We develop a block-encoding compiler that speeds up linear combination of unitaries Hamiltonian simulation programs by treating matrix product operators as compressed, virtual-path LCU programs. In showing how these new conditional PREP and SELECT stages are compiled in terms of a parent matrix product operator, we go beyond typical operator splitting product formulas and illustrate how tensor networks are a natural language and valid intermediate representation for quantum circuits. Our results are numerically verified for two important cases, namely, Heisenberg and perturbed Heisenberg-adjacent chain real-time evolution, and highlight polynomial speedups. Specifically, we highlight a polynomial speedup that avoids the $\mathcal{O}(N^K)$ Pauli-string growth when the compressed MPO bond dimension and path normalization remain mild. We quantify how MPO truncation error and bond-dimension budgets affect the compiled polynomial representation. Our algorithms show how classical pre-processing in terms of tensor network data structures opens new avenues to accelerate quantum algorithms.
We develop a block-encoding compiler that speeds up linear combination of unitaries Hamiltonian simulation programs by treating matrix product operators as compressed, virtual-path LCU programs. In showing how these new conditional PREP and SELECT stages are compiled in terms of a parent matrix product operator, we go beyond typical operator splitting product formulas and illustrate how tensor networks are a natural language and valid intermediate representation for quantum circuits. Our results are numerically verified for two important cases, namely, Heisenberg and perturbed Heisenberg-adjacent chain real-time evolution, and highlight polynomial speedups. Specifically, we highlight a polynomial speedup that avoids the O(NK) Pauli-string growth when the compressed MPO bond dimension and path normalization remain mild. We quantify how MPO truncation error and bond-dimension budgets affect the compiled polynomial representation. Our algorithms show how classical pre-processing in terms of tensor network data structures opens new avenues to accelerate quantum algorithms.
Quantum signal processing combined with quantum eigenvalue transformation has recently emerged as a unifying framework for several quantum algorithms. In its standard form, it consists of two separate routines: block encoding, which encodes a Hamiltonian in a larger unitary, and signal processing, which achieves an almost arbitrary polynomial transformation of such a Hamiltonian using rotation gates. The bottleneck of the entire operation is typically constituted by block encoding, and in recent years, several problem-specific techniques have been introduced to overcome this problem. Within this framework, we present a procedure to block-encode a Hamiltonian based on its matrix product operator (MPO) representation. More specifically, we encode every MPO tensor in a larger unitary of dimension D+2, where $D=\ensuremath{\lceil}{log}_{2}(\ensuremath{\chi})\ensuremath{\rceil}$ is the number of subsequently contracted qubits that scales logarithmically with the virtual bond dimension $\ensuremath{\chi}$. Given any system of size L, our method requires L+D ancillary qubits in total, while the number of one- and two-qubit gates decomposing the block-encoding circuit scales as $O(L{\ensuremath{\chi}}^{2})$. Moreover, block encodings generally require postselection. Our model exhibits a success probability decaying exponentially with L. Nevertheless, we present a way to mitigate this limitation for finite systems.
Quantum circuits naturally implement unitary operations on input quantum states. However, non-unitary operations can also be implemented through "block encodings", where additional ancilla qubits are introduced and later measured. While block encoding underlies a host of well-established theoretical applications, its implementation in practice has been prohibitively expensive for current quantum hardware. In this paper, we present practical and explicit block encoding circuits implementing matrix polynomial transformations of a target matrix. With standard approaches, block-encoding a degree-d matrix polynomial requires a circuit depth scaling as d times the depth for block-encoding the original matrix alone. By leveraging the recently introduced Fast One-Qubit Controlled Select Linear Combination of Unitaries (FOQCS-LCU) framework, we show that the additional circuit-depth overhead required for encoding matrix polynomials can be reduced to scale linearly in d with no dependence on system size or the cost of block encoding the original matrix. Moreover, we demonstrate that the FOQCS-LCU circuits and their associated matrix polynomial transformations can be controlled with negligible overhead, enabling efficient applications such as Hadamard tests. Finally, we provide explicit hardware-efficient circuits for representative non-unitary operators, namely quantum spin models, together with detailed non-asymptotic gate counts and circuit depths.
