We investigate the computational complexity of estimating the operator norm distance ${\rm T}_{\infty}(ρ_0,ρ_1)$, defined via the operator norm $\|A\|_{\infty} = σ_{\max}(A)$, given ${\rm poly}(n)$-size state-preparation circuits of $n$-qubit quantum states $ρ_0$ and $ρ_1$. We provide efficient quantum estimators for the operator norm distance whose complexity is independent of the rank (and thus the dimension) of the states: 1. When one state is pure, we establish an optimal quantum estimator using $Θ(1/ε)$ queries to the state-preparation circuits. Consequently, for constant additive error, say $ε=1/5$, our estimator runs in ${\rm poly}(n)$ time. Since the operator norm distance ${\rm T}_{\infty}(|ψ\rangle\!\langleψ|,ρ)$ is exactly half of the trace distance ${\rm T}(|ψ\rangle\!\langleψ|,ρ)$, our result also gives rank-independent query complexity for estimating both quantities, whereas the approaches due to van Apeldoorn, Cornelissen, Gily{é}n, and Nannicini (SODA 2023) and Wang and Zhang (TIT 2024) have query complexity scaling at least linearly with ${\rm rank}(ρ)$, which can be $\exp(n)$ in general. 2. For general quantum states, we also provide a quantum estimator using $\widetilde{O}(1/ε^{3/2})$ queries to the state-preparation circuits, which shows that the corresponding promise problem is ${\sf BQP}$-complete and improves the ${\sf QMA}$ upper bound sketched by Liu and Wang (ESA 2025). Together with an $Ω(1/ε)$ quantum query complexity lower bound, this leaves only square-root room for improvement. The key intuition behind our estimators is that, when one state is pure, the pure state $|ψ\rangle$ has overlap at least $1/2$ with the top unit eigenvector of $|ψ\rangle\!\langleψ|-ρ$, reflecting a structural feature specific to the operator norm distance.

We propose a quantum multi-level estimation framework for a functional ∑_{i=1}^n f(p_i) of a discrete distribution (p_i)_{i=1}^n. We partition the values p_i into logarithmically many intervals whose length decays exponentially. For each interval, we perform non-destructive singular value discrimination to isolate the relevant p_i, enabling adaptive estimation of the partial sum over this interval. Unlike previous variable-time approaches, our method avoids high control overhead and requires only constant extra ancilla qubits. As an application, we present efficient quantum estimators for the q-Tsallis entropy of discrete distributions. Specifically, - For q > 1, we obtain a near-optimal quantum algorithm with query complexity Θ̃(1/ε^{max{1/(2(q-1)), 1}}), improving the prior best O(1/ε^{1+1/(q-1)}) due to Liu and Wang (SODA 2025; IEEE Trans. Inf. Theory 2026). - For 0 < q < 1, we obtain a quantum algorithm with query complexity Õ(n^{1/q-1/2}/ε^{1/q}), exhibiting a quantum speedup over the near-optimal classical estimators due to Jiao, Venkat, Han, and Weissman (IEEE Trans. Inf. Theory 2017). Our results achieve, to our knowledge, the first near-optimal quantum estimators for parameterized q-entropy for non-integer q.

We consider the problem of quantum channel certification to unitary, where one is given access to an unknown d-dimensional channel ℰ, and wants to test whether ℰ is equal to a target unitary channel or is ε-far from it in the diamond norm. We present optimal quantum algorithms for this problem, settling the query complexities in three access models with increasing power. Specifically, we show that: 1) Θ(d/ε²) queries suffice for incoherent access model, matching the lower bound due to Fawzi, Flammarion, Garivier, and Oufkir (COLT 2023). 2) Θ(d/ε) queries suffice for coherent access model, matching the lower bound due to Regev and Schiff (ICALP 2008). 3) Θ(√d/ε) queries suffice for source-code access model, matching the lower bound due to Jeon and Oh (npj Quantum Inf. 2026). This demonstrates a strict hierarchy of complexities for quantum channel certification to unitary across various access models.

