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arXiv:2308.05079

Space-bounded quantum state testing via space-efficient quantum singular value transformation

François Le Gall, Yupan Liu, Qisheng Wang

Published Aug 9, 2023• 3 citations
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Abstract

Driven by exploring the power of quantum computation with a limited number of qubits, we present a novel complete characterization for space-bounded quantum computation, which encompasses settings with one-sided error (unitary $\sf coRQL$) and two-sided error ($\sf BQL$), approached from a quantum state testing perspective: - The first family of natural complete problems for unitary $\sf coRQL$, namely space-bounded quantum state certification for trace distance and Hilbert-Schmidt distance; - A new family of natural complete problems for $\sf BQL$, namely space-bounded quantum state testing for trace distance, Hilbert-Schmidt distance, and (von Neumann) entropy difference. In the space-bounded quantum state testing problem, we consider two logarithmic-qubit quantum circuits (devices) denoted as $Q_0$ and $Q_1$, which prepare quantum states $ρ_0$ and $ρ_1$, respectively, with access to their ``source code''. Our goal is to decide whether $ρ_0$ is $ε_1$-close to or $ε_2$-far from $ρ_1$ with respect to a specified distance-like measure. Interestingly, unlike time-bounded state testing problems, which exhibit computational hardness depending on the chosen distance-like measure, our results reveal that the space-bounded state testing problems, considering all three measures, are computationally as easy as preparing quantum states. Our results primarily build upon a space-efficient variant of the quantum singular value transformation (QSVT) introduced by Gilyén, Su, Low, and Wiebe (STOC 2019), which is of independent interest. Our technique provides a unified approach for designing space-bounded quantum algorithms. Specifically, we show that implementing QSVT for any bounded polynomial that approximates a piecewise-smooth function incurs only a constant overhead in terms of the space required for special forms of the projected unitary encoding.

