On Encoding Matrices using Quantum Circuits
- URL: http://arxiv.org/abs/2510.20030v2
- Date: Sun, 09 Nov 2025 16:00:53 GMT
- Title: On Encoding Matrices using Quantum Circuits
- Authors: Liron Mor Yosef, Haim Avron,
- Abstract summary: We study encodings matrices in the form of block encodings and state preparation circuits.<n>Two key results we establish are: (a) a general method for efficiently constructing a block encoding of an arbitrary matrix given in classical form, and (b) low-overhead, bidirectional conversion algorithms between block encodings and state preparation circuits.
- Score: 5.877573384886684
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over a decade ago, it was demonstrated that quantum computing has the potential to revolutionize numerical linear algebra by enabling algorithms with complexity superior to what is classically achievable, e.g., the seminal HHL algorithm for solving linear systems. Efficient execution of such algorithms critically depends on representing inputs (matrices and vectors) as quantum circuits that encode or implement these inputs. For that task, two common circuit representations emerged in the literature: block encodings and state preparation circuits. In this paper, we systematically study encodings matrices in the form of block encodings and state preparation circuits. We examine methods for constructing these representations from matrices given in classical form, as well as quantum two-way conversions between circuit representations. Two key results we establish (among others) are: (a) a general method for efficiently constructing a block encoding of an arbitrary matrix given in classical form (entries stored in classical random access memory); and (b) low-overhead, bidirectional conversion algorithms between block encodings and state preparation circuits, showing that these models are essentially equivalent. From a technical perspective, two central components of our constructions are: (i) a special constant-depth multiplexer that simultaneously multiplexes all higher-order Pauli matrices of a given size, and (ii) an algorithm for performing a quantum conversion between a matrix's expansion in the standard basis and its expansion in the basis of higher-order Pauli matrices.
Related papers
- Block encoding of sparse matrices with a periodic diagonal structure [67.45502291821956]
We provide an explicit quantum circuit for block encoding a sparse matrix with a periodic diagonal structure.<n>Various applications for the presented methodology are discussed in the context of solving differential problems.
arXiv Detail & Related papers (2026-02-11T07:24:33Z) - Beyond Sparsity: Quantum Block Encoding for Dense Matrices via Hierarchically Low Rank Compression [7.18483139409948]
Quantum algorithms for solving large scale systems of linear equations offer potential speedups.<n>This work extends the scope of these algorithms to a broad class of structured dense matrices.<n>We develop two distinct methods to make these systems amenable to quantum solvers.
arXiv Detail & Related papers (2026-02-10T12:56:49Z) - Block Encoding Linear Combinations of Pauli Strings Using the Stabilizer Formalism [0.0]
We introduce a novel method for constructing quantum circuits that block encode linear combinations of Pauli strings.<n>We present four concrete examples and use numerical simulations to compare our method's circuit complexity with that of the Linear Combination of Unitaries (LCU) approach.
arXiv Detail & Related papers (2026-01-09T11:41:46Z) - MatRL: Provably Generalizable Iterative Algorithm Discovery via Monte-Carlo Tree Search [37.24058519921229]
MatRL is a reinforcement learning framework that automatically discovers iterative algorithms for computing matrix functions.<n>We show that MatRL produces algorithms that outperform various baselines in the literature.
arXiv Detail & Related papers (2025-07-04T22:57:33Z) - Controlled measurement, Hermitian conjugation and normalization in matrix-manipulation algorithms [46.13392585104221]
We introduce the concept of controlled measurement that solves the problem of small access probability to the desired state of ancilla.<n>Separate encoding of the real and imaginary parts of a complex matrix allows to include the Hermitian conjugation into the list of matrix manipulations.<n>We weaken the constraints on the absolute values of matrix elements unavoidably imposed by the normalization condition for a pure quantum state.
arXiv Detail & Related papers (2025-03-27T08:49:59Z) - Entanglement-Assisted Coding for Arbitrary Linear Computations Over a Quantum MAC [34.32444379837011]
We study a linear computation problem over a quantum multiple access channel (LC-QMAC)<n>We propose an achievable scheme for LC-QMAC based on the stabilizer formalism and the ideas from entanglement-assisted quantum error-correcting codes (EAQECC)
arXiv Detail & Related papers (2025-01-27T18:35:33Z) - Dictionary-based Block Encoding of Sparse Matrices with Low Subnormalization and Circuit Depth [2.4487770108795393]
We propose an efficient block-encoding protocol for sparse matrices based on a novel data structure.<n>Non-zero elements with the same values belong to the same classification in our block-encoding protocol's dictionary.<n>Our protocol connects to linear combinations of unitaries (LCU) and the sparse access input model (SAIM)
arXiv Detail & Related papers (2024-05-28T09:49:58Z) - Quantum algorithms for calculating determinant and inverse of matrix and solving linear algebraic systems [43.53835128052666]
We propose quantum algorithms, purely quantum in nature, for calculating the determinant and inverse of an $(N-1)times (N-1)$ matrix.<n>The basic idea is to encode each row of the matrix into a pure state of some quantum system.
arXiv Detail & Related papers (2024-01-29T23:23:27Z) - Vectorization of the density matrix and quantum simulation of the von
Neumann equation of time-dependent Hamiltonians [65.268245109828]
We develop a general framework to linearize the von-Neumann equation rendering it in a suitable form for quantum simulations.
We show that one of these linearizations of the von-Neumann equation corresponds to the standard case in which the state vector becomes the column stacked elements of the density matrix.
A quantum algorithm to simulate the dynamics of the density matrix is proposed.
arXiv Detail & Related papers (2023-06-14T23:08:51Z) - Block-encoding structured matrices for data input in quantum computing [0.0]
We show how to construct block encoding circuits based on an arithmetic description of the sparsity and pattern of repeated values of a matrix.
The resulting circuits reduce flag qubit number according to sparsity, and data loading cost according to repeated values.
arXiv Detail & Related papers (2023-02-21T19:08:49Z) - Automatic and effective discovery of quantum kernels [41.61572387137452]
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.<n>We present an approach to this problem, which employs optimization techniques, similar to those used in neural architecture search and AutoML.<n>The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach.
arXiv Detail & Related papers (2022-09-22T16:42:14Z) - Explicit Quantum Circuits for Block Encodings of Certain Sparse Matrices [4.2389474761558406]
We show how efficient quantum circuits can be explicitly constructed for some well-structured matrices.
We also provide implementations of these quantum circuits in sparse strategies.
arXiv Detail & Related papers (2022-03-19T03:50:16Z) - Quantum algorithms for matrix operations and linear systems of equations [65.62256987706128]
We propose quantum algorithms for matrix operations using the "Sender-Receiver" model.
These quantum protocols can be used as subroutines in other quantum schemes.
arXiv Detail & Related papers (2022-02-10T08:12:20Z) - Synthesis of Quantum Circuits with an Island Genetic Algorithm [44.99833362998488]
Given a unitary matrix that performs certain operation, obtaining the equivalent quantum circuit is a non-trivial task.
Three problems are explored: the coin for the quantum walker, the Toffoli gate and the Fredkin gate.
The algorithm proposed proved to be efficient in decomposition of quantum circuits, and as a generic approach, it is limited only by the available computational power.
arXiv Detail & Related papers (2021-06-06T13:15:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.