We settle the problem of estimating the trace distance and (square root) fidelity between $n$-qubit pure quantum states to within additive error $\varepsilon$, given their independent samples, which was raised as an open question by Wang (IEEE Trans. Inf. Theory 2024). This is achieved by a quantum algorithm with optimal sample complexity $Θ(1/\varepsilon^2)$, improving the long-standing folklore with sample complexity $O(1/\varepsilon^4)$. At the heart of our algorithm is a samplized phase estimation of the product of two Householder reflections. This is realized by an improved (multi-)samplizer for pure states, through which any quantum query algorithm using $Q$ queries to the reflection operator $I - 2|ψ\rangle\!\langleψ|$ can be converted to a $δ$-close (in the diamond norm distance) quantum sample algorithm using $Θ(Q^2/δ)$ samples of the state $|ψ\rangle$. This samplizer for pure states is also shown to be optimal.

The complexity class Quantum Statistical Zero-Knowledge (QSZK), introduced by Watrous (FOCS 2002) and later refined in Watrous (SICOMP, 2009), has the best known upper bound QIP(2) ∩ co-QIP(2), which was simplified following the inclusion QIP(2) ⊆ PSPACE established in Jain, Upadhyay, and Watrous (FOCS 2009). Here, QIP(2) denotes the class of promise problems that admit two-message quantum interactive proof systems in which the honest prover is typically computationally unbounded, and co-QIP(2) denotes the complement of QIP(2). We slightly improve this upper bound to QIP(2) ∩ co-QIP(2) with a quantum linear-space honest prover. Specifically, the honest prover uses space linear in the size of the transcript of the original QSZK proof system. A similar improvement also applies to the upper bound for the non-interactive variant NIQSZK. Our main techniques are algorithmic versions of the Holevo-Helstrom measurement and the Uhlmann transform, both implementable in quantum linear space, implying polynomial-time complexity in the state dimension, using the recent space-efficient quantum singular value transformation of Le Gall, Liu, and Wang (CC, to appear).

We formalize and study the Hamiltonian certification problem, a fundamental task in quantum physics, crucial for verifying the accuracy of quantum simulations and quantum-enhanced technologies. Given access to \(e^{-iHt}\) for an unknown Hamiltonian \(H\), the goal of the problem is to determine whether \(H\) is \(\varepsilon_1\)-close to or \(\varepsilon_2\)-far from a target Hamiltonian \(H_0\). While Hamiltonian learning methods have been extensively studied, they often require restrictive assumptions and suffer from inefficiencies when adapted for certification tasks.

ABSTRACT Depression is a global mental health problem, and early identification from social media text is crucial for timely intervention. However, the implicit and context‐dependent nature of depression cues in text renders their detection particularly difficult. To address these issues, this paper proposes the Hierarchical Semantic Sentiment‐aware Linguistic Network (HSSLN) for text‐based depression detection. The Hierarchical Semantic Module (HSM) applies multi‐granularity interaction to capture hierarchical semantic dependencies. The Sentiment‐aware Module (SAM) constructs a sentiment‐related matrix to model subtle sentiment cues under weak supervision. The Linguistic‐enhanced Fusion Module (LEFM) employs hierarchical modality integration to combine linguistic patterns with semantic‐sentiment representations. Experiments on the Twitter and Reddit datasets demonstrate that HSSLN effectively identifies subtle depression‐related cues by jointly leveraging semantic, sentiment and linguistic information.

Given an array a [ 1 . . n ] , the Range Minimum Query (RMQ) problem is to maintain a data structure that supports RMQ queries: given a range [ l , r ] , find the index of the minimum element among a [ l . . r ] , i.e., arg min i ∈ [ l , r ] a [ i ] . In this paper, we propose a quantum data structure that supports RMQ queries and range updates, with an optimal time complexity Θ ˜ ( n q ) for performing q = O ( n ) operations without preprocessing, compared to the classical Θ ˜ ( n + q ) . 1 As an application, we obtain a time-efficient quantum algorithm for k -minimum finding without the use of quantum random access memory .

