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Bulletin of the American Physical Society

Dynamic learning of an experimental entanglement witness for an n-qubit system

Kathleen Walsh, Elizabeth Behrman, James E. Steck, Steven R. Skinner

Published Mar 13, 2006• 0 citations
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We present a dynamic learning paradigm for "programming" a general quantum computer. A learning algorithm is used to find the control parameters for a coupled qubit system, such that the system at an initial time evolves to a state in which a given measurement corresponds to the desired operation. We apply the method to a system of coupled superconducting quantum interference devices (SQUIDs), and demonstrate learning of the XOR, XNOR, Toffoli, and Fredkin gates. Striking out for more interesting territory, we attempt learning of an entanglement witness for a two qubit system. Simulation shows a reasonably successful mapping of the entanglement at the initial time onto the correlation function at the final time for both pure and mixed states. For pure states this mapping requires knowledge of the phase relation between the parts; however, given that knowledge, this method can be used to measure the entanglement of an unknown state.
Measurement of entanglement remains an important problem for quantum information. We present the design and simulation of an experimental method for an entanglement indicator for a general multiqubit state. The system can be in a pure or a mixed state, and it need not be ``close'' to any particular state. The system contains information about its own entanglement; we use dynamic learning methods to map this information onto a single experimental measurement which is our entanglement indicator. Our method does not require prior state reconstruction or lengthy optimization. An entanglement witness emerges from the learning process, beginning with two-qubit systems, and extrapolating this to three, four, and five qubit systems where the entanglement is not well understood. Our independently learned measures for three-qubit systems compare favorably with known entanglement measures. As the size of the system grows the amount of additional training necessary diminishes, raising hopes for applicability to large computational systems.
Entanglement witnesses are essential for certifying entanglement, yet constructing ones that are both noise-robust and economical in measurement settings remains challenging - particularly beyond qubits and for non-stabilizer ("magic") states. We present a machine-learning method that, given a target state and a user-specified number of measurement settings, generates an entanglement witness optimized for noise tolerance in the neighborhood of that state, requiring only local measurements. The approach is fully general, applying to multipartite qubit and qudit systems alike, including non-stabilizer states. For N qudits of dimension d, we train on the fully-separable eigenstates of each qudit's SU(d) generators to find a prototype witness, then tune the witness's bias term via gradient descent to maximize noise tolerance. Adversarial training further strengthens the witnesses, delivering greater noise tolerance with even fewer settings; critically, under this scheme the required training-set size becomes independent of system size. We package the entire pipeline as an automated script that, in every case we tested, produces witnesses surpassing all existing methods in noise tolerance and/or number of measurement settings. We demonstrate the method on Bell, GHZ, W, and hypergraph states, along with a range of qudit states, spanning 2-6 qubits, bipartite qudits up to d=10, and tripartite qutrits. Our witnesses achieve perfect accuracy across both physical experimental test states and large numerical sets of separable mixed states-including 30 million test states for a 3-qubit W-state witness and 10 million for a 4-qubit hypergraph-state witness-and we experimentally confirm the noise tolerance of Bell- and hypergraph state witnesses on both photonic and superconducting platforms, respectively.