arXiv subjects

Nengkun Yu

Publications and source records attributed to Nengkun Yu.

At least 19 recordsLinked to original sources

arXiv2026

Full-Rank Noise Forbids Long-Range Entanglement Swapping

Quantum repeaters extend entanglement by swapping noisy elementary links. We prove that full-rank noise forbids this at long range: for any entangled full-rank two-qubit link, there is a finite depth beyond which no end-to-end entanglement can be established regardless of the measurement outcomes on intermediate qubits, even under any adaptive postselected strategy. This limit is set by one spectral parameter of the link, giving a no-go criterion for repeater routing. In contrast, we construct link state families of rank three and rank two that admit postselected measurement outcome branches of exponentially small probability but with strictly positive concurrence at every finite depth. Swapping experiments on a superconducting processor show that links of equal initial concurrence but different rank behave differently under postselected swapping. In the language of many-body physics, the chain is a matrix-product density operator, and full-rank bonds forbid long-range localizable entanglement, while rank-deficient bonds can sustain it.

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arXiv2026

Quantum Mixedness Testing with Pauli Measurements

We consider a fundamental problem of \emph{mixedness testing}: Given $n$ copies of an $N$-qubit state $\rho$, determine whether $\rho = \mathbb{I}_d/d$ or $\|\rho-\mathbb{I}_d/d\|_1 \geq \varepsilon$ with high probability, where $d = 2^N$. In particular, we focus on performing this task in the practical setting of single-qubit measurements, where measurements are prepared independently on each qubit. We provide a nearly complete picture of single-qubit mixedness tesing by showing $n = \widetilde{\Theta}\left(\sqrt{10}^N/\varepsilon^2\right)$. To establish our lower bound, we introduce a new measurement-dependent lower bound framework for adaptive single-copy state certification. For the upper bound, we present a randomized Pauli basis measurement protocol, which relies on a new primitive for computationally efficient uniformity testing of correlation-concentrated distributions on the Boolean hypercube.

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arXiv2026

Quantum channel tomography: optimal bounds and a Heisenberg-to-classical phase transition

How many black-box queries to a quantum channel are needed to learn its full classical description? This question lies at the heart of quantum channel tomography (also known as quantum process tomography), a fundamental task in the characterization and validation of quantum hardware. Despite extensive prior work, the optimal query complexity for quantum channel tomography is far from fully understood. In this paper, we study tomography of an unknown quantum channel with input dimension $d_1$, output dimension $d_2$, and Kraus rank at most $r$, to within error $\varepsilon$. We identify the dilation rate $\tau = r d_2 / d_1$ (which always satisfies $\tau\geq 1$ due to the trace preservation of quantum channels) as a key parameter, and establish that the optimal query complexity of channel tomography exhibits distinct scaling laws across three regimes of $\tau$. - In the boundary regime ($\tau = 1$): we show that the query complexity is $\Theta(r d_1 d_2/\varepsilon)$ for Choi trace norm error $\varepsilon$, and is upper bounded by $O(\min\{r d_1^{1.5} d_2/\varepsilon, r d_1 d_2/\varepsilon^2\})$ and lower bounded by $\Omega(r d_1 d_2/\varepsilon)$ for diamond norm error $\varepsilon$. - In the away-from-boundary regime ($\tau \geq 1+\Omega(1)$): we show that the query complexity is $\Theta(r d_1 d_2/\varepsilon^2)$ for both Choi trace norm and diamond norm errors $\varepsilon$. Our results uncover a sharp Heisenberg-to-classical phase transition in the query complexity of quantum channel tomography: at $\tau=1$, the optimal query complexity exhibits Heisenberg scaling $1/\varepsilon$, whereas for $\tau\geq 1+\Omega(1)$, it exhibits classical scaling $1/\varepsilon^2$. In addition, we show that in the near-boundary regime ($1< \tau < 1+o(1)$), the query complexity exhibits a mixture of Heisenberg and classical scaling behaviors.