We investigate the computational complexity of estimating the trace of quantum state powers tr(ρ<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^q</i>) for an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> qubit mixed quantum state ρ, given its state-preparation circuit of size poly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n)</i>. This quantity is closely related to and often interchangeable with the Tsallis entropy S<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_q</i>(ρ) = 1—tr(ρ<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^q</i>)/<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i>—1, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> = 1 corresponds to the von Neumann entropy. For any non-integer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i>≥ 1 + Ω(1), we provide a quantum estimator for S<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i>(ρ) with time complexity poly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n)</i>, exponentially improving the prior best results of exp<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n)</i> 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 computable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniform approximations</i> of positive power functions into quantum singular value transformation. Our quantum algorithm reveals a sharp phase transition between the case of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> = 1 and constant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> > 1 in the computational complexity of the Quantum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i>-Tsallis Entropy Difference Problem (TsallisQED <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_q</i> ), particularly deciding whether the difference S<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_q</i>(ρ0)−S<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_q</i> (ρ1) is at least 0.001 or at most −0.001: • For any 1 + Ω (1) ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> ≤ 2, TsallisQED <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_q</i> is BQP-complete, which implies that Purity Estimation is also BQP-complete. • For any 1≤<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i>≤1+1/<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> — 1, TsallisQED q is QSZK-hard, leading to hardness of approximating the von Neumann entropy because S<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_q</i>(ρ) ≤ S (ρ), as long as BQPQSZK. The hardness results are derived from reductions based on new inequalities for the quantum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i>-Jensen–(Shannon–)Tsallis divergence with 1 ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> ≤ 2, which are of independent interest.

We introduce two models of space-bounded quantum interactive proof systems, QIPL and QIP_{U}L. The QIP_{U}L model, a space-bounded variant of quantum interactive proofs (QIP) introduced by Watrous (CC 2003) and Kitaev and Watrous (STOC 2000), restricts verifier actions to unitary circuits. In contrast, QIPL allows logarithmically many pinching intermediate measurements per verifier action, making it the weakest model that encompasses the classical model of Condon and Ladner (JCSS 1995). We characterize the computational power of QIPL and QIP_{U}L. When the message number m is polynomially bounded, QIP_{U}L ⊊ QIPL unless P = NP: - QIPL^HC, a subclass of QIPL defined by a high-concentration condition on yes instances, exactly characterizes NP. - QIP_{U}L is contained in P and contains SAC¹ ∪ BQL, where SAC¹ denotes problems solvable by classical logarithmic-depth, semi-unbounded fan-in circuits. However, this distinction vanishes when m is constant. Our results further indicate that (pinching) intermediate measurements uniquely impact space-bounded quantum interactive proofs, unlike in space-bounded quantum computation, where BQL = BQ_{U}L. We also introduce space-bounded unitary quantum statistical zero-knowledge (QSZK_{U}L), a specific form of QIP_{U}L proof systems with statistical zero-knowledge against any verifier. This class is a space-bounded variant of quantum statistical zero-knowledge (QSZK) defined by Watrous (SICOMP 2009). We prove that QSZK_{U}L = BQL, implying that the statistical zero-knowledge property negates the computational advantage typically gained from the interaction.

As often emerges in various basic quantum properties such as entropy, the trace of quantum state powers $\operatorname{tr}(\rho^q)$ has attracted a lot of attention. The recent work of Liu and Wang (SODA 2025) showed that $\operatorname{tr}(\rho^q)$ can be estimated to within additive error $\varepsilon$ with a dimension-independent sample complexity of $\widetilde O(1/\varepsilon^{3+\frac{2}{q-1}})$ for any constant $q > 1$, where only an $\Omega(1/\varepsilon)$ lower bound was given. In this paper, we significantly improve the sample complexity of estimating $\operatorname{tr}(\rho^q)$ in both the upper and lower bounds. In particular: - For $q > 2$, we settle the sample complexity with matching upper and lower bounds $\widetilde \Theta(1/\varepsilon^2)$. - For $1 < q < 2$, we provide an upper bound $\widetilde O(1/\varepsilon^{\frac{2}{q-1}})$, with a lower bound $\Omega(1/\varepsilon^{\max\{\frac{1}{q-1}, 2\}})$ for dimension-independent estimators, implying there is only room for a quadratic improvement. Our upper bounds are obtained by (non-plug-in) quantum estimators based on weak Schur sampling, in sharp contrast to the prior approach based on quantum singular value transformation and samplizer.

