Quantum singular value thresholding (QSVT) algorithm, as a core module of many mathematical models, seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors. The existing all-qubit QSVT algorithm demands lots of ancillary qubits, remaining a huge challenge for realization on near-term intermediate-scale quantum computers. In this paper, we propose a hybrid QSVT (HQSVT) algorithm utilizing both discrete variables (DVs) and continuous variables (CVs). In our algorithm, raw data vectors are encoded into a qubit system and the following data processing is fulfilled by hybrid quantum operations. Our algorithm requires O[log(MN)] qubits with O(1) qumodes and totally performs O(1) operations, which significantly reduces the space and runtime consumption.
Quantum singular value thresholding (QSVT) algorithm, as a core module of many mathematical models, seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors. The existing all-qubit QSVT algorithm demands lots of ancillary qubits, remaining a huge challenge for realization on near-term intermediate-scale quantum computers. In this paper, we propose a hybrid QSVT (HQSVT) algorithm utilizing both discrete variables (DVs) and continuous variables (CVs). In our algorithm, raw data vectors are encoded into a qubit system and the following data processing is fulfilled by hybrid quantum operations. Our algorithm requires O[log(MN)] qubits with O(1) qumodes and totally performs O(1) operations, which significantly reduces the space and runtime consumption.
A singular-value thresholding (SVT) operation is a fundamental core module of many mathematical models in computer vision and machine learning, particularly for many nuclear norm minimizing-based problems. A quantum SVT (QSVT) algorithm was proposed in Phys. Rev. A. 96, 032301 (2017) to solve an image-classification problem. This algorithm runs in O[log2(pq)], an exponential speed improvement over the classical algorithm, which runs in O[poly(pq)]. In this paper, we design a scalable quantum circuit for a QSVT. The quantum circuit is designed with $O\left[{log}_{2}\left(pq/\ensuremath{\varepsilon}\right)\right]$ qubits and $O\left[\text{poly}{log}_{2}\left(1/\ensuremath{\varepsilon}\right)\right]$ quantum gates in terms of error $O\left(\ensuremath{\varepsilon}\right)$. We also show that a high-probability and high-fidelity output can be obtained in one iteration of the quantum circuit. The quantum circuit for a QSVT implies a tempting possibility for experimental realization on a quantum computer. Finally, we propose a small-scale quantum circuit for a QSVT. We numerically simulate and demonstrate the performance of this circuit, verifying its capability to solve the intended SVT.
In order to exploit quantum advantages, quantum algorithms are indispensable for operating machine learning with quantum computers. We here propose an intriguing hybrid approach of quantum information processing for quantum linear regression, which utilizes both discrete and continuous quantum variables, in contrast to existing wisdoms based solely upon discrete qubits. In our framework, data information is encoded in a qubit system, while information processing is tackled using auxiliary continuous qumodes via qubit-qumode interactions. Moreover, it is also elaborated that finite squeezing is quite helpful for efficiently running the quantum algorithms in a realistic setup. Comparing with an all-qubit approach, the present hybrid approach is more efficient and feasible for implementing quantum algorithms, still retaining exponential quantum speed-up.
We address the problem of solving a system of linear equations via the Quantum Singular Value Transformation (QSVT). One drawback of the QSVT algorithm is that it requires huge quantum resources if we want to achieve an acceptable accuracy. To reduce the quantum cost, we propose a hybrid quantum-classical algorithm that improves the accuracy and reduces the cost of the QSVT by adding iterative refinement in mixed-precision A first quantum solution is computed using the QSVT, in low precision, and then refined in higher precision until we get a satisfactory accuracy. For this solver, we present an error and complexity analysis, and first experiments using the quantum software stack myQLM.
We introduce shadow quantum singular value transformation (Shadow QSVT): given an initial state ∣ψ⟩, a Hermitian matrix H, a polynomial f, and a set of observables {O1,…,Om}, the goal is to estimate ⟨ψ∣f(H)†Ojf(H)∣ψ⟩ for all j∈{1,…,m}. Shadow QSVT provides a systematic route to reduce the quantum resources required by standard QSVT, which constructs a unitary block-encoding of f(H). It uses structure in the input state and observables, together with the fact that many applications require only observable estimates rather than synthesizing the full unitary.
We present three algorithms that exploit structure in the initial state and observables to reduce quantum circuit depth. First, we develop a state-aware QSVT algorithm that prepares the target state with low circuit depth when the Krylov subspace associated with H and ∣ψ⟩ is low-dimensional or admits an accurate low-dimensional approximation. Second, we introduce an observable-aware Shadow QSVT algorithm that combines a new observable-aware Krylov subspace with history states to further reduce circuit depth and gate complexity. Finally, we develop Classical Shadow QSVT, which constructs a classical representation from H, f, and ∣ψ⟩ without prior knowledge of the observables or explicit preparation of the target state proportional to f(H)∣ψ⟩. This representation enables estimation of the target quantities for observables specified after the quantum computation. Together, these three algorithms provide tools for reducing the circuit depth of QSVT-based computations across a range of settings.
Low-rank matrix regression is a pivotal technique in image processing and pattern recognition that can directly handle data with intrinsic matrix structures. However, classical algorithms for this task suffer from a computational bottleneck, where the complexity escalates cubically with the matrix dimension, incurring prohibitive costs for high-dimensional datasets. In this study, we propose a quantum algorithm for low-rank matrix regression with feature extraction, leveraging the Harrow–Hassidim–Lloyd algorithm for regression matrix estimation and a modified quantum singular-value thresholding algorithm for iterative variable updates. Theoretical analysis demonstrates that our algorithm reduces the time complexity dependence on the matrix dimension from cubic to logarithmic, achieving an exponential speedup over classical counterparts when the problem is well conditioned and the precision requirements are moderate. Furthermore, we introduce an algorithmic variant utilizing the quantum memory model to bypass the reliance on quantum random access memory. Additionally, we propose a hybrid strategy leveraging quantum sketching techniques to effectively eliminate the runtime dependence on the condition number. Subsequently, we performed small-scale quantum experiments to validate the feasibility of our proposed quantum algorithm.
We develop new algorithms for Quantum Singular Value Transformation (QSVT), a unifying framework that encapsulates most known quantum algorithms and serves as the foundation for new ones. Existing implementations of QSVT rely on block encoding, incurring an intrinsic O(logL) ancilla overhead and circuit depth O(Ldλ) for polynomial transformations of a Hamiltonian H=∑k=1LHk, where d is the polynomial degree and λ=∑k∥Hk∥. We introduce a simple yet powerful approach that utilizes only basic Hamiltonian simulation techniques, namely, Trotter methods, to: (i) eliminate the need for block encoding, (ii) reduce the ancilla overhead to only a single qubit, and (iii) still maintain near-optimal complexity. Our method achieves a circuit depth of O(L(dλcomm)1+o(1)), without requiring any complicated multi-qubit controlled gates. Moreover, λcomm depends on the nested commutators of the terms of H and can be substantially smaller than λ for many physically relevant Hamiltonians, a feature absent in standard QSVT. To achieve these results, we make use of Richardson extrapolation in a novel way, systematically eliminating errors in any interleaved sequence of arbitrary unitaries and Hamiltonian evolution operators, thereby establishing a general framework that encompasses QSVT but is more broadly applicable. As applications, we develop end-to-end quantum algorithms for solving linear systems and estimating ground state properties of Hamiltonians, both achieving near-optimal complexity without relying on oracular access. Overall, our results establish a new framework for quantum algorithms, significantly reducing hardware overhead while maintaining near-optimal performance, with implications for both near-term and fault-tolerant quantum computing.