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arXiv2026

Formalizing CHSH Rigidity in Lean 4

Violation of the Clauser-Horne-Shimony-Holt (CHSH) inequality certifies genuine quantum correlations. In this work, we formalize in Lean 4 the rigidity theorem -- any strategy achieving near-optimal CHSH value must be locally isometric to the canonical qubit strategy. In the course of formalization, we identified a gap in the argument of McKague, Yang, and Scarani (arXiv:1203.2976).

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arXiv2026

Manjushri: A Tool for Equivalence Checking of Quantum Circuits

Verifying whether two quantum circuits are equivalent is a central challenge in the compilation and optimization of quantum programs. We introduce \textsc{Manjushri}, a new automated framework for scalable quantum-circuit equivalence checking. \textsc{Manjushri} uses local projections as discriminative circuit fingerprints, implemented with weighted binary decision diagrams (WBDDs), yielding a compact and efficient symbolic representation of quantum behavior. We present an extensive experimental evaluation that, for random 1D Clifford+$T$ circuits, explores the trade-off between \textsc{Manjushri} and \textsc{ECMC}, a tool for equivalence checking based on a much different approach. \textsc{Manjushri} is much faster up to depth 30 (with the crossover point varying from 39--49, depending on the number of qubits and whether the input circuits are equivalent or inequivalent): when inputs are equivalent, \textsc{Manjushri} is about 10$\times$ faster (or more); when inputs are inequivalent, \textsc{Manjushri} is about 8$\times$ faster (or more). For both kinds of equivalence-checking outcomes, \textsc{ECMC}'s success rate out to depth 50 is impressive on 32- and 64-qubit circuits: on such circuits, \textsc{ECMC} is almost uniformly successful. However, \textsc{ECMC} struggled on 128-qubit circuits for some depths. \textsc{Manjushri} is almost uniformly successful out to about depth 38, before tailing off to about 75\% at depth 50 (falling to 0\% at depth 48 for 128-qubit circuits that are equivalent). These results establish that \textsc{Manjushri} is a practical and scalable solution for large-scale quantum-circuit verification, and would be the preferred choice unless clients need to check equivalence of circuits of depth $>$38.

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arXiv2026

Optimal lower bound for quantum channel tomography in away-from-boundary regime

Consider quantum channels with input dimension $d_1$, output dimension $d_2$ and Kraus rank at most $r$. Any such channel must satisfy the constraint $rd_2\geq d_1$, and the parameter regime $rd_2=d_1$ is called the boundary regime. In this paper, we show an optimal query lower bound $\Omega(rd_1d_2/\varepsilon^2)$ for quantum channel tomography to within diamond norm error $\varepsilon$ in the away-from-boundary regime $rd_2\geq 2d_1$, matching the existing upper bound $O(rd_1d_2/\varepsilon^2)$. In particular, this lower bound fully settles the query complexity for the commonly studied case of equal input and output dimensions $d_1=d_2=d$ with $r\geq 2$, in sharp contrast to the unitary case $r=1$ where Heisenberg scaling $\Theta(d^2/\varepsilon)$ is achievable.

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arXiv2025

Quantum channel tomography and estimation by local test

We study the estimation of an unknown quantum channel $\mathcal{E}$ with input dimension $d_1$, output dimension $d_2$ and Kraus rank at most $r$. We establish a connection between the query complexities in two models: (i) access to $\mathcal{E}$, and (ii) access to a random dilation of $\mathcal{E}$. Specifically, we show that for parallel (possibly coherent) testers, access to dilations does not help. This is proved by constructing a local tester that uses $n$ queries to $\mathcal{E}$ yet faithfully simulates the tester with $n$ queries to a random dilation. As application, we show that: - $O(rd_1d_2/\varepsilon^2)$ queries to $\mathcal{E}$ suffice for channel tomography to within diamond norm error $\varepsilon$. Moreover, when $rd_2=d_1$, we show that the Heisenberg scaling $O(1/\varepsilon)$ can be achieved, even if $\mathcal{E}$ is not a unitary channel: - $O(\min\{d_1^{2.5}/\varepsilon,d_1^2/\varepsilon^2\})$ queries to $\mathcal{E}$ suffice for channel tomography to within diamond norm error $\varepsilon$, and $O(d_1^2/\varepsilon)$ queries suffice for the case of Choi state trace norm error $\varepsilon$. - $O(\min\{d_1^{1.5}/\varepsilon,d_1/\varepsilon^2\})$ queries to $\mathcal{E}$ suffice for tomography of the mixed state $\mathcal{E}(|0\rangle\langle 0|)$ to within trace norm error $\varepsilon$.