We present an optimal quantum algorithm for fidelity estimation between two quantum states when one of them is pure. In particular, the (square root) fidelity of a mixed state to a pure state can be estimated to within additive error ε by using Θ(1/ε) queries to their state-preparation circuits, achieving a quadratic speedup over the folklore O(1/ε²). Our approach is technically simple, and can moreover estimate the quantity √{tr(ρσ²)} that is not common in the literature. To the best of our knowledge, this is the first query-optimal approach to fidelity estimation involving mixed states.

Quantum $k$-minimum finding is a fundamental subroutine with numerous applications in combinatorial problems and machine learning. Previous approaches typically assume oracle access to exact function values, making it challenging to integrate this subroutine with other quantum algorithms. In this paper, we propose an (almost) optimal quantum $k$-minimum finding algorithm that works with approximate values for all $k \geq 1$, extending a result of van Apeldoorn, Gilyén, Gribling, and de Wolf (FOCS 2017) for $k=1$. As practical applications of this algorithm, we present efficient quantum algorithms for identifying the $k$ smallest expectation values among multiple observables and for determining the $k$ lowest ground state energies of a Hamiltonian with a known eigenbasis.

We investigate the computational complexity of estimating the trace of quantum state powers tr(ρq ) for an n-qubit 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 entropywhere q = 1 corresponds to the von Neumann entropy. For any non-integer q ≥ 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 (ESA 2024). Our speedup is achieved by introducing efficiently computable uniform approximations of positive power functions into quantum singular value transformation.

The palindromic tree (a.k.a. eertree) is a data structure that provides access to all palindromic substrings of a string. In this paper, we propose a dynamic version of eertree, called double-ended eertree, which supports online operations on the stored string, including double-ended queue operations, counting distinct palindromic substrings, and finding the longest palindromic prefix/suffix. At the heart of our construction, we identify a new class of substring occurrences, called surfaces, that are palindromic substring occurrences that are neither prefixes nor suffixes of any other palindromic substring occurrences, which is of independent interest. Surfaces characterize the link structure of all palindromic substrings in the eertree, thereby allowing a linear-time implementation of double-ended eertrees through a linear-time maintenance of surfaces.

Abstract. The polynomial method by Beals, Buhrman, Cleve, Mosca, and de Wolf (FOCS 1998; [ J. ACM, 48 (2001), pp. 778–797]) and the adversary method by Ambainis (STOC 2000; [ J. Comput. System Sci., 64 (2002), pp. 750–767]) and the compressed oracle method by Zhandry [ Proceedings of the 39 th CRYPTO, 2019, pp. 239–268] have been shown to be powerful in proving quantum query lower bounds for a wide variety of problems. In this paper, we propose a new method for proving quantum query lower bounds by a quantum sample-to-query lifting theorem, which is from an information theory perspective. Using this method, we obtain the following new results: (1) A quadratic relation between quantum sample and query complexities regarding quantum property testing, which is optimal and saturated by quantum state discrimination. Here, the sample complexity is measured given sample access to the quantum state to be tested, while the query complexity is measured given query access to an oracle that block-encodes the quantum state. (2) A matching lower bound [Formula: see text] for quantum Gibbs sampling at inverse temperature [Formula: see text] ([Formula: see text] suppresses logarithmic factors), showing that the quantum Gibbs sampler by Gilyén, Su, Low, and Wiebe [ Proceedings of the 51 st STOC, 2019, pp. 193–204] is optimal. (3) A new lower bound [Formula: see text] for the entanglement entropy problem with gap [Formula: see text], which was recently studied by She and Yuen [ Proceedings of the 14 th ITCS, 2023, pp. 96:1–96:17]. (4) A series of quantum query lower bounds for matrix spectrum testing, based on the sample lower bounds for quantum state spectrum testing by O’Donnell and Wright (STOC 2015; [ Comm. Math. Phys., 387 (2021), pp. 1–95]). In addition, we provide unified proofs for some known lower bounds that have been proven previously via different techniques, including those for phase/amplitude estimation and Hamiltonian simulation.

