We prove that learning an unknown quantum channel with input dimension $d_A$, output dimension $d_B$, and Choi rank $r$ to diamond distance $\varepsilon$ requires $ Ω\!\left( \frac{d_A d_B r}{\varepsilon \log(d_B r / \varepsilon)} \right)$ queries. This improves the best previous $Ω(d_A d_B r)$ bound by introducing explicit $\varepsilon$-dependence, with a scaling in $\varepsilon$ that is near-optimal when $d_A=rd_B$ but not tight in general. The proof constructs an ensemble of channels that are well-separated in diamond norm yet admit Stinespring isometries that are close in operator norm.
We prove that learning an unknown quantum channel with input dimension dA, output dimension dB, and Choi rank r to diamond distance ε requires Ω(εlog(dBr/ε)dAdBr) queries. This improves the best previous Ω(dAdBr) bound by introducing explicit ε-dependence, with a scaling in ε that is near-optimal when dA=rdB but not tight in general. The proof constructs an ensemble of channels that are well-separated in diamond norm yet admit Stinespring isometries that are close in operator norm.
Quantum process tomography, the task of estimating an unknown quantum channel, is a central problem in quantum information theory. A long-standing open question is how many uses of an unknown channel are required to learn it in diamond distance, the standard metric for distinguishing quantum processes. While quantum state tomography is well understood, for general channels the problem remained open beyond the unitary case. Here we establish the query complexity of channel tomography with optimal dependence on the dimension parameters, at any fixed constant accuracy. We design an algorithm showing that any channel with input/output dimensions din,dout and Kraus rank at most k can be learned to accuracy ε using O(dindoutk/ε2) channel uses. Conversely, we prove that Ω(dindoutk) uses are necessary at constant accuracy and that, for non-minimal Kraus rank, a separate Ω(1/ε2) contribution is unavoidable. Since channels subsume states, unitaries, isometries, and measurements as special cases, our protocol provides a unified framework for these tomography tasks, yielding new guarantees for isometry and measurement tomography while recovering known optimal scalings for state and unitary tomography. Our algorithm follows the natural strategy of performing optimal tomography on the Choi state. The main technical contribution is to show that this suffices to control the induced diamond-distance error, avoiding the dimension loss incurred by a naive conversion from Choi-state trace distance to channel diamond distance. The protocol uses the channel non-adaptively to prepare Choi-state copies, purifies them in parallel, and performs optimal pure-state tomography on the resulting purifications. Hence, we reduce channel tomography to pure-state tomography.
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 d1, output dimension d2, and Kraus rank at most r, to within error ε. We identify the dilation rate τ=rd2/d1 (which always satisfies τ≥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 τ.
- In the boundary regime (τ=1): we show that the query complexity is Θ(rd1d2/ε) for Choi trace norm error ε, and is upper bounded by O(min{rd11.5d2/ε,rd1d2/ε2}) and lower bounded by Ω(rd1d2/ε) for diamond norm error ε.
- In the away-from-boundary regime (τ≥1+Ω(1)): we show that the query complexity is Θ(rd1d2/ε2) for both Choi trace norm and diamond norm errors ε.
Our results uncover a sharp Heisenberg-to-classical phase transition in the query complexity of quantum channel tomography: at τ=1, the optimal query complexity exhibits Heisenberg scaling 1/ε, whereas for τ≥1+Ω(1), it exhibits classical scaling 1/ε2. In addition, we show that in the near-boundary regime (1<τ<1+o(1)), the query complexity exhibits a mixture of Heisenberg and classical scaling behaviors.
We study tomography for quantum channels with input dimension d1, output dimension d2, and Kraus rank at most r, to within diamond norm error ε, using adaptive experiments that retain no quantum memory between channel queries.