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arXiv2025

Towards Efficient Verification of Computation in Quantum Devices

Designing quantum processors is a complex task that demands advanced verification methods to ensure their correct functionality. However, traditional methods of comprehensively verifying quantum devices, such as quantum process tomography, face significant limitations because of the exponential growth in computational resources. These limitations arise from treating the system as a black box and ignoring its design structure. Consequently, new testing methods must be developed considering the design structure. In this paper, we investigate the structure of computations on the hardware, focusing on the layered interruptible quantum circuit model and designing a scalable algorithm to verify it comprehensively. Specifically, for a given quantum hardware that claims to process an unknown $n$ qubit $d$ layer circuit via a finite set of quantum gates, our method completely reconstructs the circuits within a time complexity of $O(d^2 t \log (n/\delta))$, guaranteeing success with a probability of at least $1-\delta$. Here, $t$ represents the maximum execution time for each circuit layer. Our approach significantly reduces execution time for completely verifying computations in quantum devices, achieving double logarithmic scaling in the problem size. Furthermore, we validate our algorithm through experiments using IBM's quantum cloud service, demonstrating its potential applicability in the noisy intermediate-scale quantum era.

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arXiv2025

Pauli Measurements Are Near-Optimal for Single-Qubit Tomography

We provide the first non-trivial lower bounds for single-qubit tomography algorithms and show that at least ${\Omega}\left(\frac{10^N}{\sqrt{N} \varepsilon^2}\right)$ copies are required to learn an $N$-qubit state $\rho\in\mathbb{C}^{d\times d},d=2^N$ to within $\varepsilon$ trace distance. Pauli measurements, the most commonly used single-qubit measurement scheme, have recently been shown to require at most $O\left(\frac{10^N}{\varepsilon^2}\right)$ copies for this problem. Combining these results, we nearly settle the long-standing question of the complexity of single-qubit tomography.

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arXiv2025

Entanglement Certification by Measuring Nonlocality

Reliable verification of entanglement is a central requirement for quantum networks. This paper presents a practical verification approach based on violations of the Clauser-Horne-Shimony-Holt (CHSH) inequality. We derive tight mathematical bounds that relate the CHSH value to entanglement fidelity and introduce a statistical framework that optimizes resource usage while ensuring reliable certification. Our main contributions are: (i) fidelity bounds derived directly from the CHSH measure, which also enable nonlocality certification at sufficiently high fidelities; (ii) a sample-complexity analysis that quantifies the number of measurements required to achieve desired confidence levels for the CHSH measure and the entanglement fidelity; and (iii) verification protocols, some with rigorous mathematical guarantees and others with numerical evaluation. Using NetSquid, we develop a simulation framework that models diverse network conditions and enables systematic exploration of trade-offs in CHSH-based verification. This framework highlights the interplay between accuracy, efficiency, and operational parameters, providing concrete guidelines for deploying entanglement verification in resource-constrained quantum networks.

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arXiv2025

SAQR-QC: A Logic for Scalable but Approximate Quantitative Reasoning about Quantum Circuits

Reasoning about quantum programs remains a fundamental challenge, regardless of the programming model or computational paradigm. Despite extensive research, existing verification techniques are insufficient -- even for quantum circuits, a deliberately restricted model that lacks classical control, but still underpins many current quantum algorithms. Many existing formal methods require exponential time and space to represent and manipulate (representations of) assertions and judgments, making them impractical for quantum circuits with many qubits. This paper presents a logic for reasoning in such settings, called SAQR-QC. The logic supports {S}calable but {A}pproximate {Q}uantitative {R}easoning about {Q}uantum {C}ircuits, whence the name. SAQR-QC has three characteristics: (i) some (deliberate) loss of precision is built into it; (ii) it has a mechanism to help the accumulated loss of precision during a sequence of reasoning steps remain small; and (iii) most importantly, to make reasoning scalable, every reasoning step is local -- i.e., it involves just a small number of qubits. We demonstrate the effectiveness of SAQR-QC via two case studies: the verification of GHZ circuits involving non-Clifford gates, and the analysis of quantum phase estimation -- a core subroutine in Shor's factoring algorithm.