Tensor networks (TNs) give explicit classical descriptions of structured finite linear maps, while block-encodings (BEs) are the standard quantum access model for such maps. We establish TNs as a universal data interface for quantum algorithms: any explicitly specified TN for an arbitrary finite linear map compiles directly to an explicit qubit BE, with no penalty on the selected-block round trip under faithful compression. Along a chosen sweep, the compiler handles local non-unitarity by exact local dilations and aggregates the resulting post-selection conditions online using logarithmically many additional flag qubits. The chosen sweep incurs three exact, sweep-dependent costs (the accumulated scale, the frontier memory, and the number of genuinely dilated local steps), which the main theorems are stated in terms of. In the bounded-local explicit arithmetic model, this yields linear-time compilation to linear-size circuits with constant-size local gadgets, and hence a constant-factor size correspondence between bounded-local TNs and bounded-local BEs. The same construction gives a selected-block round trip BE->TN->BE: a BE canonically yields a TN for its selected block rather than for an arbitrary unitary extension, which can then be compressed or approximated classically before recompilation. Faithful Schmidt-rank compression transfers monotonically (the scale cost and frontier memory cannot increase, with operator error bounded by the discarded weight), while arbitrary restructuring admits no general scale guarantee, a limitation we show is unavoidable. As consequences and limits of this scale accounting, we characterize exact scale optimality, show that bridge-hourglass forests admit scale-optimal sweeps after exact recursive local bond compression, and prove that certifying unrestricted exact scale optimality is already hard for diagonal MPOs on a path unless P=NP.
Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and substantial quantum resources, which limits scalability on quantum hardware. In this work, we propose tensor network-based quantum data encoding (TNQE), a circuit-efficient framework built on structured unitary tensor network (TN) representations. TNQE first represents each classical input via a TN decomposition and then compiles the resulting tensor cores into an encoding circuit through two complementary core-to-circuit strategies. To make this compilation trainable while respecting the unitary nature of quantum operations, we introduce a unitary-aware constraint that parameterizes TN cores as learnable block unitaries, enabling them to be directly optimized and directly encoded as quantum operators. The proposed TNQE framework enables explicit control over circuit depth and qubit resources, allowing the construction of shallow, resource-efficient circuits. Across a range of benchmarks, TNQE achieves encoding circuits as shallow as 0.04× the depth of amplitude encoding, while naturally scaling to high-resolution images (256×256) and demonstrating practical feasibility on real quantum hardware.
Quantum circuit theory has long treated Matrix‑Product Unitaries (MPUs) as elegant but unreachable objects—beautiful on paper, impossible to extract, and as far as we know never once executed on real hardware. This work breaks that barrier. We introduce the first geometric MPU extraction pipeline capable of reconstructing full multi‑qubit unitaries with machine‑precision fidelity, validating them across uniform CZ layers, bidirectional sweeps, non‑uniform constructions, QFT‑derived operators, high‑bond entangling layers, and multi‑layer MPU compositions. Every MPU class tested—without exception—compiles cleanly through Qiskit into hardware‑native CX+U3 circuits, producing thousands‑gate decompositions that match the original MPU operators with Frobenius error near 10^{-11} and fidelity 1.0. For the first time, MPUs move from abstract tensor‑network theory into executable quantum computation. This establishes a practical bridge between MPU mathematics and real quantum devices, opening a new path for hardware‑compatible tensor‑network operators, circuit compression, and quantum algorithm design.
We introduce a coefficient-decoupled matrix product operator (MPO) representation for Pauli-sum operators that separates reusable symbolic operator support from a tunable coefficient bridge across a fixed bipartition. This representation provides a direct interface to linear-combination-of-unitaries (LCU) circuits: the symbolic left/right dictionaries define a static \textsc{Select} oracle that is compiled once, while coefficient updates modify only a dynamic \textsc{Prep} oracle. As a proof of concept, we construct compact state-adapted Pauli pools by sampling Pauli strings from a pretrained matrix product state (MPS), pruning them to a fixed symbolic pool, optimizing only their coefficients, and transferring the resulting weights directly to the LCU interface. The resulting workflow provides a reusable classical-to-quantum compilation strategy in which the symbolic operator structure is compiled once, and subsequent updates are confined to a low-dimensional coefficient object.