- For quantum channels whose non-zero Choi eigenvalues are bounded below by Ω(d1/r), we establish optimal query upper and lower bounds Θ(d1d2r2/ε2). The upper bound is achieved by a nonadaptive algorithm that uses the estimator from [Surawy-Stepney et al., Quantum (2022)], together with a new diamond-norm analysis. The lower bound applies to arbitrary adaptive incoherent protocols and follows from a new local family of channels and a uniform one-query Fisher-information bound.
- For general channels, we establish an upper bound O(d1d2r2log(2d1)/ε2), nearly matching the above lower bound Ω(d1d2r2/ε2). To achieve this, we generalize the above nonadaptive algorithm by adapting the input state over O(log(2d1)) rounds with the Matrix Multiplicative Weight Update algorithm.
Consider quantum channels with input dimension d1, output dimension d2 and Kraus rank at most r. Any such channel must satisfy the constraint rd2≥d1, and the parameter regime rd2=d1 is called the boundary regime. In this paper, we show an optimal query lower bound Ω(rd1d2/ε2) for quantum channel tomography to within diamond norm error ε in the away-from-boundary regime rd2≥2d1, matching the existing upper bound O(rd1d2/ε2). In particular, this lower bound fully settles the query complexity for the commonly studied case of equal input and output dimensions d1=d2=d with r≥2, in sharp contrast to the unitary case r=1 where Heisenberg scaling Θ(d2/ε) is achievable.
We study the problem of learning nearly (s,ε)-sparse unitaries, meaning that the Pauli spectrum is concentrated on at most s components with at most ε residual mass in Pauli ℓ1-norm. This class generalizes well-studied families, including sparse unitaries, quantum k-juntas, 2k-Pauli dimensional channels, and compositions of depth O(loglogn) circuits with near-Clifford circuits.
Given query access to an unknown nearly sparse unitary U, our goal is to efficiently (both in time and query complexity) construct a quantum channel that is close in diamond distance to U. We design a learning algorithm achieving this guarantee using O~(s6/ε4) forward queries to U, and running time polynomial in relevant parameters.
A key contribution is an efficient quantum algorithm that, given query access to an arbitrary unknown unitary U, estimates all Pauli coefficients (up to a shared global phase) whose magnitude exceeds a given threshold θ, extending existing sparse recovery techniques to general unitaries.
We also study the broader class of unitaries with bounded Pauli ℓ1-norm. For that class, we prove an exponential query lower bound Ω(2n/2). We introduce a more relaxed accuracy metric which is the diamond distance restricted to a set of input states. Then, we show that, under this metric, unitaries with Pauli ℓ1-norm uniformly bounded by L1 are learnable with O~(L18/ε16).
The recently introduced random purification channel, which converts n copies of an arbitrary mixed quantum state into n copies of the same uniformly random purification, has emerged as a powerful tool in quantum information theory. Motivated by this development, we introduce a channel-level analogue, which we call the random Stinespring superchannel. This consists in a procedure to transform n parallel queries of an arbitrary quantum channel into n parallel queries of the same uniformly random Stinespring isometry, via universal encoding and decoding operations that are efficiently implementable. When the channel is promised to have Choi rank at most r, the procedure can be tailored to yield a Stinespring environment of dimension r. We present two applications of the random Stinespring superchannel, one in quantum Shannon theory and one in quantum learning theory. In quantum Shannon theory, we prove a channel-level analogue of Uhlmann's theorem for quantum divergences. In quantum learning theory, our construction shows that tomography of quantum channels reduces to tomography of isometries. This yields a simple channel learning algorithm, based on existing isometry learning protocols, that matches the performance of the two recently proposed channel tomography algorithms. Complementarily, whereas the optimality of these algorithms had previously been established only up to a logarithmic factor in the dimension, we close this gap by removing this logarithmic factor from the lower bound. Taken together, our results fully establish the optimality of these recently introduced channel learning algorithms, showing that the optimal query complexity of learning a quantum channel with input dimension dA, output dimension dB, and Choi rank r is Θ(dAdBr).