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arXiv2025

Approximation does not help in quantum unitary time-reversal

Access to the time-reverse $U^{-1}$ of an unknown quantum unitary process $U$ is widely assumed in quantum learning, metrology, and many-body physics. The fundamental task of unitary time-reversal dictates implementing $U^{-1}$ to within diamond-norm error $\epsilon$ using black-box queries to the $d$-dimensional unitary $U$. Although the query complexity of this task has been extensively studied, existing lower bounds either hold only for the exact case (i.e., $\epsilon=0$) or are suboptimal in $d$. This raises a central question: does approximation help reduce the query complexity of unitary time-reversal? We settle this question in the negative by establishing a robust and tight lower bound $\Omega((1-\epsilon)d^2)$ with explicit dependence on the error $\epsilon$. This implies that unitary time-reversal retains optimal exponential hardness (in the number of qubits) even when constant error is allowed. Our bound applies to adaptive and coherent algorithms with unbounded ancillas and holds even when $\epsilon$ is an average-case distance error.

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arXiv2025

Scalable Equivalence Checking and Verification of Shallow Quantum Circuits

This paper concerns the problem of checking if two shallow (i.e., constant-depth) quantum circuits perform equivalent computations. Equivalence checking is a fundamental correctness question -- needed, e.g., for ensuring that transformations applied to a quantum circuit do not alter its behavior. For quantum circuits, the problem is challenging because a straightforward representation on a classical computer of each circuit's quantum state can require time and space that are exponential in the number of qubits $n$. The paper presents decision procedures for two variants of the equivalence-checking problem. Both can be carried out on a classical computer in time and space that, for any fixed depth, is linear in $n$. Our critical insight is that local projections are precise enough to completely characterize the output state of a shallow quantum circuit. Instead of explicitly computing the output state of a circuit, we generate a set of local projections that serve as constraints on the output state. Moreover, the circuit's output state is the unique quantum state that satisfies all the constraints. Beyond equivalence checking, we show how to use the constraint representation to check a class of assertions, both statically and at run time. Our assertion-checking methods are sound and complete for assertions expressed as conjunctions of local projections. Our experiments show that on a server equipped with 2 x Intel\textsuperscript{\textregistered} Xeon\textsuperscript{\textregistered} Gold 6338 CPUs (128 threads total) and 1.0~TiB of RAM, running Ubuntu 20.04.6 LTS, the constraint representation of a random 100-qubit circuit of depth 6 can be computed in 19.8 seconds. For fixed inputs $\ket{0}^{\otimes 100}$, equivalence checking of {random} 100-qubit circuits of depth 3 takes 4.46 seconds; for arbitrary inputs, it takes no more than 31.96 seconds.

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arXiv2025

Pauli measurements are not optimal for single-copy tomography

Quantum state tomography is a fundamental problem in quantum computing. Given $n$ copies of an unknown $N$-qubit state $\rho \in \mathbb{C}^{d \times d},d=2^N$, the goal is to learn the state up to an accuracy $\epsilon$ in trace distance, with at least probability 0.99. We are interested in the copy complexity, the minimum number of copies of $\rho$ needed to fulfill the task. Pauli measurements have attracted significant attention due to their ease of implementation in limited settings. The best-known upper bound is $O(\frac{N \cdot 12^N}{\epsilon^2})$, and no non-trivial lower bound is known besides the general single-copy lower bound $\Omega(\frac{8^n}{\epsilon^2})$, achieved by hard-to-implement structured POVMs such as MUB, SIC-POVM, and uniform POVM. We have made significant progress on this long-standing problem. We first prove a stronger upper bound of $O(\frac{10^N}{\epsilon^2})$. To complement it with a lower bound of $\Omega(\frac{9.118^N}{\epsilon^2})$, which holds under adaptivity. To our knowledge, this demonstrates the first known separation between Pauli measurements and structured POVMs. The new lower bound is a consequence of a novel framework for adaptive quantum state tomography with measurement constraints. The main advantage over prior methods is that we can use measurement-dependent hard instances to prove tight lower bounds for Pauli measurements. Moreover, we connect the copy-complexity lower bound to the eigenvalues of the measurement information channel, which governs the measurement's capacity to distinguish states. To demonstrate the generality of the new framework, we obtain tight-bounds for adaptive quantum tomography with $k$-outcome measurements, where we recover existing results and establish new ones.

