Iman Marvian

The problem of coherence distillation asks: given multiple copies of a coherent input state, produce a coherent output state with much less noise (that is, much closer to a pure coherent state), in such a way that the time evolution of the output state matches the time evolution of the input state. We thoroughly address this problem for the case of qubit states. Principally, we compute the distillation channel that is asymptotically optimal in the limit of a large number of input qubit states and compute the resulting infidelity (one minus fidelity) between the output qubit state and the desired pure state. We show that this infidelity is closely related to a resource known as purity of coherence, a quantity obtained from the Right Logarithmic Derivative (RLD) Fisher information metric. We thus demonstrate an operational meaning for purity of coherence as the quantity whose monotonicity on time-translation invariant channels sets the strongest asymptotic bound on the capability of coherence distillation for qubit states. For a special choice of input and output state, we additionally investigate a number of extensions of this primary result. In particular, we present numerical schemes to produce the exact optimal distillation protocol for a fixed value of $N$, extend the computation of the optimal protocol and the minimum infidelity to the $1/N^2$ and $1/N^3$ orders, estimate the amount of purity of coherence that is dissipated by the optimal distillation protocol, and compute the mathematical behavior of the protocol more precisely in the low-noise and high-noise limits.

The goal of qubit purification is to combine multiple noisy copies of an unknown pure quantum state to obtain one or more copies that are closer to the pure state. We show that a simple protocol based solely on random SWAP tests achieves the same fidelity as the Schur transform, which is optimal. This protocol relies only on elementary two-qubit SWAP tests, which project a pair of qubits onto the singlet or triplet subspaces, to identify and isolate singlet pairs, and then proceeds with the remaining qubits. For a system of $n$ qubits, we show that after approximately $T \approx n \ln n$ random SWAP tests, a sharp transition occurs: the probability of detecting any new singlet decreases exponentially with $T$. Similarly, the fidelity of each remaining qubit approaches the optimal value given by the Schur transform, up to an error that is exponentially small in $T$. More broadly, this protocol achieves what is known as weak Schur sampling and unitary Schur sampling with error $\epsilon$, after only $2n \ln(n \epsilon^{-1})$ SWAP tests. That is, it provides a lossless method for extracting any information invariant under permutations of qubits, making it a powerful subroutine for tasks such as quantum state tomography and metrology.

Finding the consequences of symmetries in dynamics is a subject with broad applications in physics. We are interested to find tools to study the consequences of symmetry which apply to open as well as closed quantum systems. We introduce a different point of view for thinking about asymmetry which is not based on symmetric dynamics; instead it is based on the intuition that a state which breaks symmetry can be thought as a signal which carries information about the group element. Using this point of view we use information measures to build asymmetry measures via the Holevo quantity. We show that for pure states Noether's theorem includes all the possible implications of the symmetry of dynamics, but for mixed states conservation of asymmetry measures imply more constraints than those prescribed by Noether's theorem.

Learning the Hamiltonian governing a quantum system is a central task in quantum metrology, sensing, and device characterization. Existing Heisenberg-limited Hamiltonian learning protocols either require multi-qubit operations that are prone to noise, or single-qubit operations whose frequency or strength increases with the desired precision. These two requirements limit the applicability of Hamiltonian learning on near-term quantum platforms. We present a protocol that learns a quantum Hamiltonian with the optimal Heisenberg-limited scaling using only single-qubit control in the form of static fields with strengths that are independent of the target precision. Our protocol is robust against the state preparation and measurement (SPAM) error. By overcoming these limitations, our protocol provides new tools for device characterization and quantum sensing. We demonstrate that our method achieves the Heisenberg-limited scaling through rigorous mathematical proof and numerical experiments. We also prove an information-theoretic lower bound showing that a non-vanishing static field strength is necessary for achieving the Heisenberg limit unless one employs an extensive number of discrete control operations.