A fundamental challenge in quantum information science is certifying that an n-qubit state$\rho$prepared in the lab closely matches a target state$\vert \psi\rangle$. Previous approaches to this problem often require deep quantum circuits, exponentially many single-qubit measurements, or are limited to specific state families. In this work, we introduce a new method that leverages a connection between state certification and the mixing time of a random walk, allowing almost all n-qubit target states, including those with exponential circuit complexity, to be certified with only$\mathrm{O}(n^{2})$single-qubit measurements. Our protocol is broadly compatible with various experimental platforms and has applications in benchmarking quantum systems, optimizing quantum circuits, and efficiently learning and verifying representations of quantum states—such as neural networks and tensor networks—using only single-qubit measurements. Moreover, these verified representations enable the efficient prediction of highly non-local properties of$\rho$that would otherwise require an exponential number of measurements.

Matrix product states (MPS) are a class of physically-relevant quantum states which arise in the study of quantum many-body systems. A quantum state comprised of n qudits is said to be an MPS of bond dimension r if the reduced density matrix ψ1, …, k has rank r for each k ∊ {1, …, n}. When r = 1, this corresponds to the set of product states, i.e. states of the form |ψ1〉 ⊗ ⃛ ⊗ |ψn), which possess no entanglement. For larger values of r, this yields a more expressive class of quantum states, which are allowed to possess limited amounts of entanglement. Devising schemes for testing the amount of entanglement in quantum systems has played a crucial role in quantum computing and information theory. In this work, we study the problem of testing whether an unknown state |ψ〉 is an MPS in the property testing model. In this model, one is given m identical copies of |ψ〉, and the goal is to determine whether |ψ〉 is an MPS of bond dimension r or whether |ψ〉 is far from all such states. For the case of product states, we study the product test, a simple two-copy test previously analyzed by Harrow and Montanaro [17], and a key ingredient in their proof that QMA(2) = QMA(k) for k ≥ 2. We give a new and simpler analysis of the product test which achieves an optimal bound for a wide range of parameters, answering open problems in [17] and [23]. For the case of r ≥ 2, we give an efficient algorithm for testing whether |ψ〉 is an MPS of bond dimension r using m = O(nr2) copies, independent of the dimensions of the qudits, and we show that Ω(n1/2) copies are necessary for this task. This lower bound shows that a dependence on the number of qudits n is necessary, in sharp contrast to the case of product states where a constant number of copies suffices.

We study the problem of learning the Hamiltonian of a quantum many-body system given samples from its Gibbs (thermal) state. The classical analog of this problem, known as learning graphical models or Boltzmann machines, is a well-studied question in machine learning and statistics. In this work, we give the first sample-efficient algorithm for the quantum Hamiltonian learning problem. In particular, we prove that polynomially many samples in the number of particles (qudits) are necessary and sufficient for learning the parameters of a spatially local Hamiltonian in l_2-norm. Our main contribution is in establishing the strong convexity of the log-partition function of quantum many-body systems, which along with the maximum entropy estimation yields our sample-efficient algorithm. Classically, the strong convexity for partition functions follows from the Markov property of Gibbs distributions. This is, however, known to be violated in its exact form in the quantum case. We introduce several new ideas to obtain an unconditional result that avoids relying on the Markov property of quantum systems, at the cost of a slightly weaker bound. In particular, we prove a lower bound on the variance of quasi-local operators with respect to the Gibbs state, which might be of independent interest. Our work paves the way toward a more rigorous application of machine learning techniques to quantum many-body problems.

We present a quasi-polynomial time classical algorithm that estimates the partition function of quantum many-body systems at temperatures above the thermal phase transition point. It is known that in the worst case, the same problem is NP-hard below this point. Together with our work, this shows that the transition in the phase of a quantum system is also accompanied by a transition in the hardness of approximation. We also show that in a system of n particles above the phase transition point, the correlation between two observables whose distance is at least Ω(logn) decays exponentially. We can improve the factor of logn to a constant when the Hamiltonian has commuting terms or is on a 1D chain. The key to our results is a characterization of the phase transition and the critical behavior of the system in terms of the complex zeros of the partition function. Our work extends a seminal work of Dobrushin and Shlosman on the equivalence between the decay of correlations and the analyticity of the free energy in classical spin models. On the algorithmic side, our result extends the scope of a recent approach due to Barvinok for solving classical counting problems to quantum many-body systems.

In this paper, a simple memory limited transmitter for molecular communication is proposed, in which information is encoded in the emission rate of the molecules. Taking advantage of memory, the proposed transmitter reduces the ISI problem by properly adjusting its emission rate, which can be interpreted as water-filling on the expected interference. The error probability of the proposed scheme is derived and the result is compared with the error probability of the optimal transmitter obtained by dynamic programming methods. Furthermore, for the special case of channel with one symbol memory, a tight lower bound on error probability is derived. Numerical results show that the performance of introduced transmitter is near optimal. Simplicity is the key feature of the presented communication system: the transmitter follows a simple rule, the receiver is a simple threshold decoder, and only one type of molecule is used to convey the information.

In this paper, a simple memory limited transmitter for molecular communication is proposed, in which information is encoded in the diffusion rate of the molecules. Taking advantage of memory, the proposed transmitter reduces the ISI problem by properly adjusting its diffusion rate. The error probability of the proposed scheme is derived and the result is compared with the lower bound on error probability of the optimum transmitter. It is shown that the performance of introduced transmitter is near optimal (under certain simplifications). Simplicity is the key feature of the presented communication system: the transmitter follows a simple rule, the receiver is a simple threshold decoder and only one type of molecule is used to convey the information.