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arXiv2024

What if you have only one copy? Low-depth quantum circuits have no advantage in decision problems!

The conventional approach to understanding the characteristics of an unknown quantum state involves having numerous identical independent copies of the system in that state. However, we demonstrate that gleaning insights into specific properties is feasible even with a single-state sample. Perhaps surprisingly, the confidence level of our findings increases proportionally with the number of qubits. Our conclusions apply to quantum states with low circuit complexity, including noise-affected ones. Additionally, this extends to learning from a solitary sample of probability distributions. Our results establish a strong lower bound for discriminating quantum states with low complexity. Furthermore, we reveal no quantum advantage in decision problems involving low-depth quantum circuits. Our results can be used to verify NISQ devices.

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arXiv2024

Adaptivity is not helpful for Pauli channel learning

We prove that adaptive strategies offer no advantage over non-adaptive ones for learning and testing Pauli channels using entangled inputs. This key observation allows us to characterize the query complexity for several fundamental tasks by translating optimal classical estimation algorithms into the quantum setting. First, we determine the tight query complexity for learning a Pauli channel under the general $\ell_p$ norm, providing results that improve upon or match the best-known bounds for the $\ell_1, \ell_2,$ and $\ell_\infty$ distances. Second, we resolve the complexity of testing whether a Pauli channel is a white noise source. Finally, we show that the optimal query complexities for estimating the Shannon entropy and support size of the channel's error distribution, and for estimating the diamond distance between two Pauli channels, are all $\Theta\left(\tfrac{4^n}{n\epsilon^2}\right)$.

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arXiv2023

Learning marginals suffices!

Beyond computer science, quantum complexity theory can potentially revolutionize multiple branches of physics, ranging from quantum many-body systems to quantum field theory. In this paper, we investigate the relationship between the sample complexity of learning a quantum state and the circuit complexity of the state. The circuit complexity of a quantum state refers to the minimum depth of the quantum circuit necessary to implement it. We show that learning its marginals for the quantum state with low circuit complexity suffices for state tomography, thus breaking the exponential barrier of the sample complexity for quantum state tomography. Our proof is elementary and overcomes difficulties characterizing short-range entanglement by bridging quantum circuit complexity and ground states of gapped local Hamiltonians. Our result, for example, settles the quantum circuit complexity of the multi-qubit GHZ state exactly.

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arXiv2023

Accelerating Voting by Quantum Computation

Studying the computational complexity and designing fast algorithms for determining winners under voting rules are classical and fundamental questions in computational social choice. In this paper, we accelerate voting by leveraging quantum computation: we propose a quantum-accelerated voting algorithm that can be applied to any anonymous voting rule. We show that our algorithm can be quadratically faster than any classical algorithm (based on sampling with replacement) under a wide range of common voting rules, including positional scoring rules, Copeland, and single transferable voting (STV). Precisely, our quantum-accelerated voting algorithm outputs the correct winner with high probability in $\Theta\left(\frac{n}{\text{MOV}}\right)$ time, where $n$ is the number of votes and $\text{MOV}$ is {\em margin of victory}, the smallest number of voters to change the winner. In contrast, any classical voting algorithm based on sampling with replacement requires $\Omega\left(\frac{n^2}{\text{MOV}^2}\right)$ time under a large class of voting rules. Our theoretical results are supported by experiments under plurality, Borda, Copeland, and STV.

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