Block encoding allows quantum computers to simulate non-unitary matrices by embedding them into larger unitary operations [1]. This approach plays a critical role in many quantum algorithms that require matrix-vector multiplication such as quantum linear solvers, quantum machine learning models, and quantum reservoir computing. However, recovering the desired output state using block encoding is inherently probabilistic. The correct result is obtained only when the ancilla qubits collapse to the zero state during measurement, and the probability of this happening can be very low. As a result, repeated trials are often needed, which increases resource usage and reduces efficiency. In this work, we present a Grover-style amplitude amplification technique that significantly boosts the success probability without altering the final output state. By applying a series of unitary reflections, the method amplifies the amplitude of the successful outcome, reducing the number of repetitions required to obtain the correct result. This results in a quadratic improvement in efficiency compared to repeated sampling. Our results show that the success probability improved significantly from as low as 15 % to over 85 % after applying the amplification method. This improvement could help quantum programs run more efficiently in noisy environments and makes it easier to use block-encoded matrices in real applications.
Block encoding allows quantum computers to simulate non-unitary matrices by embedding them into larger unitary operations [1]. This approach plays a critical role in many quantum algorithms that require matrix-vector multiplication such as quantum linear solvers, quantum machine learning models, and quantum reservoir computing. However, recovering the desired output state using block encoding is inherently probabilistic. The correct result is obtained only when the ancilla qubits collapse to the zero state during measurement, and the probability of this happening can be very low. As a result, repeated trials are often needed, which increases resource usage and reduces efficiency. In this work, we present a Grover-style amplitude amplification technique that significantly boosts the success probability without altering the final output state. By applying a series of unitary reflections, the method amplifies the amplitude of the successful outcome, reducing the number of repetitions required to obtain the correct result. This results in a quadratic improvement in efficiency compared to repeated sampling. Our results show that the success probability improved significantly from as low as 15 % to over 85 % after applying the amplification method. This improvement could help quantum programs run more efficiently in noisy environments and makes it easier to use block-encoded matrices in real applications.
In quantum computing, every operation must be unitary to ensure reversibility and preserve probability amplitudes, which is a fundamental requirement for any quantum process. However, many real-world computations in areas such as machine learning, signal processing, and dynamical modeling inherently involve non-unitary matrices. Since these cannot be directly implemented on quantum hardware, a practical workaround is required. One promising solution is block-encoding, a framework that embeds a non-unitary matrix A in C^{n x n} into a larger unitary matrix U_A in C^{2n x 2n}, enabling the simulation of non-unitary actions within quantum circuits through ancilla preparation and post-selection. This thesis presents a novel and efficient method for block-encoding based on Gram-Schmidt orthogonalization. The algorithm constructs a unitary U_A with A embedded as the top-left block and remaining components filled to ensure global unitarity. Compared to traditional Singular Value Decomposition (SVD)-based approaches, the proposed method achieves at least 20% improvement in execution time, without compromising output fidelity or numerical stability. A corresponding quantum circuit is developed to extract A|psi> (that is, the transformed version of the input quantum state |psi> after applying the linear operator A) using post-selection on ancilla qubits, with the success probability determined by
Block encoding of non-unitary matrices in a quantum framework is a transformative technique for broadening the capabilities of quantum algorithms, allowing them to address a wider range of computational challenges. This paper introduces a novel embedding method to construct unitary matrices from non-unitary matrices, enabling their efficient encoding into quantum circuits. Utilizing the Gram-Schmidt process, the proposed approach embeds ann×nnon-unitary matrixAinto a2n×2nunitary matrixUA. When Compared to conventional methods such as Singular Value Decomposition (SVD), our approach achieves a 20 % faster execution time while maintaining high accuracy. Block encoding method has the potential to significantly advance quantum computing applicability by building unitary matrices from non-unitary ones in areas like optimization, quantum machine learning, and large-scale quantum simulations.
Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polynomial functions on n qubits. This case holds special relevance, as any continuous function on a closed interval can be uniformly approximated with arbitrary precision by a polynomial function. The first approach relies on the matrix product state representation (MPS). We study and benchmark the approximations of the target state when the bond dimension is assumed to be small. The second algorithm combines two subroutines. Initially we encode the linear function into the quantum registers either via its MPS or with a shallow sequence of multi-controlled gates that loads the linear function's Hadamard-Walsh series, and we explore how truncating the Hadamard-Walsh series of the linear function affects the final fidelity. Applying the inverse discrete Hadamard-Walsh transform converts the state encoding the series coefficients into an amplitude encoding of the linear function. Thus, we use this construction as a building block to achieve an exact block encoding of the amplitudes corresponding to the linear function on k0 qubits and apply the quantum singular value transformation that implements a polynomial transformation to the block encoding of the amplitudes. This unitary together with the Amplitude Amplification algorithm will enable us to prepare the quantum state that encodes the polynomial function on k0 qubits. Finally we pad n−k0 qubits to generate an approximated encoding of the polynomial on n qubits, analyzing the error depending on k0 . In this regard, our methodology proposes a method to improve the state-of-the-art complexity by introducing controllable errors.
With the rapid development of quantum machine learning, quantum neural networks (QNNs) have become a research hotspot. However, the quantum gates used to implement feature mapping in this model are all linear transformations, which directly affects the mapping ability of the model. Therefore, how to enhance the mapping capability of QNN is an important issue that has not yet been effectively addressed. This paper proposes a repetitive amplitude encoding method that encodes the probability amplitudes of multiple qubit blocks by repeatedly using the same set of classical data, effectively improving the mapping capability of QNN. Taking the MNIST dataset as an example, the experimental results comparing the repetitive amplitude encoding method with several existing encoding methods show that, firstly, when the number of classes is fixed, the repetitive amplitude encoding is superior to other methods. Secondly, when the number of hidden layers in QNN is fixed, as the number of classes increases, the performance of repetitive amplitude encoding not only consistently outperforms other methods, but this advantage becomes increasingly apparent. Finally, the repetitive amplitude encoding-based QNN was applied to reservoir lithology identification in the field of oil and gas exploration, IRIS and WINe classification datasets. By comparing with classical neural networks, the proposed method was validated for its adaptability to different classification problems and superior classification performance compared to classical neural networks.
The Grover algorithm is a fundamental quantum algorithm that achieves a quadratic speedup for unstructured search problems, requiring O(√N) queries instead of O(N) classically. It works by repeatedly applying an oracle and a diffusion operator to amplify the probability of marked states. This advantage makes it relevant to cryptography, optimization, constraint satisfaction, and as a general primitive via amplitude amplification in areas like quantum machine learning and simulation. However, practical implementations are severely constrained by current Noisy Intermediate Scale Qualtum machines with limited coherence, deep oracle circuits and lack of scalable QRAM, restricting demonstrations to small-scale experiments with reproducibility challenges.
Implementing polynomial functions of Hermitian matrices on quantum hardware is a foundational task in quantum computing, critical for accurate Hamiltonian simulation, quantum linear system solving, high-fidelity state preparation, machine learning kernels, and other advanced quantum algorithms. Existing state-of-the-art techniques, including Qubitization, Quantum Singular Value Transformation (QSVT), and Quantum Signal Processing (QSP), rely heavily on block-encoding the Hermitian matrix. These methods are often constrained by the complexity of preparing the block-encoded state, the overhead associated with the required ancillary qubits, or the challenging problem of angle synthesis for the polynomial's phase factors, which limits the achievable circuit depth and overall efficiency. In this work, we propose a novel and resource-efficient approach to implement arbitrary polynomials of a Hermitian matrix by leveraging the Generalized Quantum Signal Processing (GQSP) framework. Our method circumvents the need for block-encoding and avoids the compounding post-selection overheads characteristic of LCU-based constructions, achieving a stable, degree-independent success probability. We derive closed-form expressions for symmetric polynomial expansions and demonstrate how linear combinations of GQSP circuits can realize the desired transformation. This approach reduces resource overhead and opens new pathways for quantum algorithm design for functions of Hermitian matrices, particularly in settings where the Hermitian operator arises naturally from symmetric combinations of unitaries.