DOI
10.48550/arxiv.2308.05079

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Driven by exploring the power of quantum computation with a limited number of qubits, we present a novel complete characterization for space-bounded quantum computation, which encompasses settings with one-sided error (unitary coRQL\sf coRQLcoRQL) and two-sided error (BQL\sf BQLBQL), approached from a quantum state testing perspective: - The first family of natural complete problems for unitary coRQL\sf coRQLcoRQL, namely space-bounded quantum state certification for trace distance and Hilbert-Schmidt distance; - A new family of natural complete problems for BQL\sf BQLBQL, namely space-bounded quantum state testing for trace distance, Hilbert-Schmidt distance, and (von Neumann) entropy difference. In the space-bounded quantum state testing problem, we consider two logarithmic-qubit quantum circuits (devices) denoted as Q0Q_0Q0​ and Q1Q_1Q1​, which prepare quantum states ρ0ρ_0ρ0​ and ρ1ρ_1ρ1​, respectively, with access to their ``source code''. Our goal is to decide whether ρ0ρ_0ρ0​ is ε1ε_1ε1​-close to or ε2ε_2ε2​-far from ρ1ρ_1ρ1​ with respect to a specified distance-like measure. Interestingly, unlike time-bounded state testing problems, which exhibit computational hardness depending on the chosen distance-like measure, our results reveal that the space-bounded state testing problems, considering all three measures, are computationally as easy as preparing quantum states. Our results primarily build upon a space-efficient variant of the quantum singular value transformation (QSVT) introduced by Gilyén, Su, Low, and Wiebe (STOC 2019), which is of independent interest. Our technique provides a unified approach for designing space-bounded quantum algorithms. Specifically, we show that implementing QSVT for any bounded polynomial that approximates a piecewise-smooth function incurs only a constant overhead in terms of the space required for special forms of the projected unitary encoding.
Recent advancements in quantum devices have posed an intriguing challenge of verifying their intended functionality.These devices, typically time-bounded, are often designed to prepare specific 𝑛-qubit states, denoted as 𝜌 ! and 𝜌 " .The problem of (tolerant) quantum state testing aims to design algorithms that can efficiently decide whether 𝜌 ! is either 𝜀 " -close to or 𝜀 # -far from 𝜌 " with respect to a chosen distance-like measure.This problem generalizes classical distribution testing to the quantum domain and forms a part of quantum property testing, an emerging field dedicated to developing efficient quantum testers for properties of quantum objects.The computational complexity of time-bounded state testing varies significantly with the choice of closeness measure, exhibiting a dichotomy-like behavior:• For both the trace distance (ℓ " norm) and the quantum (von Neumann) entropy difference, the problems correspond to the complexity class QSZK, as shown in Watrous (FOCS 2002), Watrous (SICOMP 2009), and Ben-Aroya, Schwartz, and Ta-Shma (CCC 2008), which is widely believed to be strictly more powerful than BQP.
We introduce shadow quantum singular value transformation (Shadow QSVT): given an initial state ∣ψ⟩|ψ\rangle∣ψ⟩, a Hermitian matrix HHH, a polynomial fff, and a set of observables {O1,…,Om}\{O_1,\dots,O_m\}{O1​,…,Om​}, the goal is to estimate ⟨ψ∣f(H)†Ojf(H)∣ψ⟩\langleψ|f(H)^{\dagger}O_j f(H)|ψ\rangle⟨ψ∣f(H)†Oj​f(H)∣ψ⟩ for all j∈{1,…,m}j\in\{1,\dots,m\}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)f(H)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 HHH and ∣ψ⟩|ψ\rangle∣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 HHH, fff, and ∣ψ⟩|ψ\rangle∣ψ⟩ without prior knowledge of the observables or explicit preparation of the target state proportional to f(H)∣ψ⟩f(H)|ψ\ranglef(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.
Motivated by understanding the power of quantum computation with restricted number of qubits, we give two complete characterizations of unitary quantum space bounded computation. First we show that approximating an element of the inverse of a well-conditioned efficiently encoded 2k(n)×2k(n)2^{k(n)}\times 2^{k(n)}2k(n)×2k(n) matrix is complete for the class of problems solvable by quantum circuits acting on O(k(n))\mathcal{O}(k(n))O(k(n)) qubits with all measurements at the end of the computation. Similarly, estimating the minimum eigenvalue of an efficiently encoded Hermitian 2k(n)×2k(n)2^{k(n)}\times 2^{k(n)}2k(n)×2k(n) matrix is also complete for this class. In the logspace case, our results improve on previous results of Ta-Shma [STOC '13] by giving new space-efficient quantum algorithms that avoid intermediate measurements, as well as showing matching hardness results. Additionally, as a consequence we show that PreciseQMA, the version of QMA with exponentially small completeness-soundess gap, is equal to PSPACE. Thus, the problem of estimating the minimum eigenvalue of a local Hamiltonian to inverse exponential precision is PSPACE-complete, which we show holds even in the frustration-free case. Finally, we can use this characterization to give a provable setting in which the ability to prepare the ground state of a local Hamiltonian is more powerful than the ability to prepare PEPS states. Interestingly, by suitably changing the parameterization of either of these problems we can completely characterize the power of quantum computation with simultaneously bounded time and space.
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(log⁡L)O(\log L)O(logL) ancilla overhead and circuit depth O~(Ldλ)\widetilde{O}(L dλ)O(Ldλ) for polynomial transformations of a Hamiltonian H=∑k=1LHkH=\sum_{k=1}^L H_kH=∑k=1L​Hk​, where ddd is the polynomial degree and λ=∑k∥Hk∥λ=\sum_{k}\|H_k\|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))\widetilde{O}(L(dλ_{\mathrm{comm}})^{1+o(1)})O(L(dλcomm​)1+o(1)), without requiring any complicated multi-qubit controlled gates. Moreover, λcommλ_{\mathrm{comm}}λcomm​ depends on the nested commutators of the terms of HHH and can be substantially smaller than λλλ 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 investigate the computational complexity of estimating the trace of quantum state powers tr(ρq) for annqubit mixed quantum state ρ, given its state-preparation circuit of size poly(n). This quantity is closely related to and often interchangeable with the Tsallis entropy Sq(ρ) = 1—tr(ρq)/q—1, whereq= 1 corresponds to the von Neumann entropy. For any non-integerq≥ 1 + Ω(1), we provide a quantum estimator for Sq(ρ) with time complexity poly(n), exponentially improving the prior best results of exp(n)due to Acharya, Issa, Shende, and Wagner (ISIT 2019), Wang, Guan, Liu, Zhang, and Ying (TIT 2024)| Wang, Zhang, and Li (TIT 2024), and Wang and Zhang (TIT 2025). Our speedup is achieved by introducing efficiently computableuniform approximationsof positive power functions into quantum singular value transformation. Our quantum algorithm reveals a sharp phase transition between the case ofq= 1 and constantq> 1 in the computational complexity of the Quantumq-Tsallis Entropy Difference Problem (TsallisQEDq), particularly deciding whether the difference Sq(ρ0)−Sq(ρ1) is at least 0.001 or at most −0.001: • For any 1 + Ω (1) ≤q≤ 2, TsallisQEDqis BQP-complete, which implies that Purity Estimation is also BQP-complete. • For any 1≤q≤1+1/n— 1, TsallisQED q is QSZK-hard, leading to hardness of approximating the von Neumann entropy because Sq(ρ) ≤ S (ρ), as long as BQPQSZK. The hardness results are derived from reductions based on new inequalities for the quantumq-Jensen–(Shannon–)Tsallis divergence with 1 ≤q≤ 2, which are of independent interest.
This paper presents a general space-efficient method for error reduction for unitary quantum computation. Consider a polynomial-time quantum computation with completeness c and soundness s, either with or without a witness (corresponding to QMA and BQP, respectively). To convert this computation into a new computation with error at most 2^{-p}, the most space-efficient method known requires extra workspace of O(p*log(1/(c-s))) qubits. This space requirement is too large for scenarios like logarithmic-space quantum computations. This paper shows an errorreduction method for unitary quantum computations (i.e., computations without intermediate measurements) that requires extra workspace of just O(log(p/(c-s))) qubits. This in particular gives the first method of strong amplification for logarithmic-space unitary quantum computations with two-sided bounded error. This also leads to a number of consequences in complexity theory, such as the uselessness of quantum witnesses in bounded-error logarithmic-space unitary quantum computations, the PSPACE upper bound for QMA with exponentially-small completeness-soundness gap, and strong amplification for matchgate computations.