which fully characterizes generic nested amplitude amplifications, improves the $\ell_1$-norm input cost scaling of Ambainis to an $\ell_{\frac{2}{3}}$-quasinorm scaling, and admits a deterministic amplification schedule for the quantum linear system problem.

Quantum linear system algorithm with optimal queries to initial state preparation

TL;DR

A new Variable Time Amplitude Amplification algorithm with Tunable thresholds (Tunable VTAA), which fully characterizes generic nested amplitude amplifications, improves the -norm input cost scaling of Ambainis to an -quasinorm scaling, and admits a deterministic amplification schedule for the quantum linear system problem.

Abstract

Quantum algorithms for linear systems produce the solution state by querying two oracles: that block encodes the coefficient matrix and that prepares the initial state. We present a quantum linear system algorithm making queries to , which is optimal in the success probability, and queries to , nearly optimal in all parameters including the condition number and accuracy. Notably, our complexity scaling of initial state preparation holds even when is not known . This contrasts with recent results achieving complexity to both oracles, which, while optimal in , is highly suboptimal in as can be arbitrarily larger than . In various applications such as solving differential equations, preparing ground states of operators with real spectra, and estimating and transforming eigenvalues of non-normal matrices, we can further improve the dependence on using a block preconditioning scheme to nearly match or outperform best previous results based on other methods, which also furnishes an extremely simple quantum linear system algorithm with an optimal query complexity to . Underlying our results is a new Variable Time Amplitude Amplification algorithm with Tunable thresholds (Tunable VTAA), which fully characterizes generic nested amplitude amplifications, improves the -norm input cost scaling of Ambainis to an -quasinorm scaling, and admits a deterministic amplification schedule for the quantum linear system problem.

Paper Structure

This paper contains 52 sections, 32 theorems, 375 equations, 3 figures, 4 tables.

Key Result

Proposition 4

The following correspondence holds between Tunable VTAA and variable time nested amplitude amplification.

Figures (3)

  • Figure 1: A diagrammatic illustration of the main result and its applications.
  • Figure 2: Illustration of the transition of amplitudes in a VTAA/nested amplitude amplification. The flow of algorithms is represented by arrows, with the input branch colored in red and amplified one in blue. Assuming there is no over amplification, for any pair of connected nodes, the amplitude from the bottom node is less than or equal to that from the top node.
  • Figure 3: Illustration of the qualitative behavior of functions used in branch marking (left panel) and GPE (right panel).

Theorems & Definitions (48)

  • Definition 1: Variable time algorithm and amplification
  • Definition 2: Variable time nested amplitude amplification
  • Definition 3: Tunable variable time amplitude amplification
  • Proposition 4: Universality of Tunable VTAA
  • Lemma 5: Representation of query product
  • Proposition 6: Query complexity of Tunable VTAA
  • Lemma 7: Amplitude estimation
  • Theorem 1: Solution norm estimation with optimal initial state preparation
  • Lemma 8: Block encoding inversion Gilyen2018singular
  • Theorem 2: Quantum linear system algorithm with optimal initial state preparation
  • ...and 38 more