Lexicographically minimal string rotation is a fundamental problem in string processing that has recently garnered significant attention in quantum computing. Near-optimal quantum algorithms have been proposed for solving this problem, utilizing a divide-and-conquer structure. In this note, we show that its quantum query complexity is n ⋅ 2 O ( log ⁡ n ) , improving the prior result of n ⋅ 2 ( log ⁡ n ) 1 / 2 + ε by Akmal and Jin (2022). Notably, this improvement is quasi-polylogarithmic, which is achieved by only logarithmic level-wise optimization using fault-tolerant quantum minimum finding.

Entropy is a measure of the randomness of a system. Estimating the entropy of a quantum state is a basic problem in quantum information. In this paper, we introduce a time-efficient quantum approach to estimating the von Neumann entropyS(ρ) and Rényi entropySα(ρ) of anN-dimensional quantum state ρ, given access to independent samples of ρ. Specifically, we provide the following quantum estimators. • A quantum estimator forS(ρ) with time complexity Õ (N2),1improving the prior best time complexity Õ(N6) by Acharya, Issa, Shende, and Wagner (2020) and Bavarian, Mehraba, and Wright (2016). • A quantum estimator forSα(ρ) with time complexity Õ (N4/α−2) for 0N4−2/α) for α > 1, improving the prior best time complexity Õ(N6/α) for 0N6) for α > 1 by Acharya, Issa, Shende, and Wagner (2020), though at a cost of a slightly larger sample complexity. Moreover, these estimators are naturally extensible to the low-rank case. We also provide a sample lower bound Ω(max{N/ε,N1/α−1/ε1/α}) for estimatingSα(ρ). Technically, our method is quite different from the previous ones that are based on weak Schur sampling and Young diagrams. At the heart of our construction, is a novel tool calledsamplizer, which can “samplize” a quantum query algorithm to a quantum algorithm with similar behavior using only samples of quantum states; this suggests a new framework for estimating quantum entropies. Specifically, when a quantum oracleUblockencodes a mixed quantum state ρ, any quantum query algorithm usingQqueries toUcan be samplized to a δ-close (in the diamond norm) quantum algorithm using Θ(Q2/δ) samples of ρ. Moreover, this samplization is proven to be optimal, up to a polylogarithmic factor.

Entropy is a measure of the randomness of a system. Estimating the entropy of a quantum state is a basic problem in quantum information. In this paper, we introduce a time-efficient quantum approach to estimating the von Neumann entropy S(ρ) and Rényi entropy S_α(ρ) of an N-dimensional quantum state ρ, given access to independent samples of ρ. Specifically, we provide the following quantum estimators. - A quantum estimator for S(ρ) with time complexity Õ(N²), improving the prior best time complexity Õ(N⁶) by Acharya, Issa, Shende, and Wagner (2020) and Bavarian, Mehraba, and Wright (2016). - A quantum estimator for S_α(ρ) with time complexity Õ(N^{4/α-2}) for 0 < α < 1 and Õ(N^{4-2/α}) for α > 1, improving the prior best time complexity Õ(N^{6/α}) for 0 < α < 1 and Õ(N⁶) for α > 1 by Acharya, Issa, Shende, and Wagner (2020), though at a cost of a slightly larger sample complexity. Moreover, these estimators are naturally extensible to the low-rank case. We also provide a sample lower bound Ω(max{N/ε, N^{1/α-1}/ε^{1/α}}) for estimating S_α(ρ). Technically, our method is quite different from the previous ones that are based on weak Schur sampling and Young diagrams. At the heart of our construction, is a novel tool called samplizer, which can "samplize" a quantum query algorithm to a quantum algorithm with similar behavior using only samples of quantum states; this suggests a unified framework for estimating quantum entropies. Specifically, when a quantum oracle U block-encodes a mixed quantum state ρ, any quantum query algorithm using Q queries to U can be samplized to a δ-close (in the diamond norm) quantum algorithm using Θ~(Q²/δ) samples of ρ. Moreover, this samplization is proven to be optimal, up to a polylogarithmic factor.

