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Cambridge University Press eBooks

State Similarity Tests

Robert Hundt

Published Nov 20, 2025• 0 citations
Publisher

Abstract

This chapter discusses the terms overlap and similarity between quantum states and introduces the important swap test, as well as the Hadamard test and the inversion test. The mathematical derivations in this chapter are still very detailed.
DOI
10.1017/9781009548519.008

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Armed with the knowledge and infrastructure from the previous chapters, the first set of quantum algorithms is introduced. The algorithms in this chapter are typically shorter and require less preparation than those in later chapters. Additionally, the mathematical derivations are developed with great detail. The chapter starts with the simplest possible algorithm: a quantum random number generator. This is followed by several gate equivalences, a classical full adder implemented with quantum gates, and the Swap Test to measure similarity between states. Two algorithms that utilize entanglement come next: quantum teleportation and superdense coding. After this, three so-called oracle algorithms are discussed,the Bernstein–Vazirani algorithm, Deutsch’s algorithm, and Deutsch–Jozsa’s algorithm. These are the first quantum algorithms that perform better than their classical counterparts. Oracle construction itself is discussed at great length.
Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap between two quantum states ρ and σ . The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ = σ , quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error—compared to the Swap Test—on these computers.
The Hadamard test is a standard quantum primitive for estimating inner products and expectation values, but in data-processing settings its practical utility is often limited by the cost of preparing amplitude-encoded quantum states. In this study, we investigate an angle-encoding variant of the Hadamard test for estimating cosine similarity between normalized real-valued vectors. The proposed method decomposes the similarity computation into elementwise two-qubit Hadamard-test circuits that can, in principle, be executed in parallel, resulting in constant circuit depth with respect to the vector dimension at the expense of a larger qubit footprint and classical post-processing. Because the resulting estimator is approximate, we analyze the induced bias and show that it is non-negative under the approximation used in our derivation. Numerical experiments on random normalized vectors show that, in the tested setting, the estimation error decreases as the vector dimension increases. We further illustrate a possible application to cosine-attention-based Transformer models. These results suggest that the angle-encoding Hadamard test may provide a useful design point for near-term similarity estimation when shallow circuit depth is preferred over compact qubit usage.