Block encodings are a fundamental primitive in quantum algorithms, but can often have large ancilla overhead. In this work, we introduce novel techniques for reducing this overhead in two distinct ways. In Part I, we prove the existence of a "space-time tradeoff" by deriving an algorithm that, for any block encoding, approximately uncomputes all but one of its ancilla (freeing up those ancillae for reuse in later parts of a quantum algorithm). In Part II, we evaluate the minimum number of ancillae required to perform coherent multiplication of block encodings and introduce a "space-accuracy tradeoff". Specfically, we prove that logarithmic ancillae is optimal for exact multiplication of block encodings, but show that (in certain block encoding regimes) approximate multiplication of block encodings can be achieved to high-precision with just one ancilla.
Block encodings are a fundamental primitive in quantum algorithms, but can often have large ancilla overhead. In this work, we introduce novel techniques for reducing this overhead in two distinct ways. In Part I, we prove the existence of a "space-time tradeoff" by deriving an algorithm that, for any block encoding, approximately uncomputes all but one of its ancilla (freeing up those ancillae for reuse in later parts of a quantum algorithm). In Part II, we evaluate the minimum number of ancillae required to perform coherent multiplication of block encodings and introduce a "space-accuracy tradeoff". Specfically, we prove that logarithmic ancillae is optimal for exact multiplication of block encodings, but show that (in certain block encoding regimes) approximate multiplication of block encodings can be achieved to high-precision with just one ancilla.
Block encodings are a basic interface between quantum algorithms and linear algebra. Standard LCU constructions achieve optimal circuit depth but typically require logarithmically many ancilla qubits. We ask how much quantum workspace can be reduced without sacrificing circuit depth, and study this tradeoff from both algorithmic and lower-bound perspectives.
For a Hermitian decomposition A=∑j=1LαjHj, with ∥Hj∥=1 and α=∑j∣αj∣, we give two coherent ε-approximate block-encoding constructions. The first uses one ancilla qubit and has depth O(L(α/ε)o(1)), while the second uses O(loglog(α/ε)) ancillas and achieves depth O(L).
For a broad Suzuki-based coherent simulation architecture, we prove an ancilla-depth tradeoff. In the polynomial-resource regime and for a constant number of coherent rounds, log(1/ε)≤O((logQ)2+2alogQ), where Q is depth normalized by the number of Hamiltonian terms and a is the ancilla count. Thus polylogarithmic dependence on 1/ε requires more than constantly many ancillas within this architecture. In a separate repeated-query LCU model, for balanced coefficients 1/L and error ε=η/L with fixed 0<η<1, we prove 2a=Ωη(L2/(T+L)), where T is the number of oracle queries. Hence a=Ω(logL) when T=O(Lα) for some α<2. Moreover, in the exact case, a≥logL regardless of T. We also extend this tradeoff to arbitrary nonnegative coefficients.
Finally, we apply our low-ancilla constructions to normalized trace estimation in DQC1, obtaining an optimal algorithm linear in the approximate degree together with a matching query lower bound. Together, these results establish quantitative space-depth and space-query tradeoffs in two natural circuit models.
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.
We determine the non-Clifford T-gate cost of constructing block encodings of structured fermionic and spin Hamiltonians in a unitary Clifford+T model, when arbitrarily many clean ancillas and unrestricted block-encoding subnormalization are allowed, but without mid-circuit measurements or classical feed-forward. Our main technical tool is an ancilla-compression theorem: any block encoding of an n-qubit operator with a clean ancillas and at most sT gates can be compressed to use at most min{a,n+2s} ancillas, without increasing the absolute error or T-count. For general second-quantized Hamiltonians with bounded one- and two-body coefficients, at operator-norm block-encoding error ε, a volume-covering argument combined with circuit counting gives the worst-case lower bound Ω(n2log(n4/ε)), matching the existing upper bound at fixed precision. For the bond-dependent Kitaev honeycomb family on n spins, we obtain independent lower bounds Ω(n) from stabilizer nullity and Ω(log(1/ε)) from one-qubit state preparation, established using different Hamiltonian instances. Together with an explicit LCU construction, they give the tight worst-case scaling Θ(n+log(1/ε)). As an application, we evaluate the T-count of a Hamiltonian simulation circuit based on quantum singular value transformation, with each block-encoding query compiled separately. When phase synthesis and controlled queries add at most constant-factor overhead, the simulation T-count scales as the query count times the optimal T-count per query.
Block-encoding operators are one of the essential components in quantum algorithms based on Quantum Signal Processing. Their gate complexity largely determines the overall gate complexity of the full algorithm. Using variational methods, we compile single-ancilla block-encoding unitaries with near-optimal resource requirements for a large range of input matrices. We find that the number of variational parameters in the parameterized quantum circuit approaches the number of free parameters in the input matrices, depending on whether they are real, complex and/or hermitian. Additionally, symmetries present in the input matrix can be incorporated into the ansatz circuit, reducing the parameter count further and enhancing optimizability. While performing a variational compilation of block-encodings ceases to be computationally feasible for large system sizes, the constructed operators can be used as components of larger block-encodings via a linear combination of block-encodings.
Unitary synthesis underlies universal quantum computation by translating unitary transformations into circuits of elementary gates. We show that for any budget m≥0 of ancilla, every n-qubit unitary can be exactly implemented using a quantum circuit consisting of O(4n) CNOT and single-qubit gates in O(n+4n/(n+m)) layers attaining the asymptotically optimal depth--ancilla tradeoff while simultaneously achieving optimal circuit size. The key idea is to encode computational basis states as wavefunctions sharing a Gaussian envelope, converting unitary action on amplitudes into coordinate transformations that admit shallow circuit implementations. The continuous encoding is then discretized, with approximation errors removed by exact residual correction. The depth-ancilla tradeoff follows by recursively applying the quantum Shannon decomposition, synthesizing its smaller unitary factors, and reusing ancillary qubits.
Uncomputation of ancilla qubits is essential in quantum computing to reset auxiliary qubits to a zero state, ensuring their safe discarding and reducing resource overhead. Current methods for uncomputation either rely on manual procedures or use additional ancilla qubits to store intermediate computation results, which increases qubit storage requirements. Additionally, most in-place uncomputation techniques employ an all-or-nothing strategy, wherein they perform uncomputation only when all ancilla qubits in the given circuit can be uncomputed.In this work, we tackle the problem of uncomputing a maximum number of ancilla qubits in a quantum circuit with minimal qubit storage overhead. We propose a novel approach where partial uncomputation—reverting the last ’m’ gates—enables the uncomputation of more qubits. We present three algorithms designed to uncompute the largest possible set of ancilla qubits and demonstrate their effectiveness through experiments on various randomly generated quantum circuits.