Abstract Lexicographically minimal string rotation (LMSR) is a problem to find the minimal one among all rotations of a string in the lexicographical order, which is widely used in equality checking of graphs, polygons, automata and chemical structures. In this paper, we propose an $$O(n^{3/4})$$ O(n3/4) quantum query algorithm for LMSR. In particular, the algorithm has average-case query complexity $$O(\sqrt{n} \log n)$$ O(nlogn) , which is shown to be asymptotically optimal up to a polylogarithmic factor, compared to its $$\Omega \left( \sqrt{n/\log n}\right) $$ Ωn/logn lower bound. Furthermore, we show that our quantum algorithm outperforms any (classical) randomized algorithms in both worst and average cases. As an application, it is used in benzenoid identification and disjoint-cycle automata minimization.

We explore potential quantum speedups for the fundamental problem of testing the properties of closeness andk-wise uniformity of probability distributions. •Closeness testingis the problem of distinguishing whether twon-dimensional distributions are identical or at least ε-far in ℓ1- or ℓ2-distance. We show that the quantum query complexities for ℓ1- and ℓ2-closeness testing areO(√n/ε) andO(1/ε), respectively, both of which achieve optimal dependence on ε, improving the prior best results of Gilyén and Li (2019). •k-wise uniformity testingis the problem of distinguishing whether a distribution over {0, 1}nis uniform when restricted to anykcoordinates or ε-far from any such distribution. We propose the first quantum algorithm for this problem with query complexityO(√nk/ε), achieving a quadratic speedup over the state-of-the-art classical algorithm with sample complexityO(nk/ε2) by O’Donnell and Zhao (2018). Moreover, whenk= 2 our quantum algorithm outperforms any classical one because of the classical lower bound Ω(n/ε2). All our quantum algorithms are fairly simple and time-efficient, using only basic quantum subroutines such as amplitude estimation.

Measuring the distinguishability between quantum states is a basic problem in quantum information theory. In this paper, we develop optimal quantum algorithms that estimate both the trace distance and the (square root) fidelity between pure states to within additive error$\varepsilon $using$\Theta (1/\varepsilon)$queries to their state-preparation circuits, quadratically improving the long-standing folklore$O(1/\varepsilon ^{2}) $. At the heart of our construction, is an algorithmic tool for quantum square root amplitude estimation, which generalizes the well-known quantum amplitude estimation.

We propose a series of quantum algorithms for computing a wide range of quantum entropies and distances, including the von Neumann entropy, quantum Rényi entropy, trace distance, and fidelity. The proposed algorithms significantly outperform the prior best (and even quantum) ones in the low-rank case, some of which achieve exponential speedups. In particular, forN-dimensional quantum states of rankr, our proposed quantum algorithms for computing the von Neumann entropy, trace distance and fidelity within additive error ε have time complexity of Õ(r/ε2), Õ(r5/ε6) and Õ(r6.5/ε7.5), respectively. By contrast, prior quantum algorithms for the von Neumann entropy and trace distance usually have time complexity Ω(N), and the prior best one for fidelity has time complexity Õ(r12.5/ε13.5). The key idea of our quantum algorithms is to extend block-encoding from unitary operators in previous work to quantum states (i.e., density operators). It is realized by developing several convenient techniques to manipulate quantum states and extract information from them. The advantage of our techniques over the existing methods is that no restrictions on density operators are required; in sharp contrast, the previous methods usually require a lower bound on the minimal non-zero eigenvalue of density operators.

In quantum information, trace distance is a basic metric of distinguishability between quantum states. However, there is no known efficient approach to estimate the value of trace distance in general. In this paper, we propose efficient quantum algorithms for estimating the trace distance within additive error ε between mixed quantum states of rankr. Specifically, we first provide a quantum algorithm usingr· Õ(1/ε2) queries to the quantum circuits that prepare the purifications of quantum states. Then, we modify this quantum algorithm to obtain another algorithm using Õ (r2/ε5) samples of quantum states, which can be applied to quantum state certification. These algorithms have query/sample complexities that are independent of the dimensionNof quantum states, and their time complexities only incur an extraO(log(N)) factor. In addition, we show that the decision version of low-rank trace distance estimation is BQP-complete.

