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arXiv:2610.23429

Cayley access models for matrix functions: reachable class and cost

Gianna M. Del Corso

quant-phmath.NASubmitted Oct 16, 2026• Announced Oct 19, 2026• 0 citations
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Abstract

We study how to compute $f(A)b$ on a quantum computer, where $A$ is Hermitian. When $f$ is nonsmooth or has localized difficulty in the spectrum, classical numerical linear algebra approximates it with rational functions rather than polynomials. We propose the Cayley transform, parameterized by $σ\in \mathbb{R}$ and $γ> 0$, as an alternative to the standard quantum access model. The two parameters let one concentrate accuracy where $f$ is difficult, which standard models can achieve only by raising the polynomial degree globally. This transformation converts Hermitian matrices to unitaries, allowing generalized quantum signal processing to apply Laurent polynomials bounded on the unit circle. We compare three access models (block encoding, matrix exponential, and Cayley encoding) in terms of query complexity and of the cost of their circuit realization. When the difficulty of $f$ is localized in the spectrum, the Cayley encoding reduces the required degree by a factor of three to four, and it represents the shifted resolvent exactly at degree one. For spectral projectors, the standard models cannot reach the target accuracy at fixed degree, because their approximants cannot concentrate resolution near the discontinuity. Realizing the Cayley encoding from a block encoding requires solving a shifted linear system, whose cost offsets its query-complexity gain over the standard models. For structured matrices arising in practice, however, low-depth circuits for the encoding can be constructed directly, and the gain is preserved at the circuit level.

