Quantum singular value transformation and beyond: Exponential improvements for quantum matrix arithmetics
Presented at the ACM Symposium on Theory of Computing (June 2019), Phoenix, Arizona, USA
An n-qubit quantum circuit performs a unitary operation on an exponentially large, 2n-dimensional, Hilbert space, which is a major source of quantum speed-ups. We develop a new “Quantum singular value transformation” algorithm that can directly harness the advantages of exponential dimensionality by applying polynomial transformations to the singular values of a block of a unitary operator. The transformations are realized by quantum circuits with a very simple structure – typically using only a constant number of ancilla qubits – leading to optimal algorithms with appealing constant factors. We show that our framework allows describing many quantum algorithms on a high level, and enables remarkably concise proofs for many prominent quantum algorithms, ranging from optimal Hamiltonian simulation to various quantum machine learning applications. We also devise a new singular vector transformation algorithm, describe how to exponentially improve the complexity of implementing fractional queries to unitaries with a gapped spectrum, and show how to efficiently implement principal component regression. Finally, we also prove a quantum lower bound on spectral transformations.
|Keywords||Block-encdoing, Quantum signal processing, Qubitization|
|Conference||ACM Symposium on Theory of Computing|
Gilyén, A.P, Su, Y, Low, G.H, & Wiebe, N. (2019). Quantum singular value transformation and beyond: Exponential improvements for quantum matrix arithmetics. In Proceedings of the Annual ACM SIGACT Symposium on Theory of Computing (pp. 193–204).