We propose the first online quantum algorithm for zero-sum games with $\widetilde O(1)$ regret under the game setting. Moreover, our quantum algorithm computes an $\varepsilon$-approximate Nash equilibrium of an $m \times n$ matrix zero-sum game in quantum time $\widetilde O(\sqrt{m+n}/\varepsilon^{2.5})$. Our algorithm uses standard quantum inputs and generates classical outputs with succinct descriptions, facilitating end-to-end applications. Technically, our online quantum algorithm "quantizes" classical algorithms based on the optimistic multiplicative weight update method. At the heart of our algorithm is a fast quantum multi-sampling procedure for the Gibbs sampling problem, which may be of independent interest.

Estimating the unitarity of an unknown quantum channel$\mathcal {E}$provides information on how much it is unitary, which is a basic and important problem in quantum device certification and benchmarking. Unitarity estimation can be performed with either coherent or incoherent access, where the former in general leads to better query complexity while the latter allows more practical implementations. In this paper, we provide a unified framework for unitarity estimation, which induces ancilla-efficient algorithms that use$O(\epsilon ^{-2})$and$O(\sqrt {d}\cdot \epsilon ^{-2})$calls to$\mathcal {E}$with coherent and incoherent accesses, respectively, where$d$is the dimension of the system that$\mathcal {E}$acts on and$\epsilon $is the required precision. We further show that both the$d$-dependence and$\epsilon $-dependence of our algorithms are optimal. As part of our results, we settle the query complexity of the distinguishing problem for depolarizing and unitary channels with incoherent access by giving a matching lower bound$\Omega (\sqrt {d})$, improving the prior best lower bound$\Omega (\sqrt [{3}]{d})$by (Aharonov et al., 2022) and (Chen et al., FOCS 2021).

For two unknown mixed quantum states$\rho $and$\sigma $in an$N$-dimensional Hilbert space, computing their fidelity$F(\rho,\sigma)$is a basic problem with many important applications in quantum computing and quantum information, for example verification and characterization of the outputs of a quantum computer, and design and analysis of quantum algorithms. In this paper, we propose a quantum algorithm that solves this problem in${\mathrm{ poly}}(\log (N), r, 1/\varepsilon)$time, where$r$is the lower rank of$\rho $and$\sigma $, and$\varepsilon $is the desired precision, provided that the purifications of$\rho $and$\sigma $are prepared by quantum oracles. This algorithm exhibits an exponential speedup over the best known algorithm (based on quantum state tomography) which has time complexity polynomial in$N$.

We define a formal framework for equivalence checking of sequential quantum circuits. The model we adopt is a quantum state machine, which is a natural quantum generalization of Mealy machines. A major difficulty in checking quantum circuits (but not present in checking classical circuits) is that the state spaces of quantum circuits are continuums. This difficulty is resolved by our main theorem showing that equivalence checking of two quantum Mealy machines can be done with input sequences that are taken from some chosen basis (which are finite) and have a length quadratic in the dimensions of the state Hilbert spaces of the machines. Based on this theoretical result, we develop an (and to the best of our knowledge, the first) algorithm for checking equivalence of sequential quantum circuits with running time$\mathcal {O}(2^{3m+5l}(2^{3m}{\,+\,}2^{3l}))$, where$m$and$l$denote the numbers of input and internal qubits, respectively. The complexity of our algorithm is comparable with that of the known algorithms for checking classical sequential circuits in the sense that both are exponential in the number of (qu)bits. Several case studies and experiments are presented.

Exploration efficiency challenges for multi-agent reinforcement learning (MARL), as the policy learned by confederate MARL depends on the interaction among agents. Less informative reward also restricts the learning speed of MARL in comparison with the informative label in supervised learning. This paper proposes a novel communication method which helps agents focus on different exploration subarea to guide MARL to accelerate exploration. We propose a predictive network to forecast the reward of current state-action pair and use the guidance learned by the predictive network to modify the reward function. An improved prioritized experience replay is employed to help agents better take advantage of the different knowledge learned by different agents. Experimental results demonstrate that the proposed algorithm outperforms existing methods in cooperative multi-agent environments.