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We study how to compute f(A)bf(A)bf(A)b on a quantum computer, where AAA is Hermitian. When fff is nonsmooth or has localized difficulty in the spectrum, classical numerical linear algebra approximates it with rational functions rather than polynomials.
We propose the Cayley transform, parameterized by σ∈Rσ\in \mathbb{R}σ∈R and γ>0γ> 0γ>0, as an alternative to the standard quantum access model. The two parameters let one concentrate accuracy where fff is difficult, which standard models can achieve only by raising the polynomial degree globally. This transformation converts Hermitian matrices to unitaries, allowing generalized quantum signal processing to apply Laurent polynomials bounded on the unit circle.
We compare three access models (block encoding, matrix exponential, and Cayley encoding) in terms of query complexity and of the cost of their circuit realization. When the difficulty of fff is localized in the spectrum, the Cayley encoding reduces the required degree by a factor of three to four, and it represents the shifted resolvent exactly at degree one. For spectral projectors, the standard models cannot reach the target accuracy at fixed degree, because their approximants cannot concentrate resolution near the discontinuity.
Realizing the Cayley encoding from a block encoding requires solving a shifted linear system, whose cost offsets its query-complexity gain over the standard models. For structured matrices arising in practice, however, low-depth circuits for the encoding can be constructed directly, and the gain is preserved at the circuit level.
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.
Quantum algorithms for basic numerical linear algebraic tasks have proven essential for translating diverse problems to a unified quantum computational context. Many of these tasks---e.g., applying a function to the spectrum of a matrix embedded in a unitary process (a so-called block encoding), or taking linear combinations of block encodings---are well-addressed by techniques like quantum singular value transformation or linear combination of unitaries. However, there exist useful matrix transforms whose realization is unclear or inefficient. In this work we construct improved quantum algorithms for some of these transforms, the simplest of which is a polynomial function applied element-wise. We show the space required to compute quantum element-wise transforms (QEWTs) can be reduced exponentially in the degree of the applied function compared to prior work, and rectify errors in previous constructions. We prove novel space and query lower bounds for QEWTs, construct useful function families nearly saturating these bounds, and establish the hardness of QEWT dequantization. This represents the first sound theoretical treatment of QEWTs, and brings their resource requirements on par with other practical block encoding techniques. We present our algorithms alongside concrete applications to machine learning inference and signal processing.
Polynomial transformations are basic primitives in quantum algorithms: quantum signal processing and singular value transformation compile univariate polynomials into circuits with query complexity essentially set by degree. Multivariable transformations of noncommuting matrices, however, lack a comparable synthesis theory. We develop a complete constructive theory under joint block access for all polynomials contractive on the prescribed matrix domain. Given a compact finite-state description of a degree-DDD polynomial PPP and 0<τ<10<τ<10<τ<1, we synthesize PPP with (1+τ)(1+τ)(1+τ)-optimal normalization using O(D/τ)O(D/\sqrtτ)O(D/τ​) queries, and exactly DDD under row-block access, matching a degree lower bound. The complete circuit realization is classically computable in polynomial time with polylogarithmic dependence on accuracy. The key to our construction is a finite algorithmic Schur--Agler theorem: the coefficient recurrence defines a polynomial-dimensional continuation space supporting a complete semidefinite certificate for contractivity on the prescribed domain. Factoring this certificate yields the contractive realization underlying the query algorithm. Beyond matrix transformations, our framework lifts multivariable polynomial synthesis to quantum channel transformations. Given coherent Kraus access K={Ka}aK=\{K_a\}_aK={Ka​}a​, we synthesize any finite jointly contractive family of noncommutative polynomial maps K↦{Fb(K)}bK\mapsto\{F_b(K)\}_bK↦{Fb​(K)}b​ as completely positive operations, allowing coherent interference among Kraus histories. Furthermore, channel-level transformations specified by causal Choi data can be synthesized explicitly as fixed-order quantum combs. Collectively, these results point to a broader program: multivariable approximation as a language for multi-operator quantum algorithms and higher-order quantum information processing.
Modern quantum algorithms are increasingly formulated as coherent procedures for implementing polynomial transformations of operators and singular values. This perspective provides a powerful and unifying language for quantum algorithm design, connecting a wide range of distinct problems through five closely related key tools: block-encoding, qubitization, QSP, QSVT and GQSP. Block-encoding embeds non-unitary matrices into larger unitaries; qubitization converts block-encodings into structured operators; QSP, QSVT and GQSP enable polynomial transformations with near-optimal query complexity. Together, these techniques form a general toolkit for transforming matrix functions into implementable quantum circuits. This paper develops these techniques from first principles as a unified framework for constructing quantum algorithms. We apply this framework to representative applications to highlight design principles and demonstrate how distinct algorithms can be constructed from a unified sequence of operator transformations. A central contribution is a systematic decision workflow for selecting the appropriate approach according to the operator structure and the desired transformation polynomial. This perspective clarifies when direct GQSP or through qubitization, or Laurent expansion, or QSVT is most appropriate. We organize algorithmic design into an end-to-end pipeline: identifying the target matrix function, constructing an appropriate block-encoding, determining the relevant spectral domain, designing a polynomial or Laurent-polynomial approximation, synthesizing the phase factors, and translating the transformation into an executable quantum circuit. By applying this unified framework to example applications, we showcase a practical methodology for reasoning, designing, and implementing quantum algorithms based on polynomial transformations.
Many problems in linear algebra-such as those arising from non-Hermitian physics, transcorrelated quantum chemistry and differential equations-can be solved on a quan-tum computer by processing eigenvalues of the non-normal input matrices. However, the existing Quantum Singular Value Transformation (QSVT) framework is ill-suited to this task, as eigenvalues and singular values are different in general. We present a Quantum EigenValue Transformation (QEVT) framework for applying arbitrary polynomial transformations on eigenvalues of block-encoded non-normal operators, and a related Quantum EigenValue Estimation (QEVE) algorithm for operators with real spectra. QEVT has query complexity to the block encoding nearly recovering that of the QSVT for a Hermitian input, and QEVE achieves the Heisenberg-limited scaling for diagonalizable input matrices. As applications, we develop a linear differential equation solver with strictly linear time query complexity for average-case diagonalizable operators, as well as a ground state preparation algorithm that upgrades previous nearly optimal results for Hermitian Hamiltonians to diagonalizable matrices with real spectra. Underpinning our algorithms is an efficient method to prepare a quantum super-position of Faber polynomials, which generalize the nearly-best uniform approximation properties of Chebyshev polynomials to the complex plane. Of independent interest, we also develop techniques to generatennnFourier coefficients with 0 (poly log (nnn)) gates compared to prior approaches with linear cost.
Quantum Signal Processing (QSP) and Quantum Singular Value Transformation (QSVT) provide an efficient framework for implementing polynomials of block-encoded matrices, and thus offer a systematic approach to quantum algorithm design. However, despite a number of recent advances, important limitations remain. In particular, QSP can only transform unitary matrices, by applying a polynomial to their eigenvalues, while QSVT is a singular-value transformation and thus one can only obtain the polynomial of Hermitian matrices. As a consequence, these techniques do not directly apply to an arbitrary non-Hermitian matrix that is not diagonalizable. In this work, we propose a simple yet powerful method to extend these ideas to arbitrary square matrices by acting on their eigenvalues. To this end, we introduce the notion of an nnn-regular block encoding, namely, a block encoding whose kkk-th power reproduces the kkk-th power of the encoded matrix for every 0<k<n0 < k < n0<k<n. We show that applying QSP to any unitary with this property is equivalent to applying a polynomial of degree at most nnn to the block-encoded matrix, independently of its internal structure. Moreover, we provide a simple construction that transforms any block encoding into an nnn-regular one using only O(log⁡n)O(\log n)O(logn) ancillary qubits and operations. Finally, we show that this construction induces the desired transformation on the eigenvalues associated with the Jordan normal form of the matrix; for non-diagonalizable matrices this yields the full matrix function P(A)P(A)P(A), whose action on each Jordan block involves the derivatives of PPP, and not merely the map λ↦P(λ)λ\mapsto P(λ)λ↦P(λ).