Differential Evolution for Many-Particle Adaptive Quantum Metrology

We devise powerful algorithms based on differential evolution for adaptive many-particle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are orders-of-magnitude more efficient and surpass the few-dozen-particle limitation arising in methods based on particle-swarm optimization. We apply our method to the binary-decision-tree model for quantum-enhanced phase estimation as well as to a new problem: a decision tree for adaptive estimation of the unknown bias of a quantum coin in a quantum walk and show how this latter case can be realized experimentally.Comment: Fig. 2(a) is the cover of Physical Review Letters Vol. 110 Issue 2

Quantum feedback and adaptive measurements

Summary form only given. Although real-time feedback of measured signals is an essential component of sensing and control in classical settings, models for quantum feedback that are rigorous yet useful have only become possible since the advent of measurement-based quantum trajectory theory. The quantum feedback scenario introduces new concerns of coherence and measurement backaction, but recent work has shown that these can be treated properly in a formal integration of quantum trajectory theory with standard state-space formulations of filtering and control theory. Pioneering studies by H. M. Wiseman have shown that such models can be used to design and to analyze realistic schemes for adaptive homodyne measurement and for feedback control of atomic motion. Much of the ongoing research in our group focuses on the experimental implementation of such schemes. For a broad range of quantum feedback scenarios, certain recurring technical issues arise out of the need to perform complex, high-bandwidth processing of measured signals. We are developing a "rapid-prototyping" approach to refining signal processing and feedback algorithms via quantum trajectory simulation on a PC, followed by translation of the algorithms into hardware Description language (HDL)

Quantum logic as superbraids of entangled qubit world lines

Presented is a topological representation of quantum logic that views entangled qubit spacetime histories (or qubit world lines) as a generalized braid, referred to as a superbraid. The crossing of world lines is purely quantum in nature, most conveniently expressed analytically with ladder-operator-based quantum gates. At a crossing, independent world lines can become entangled. Complicated superbraids are systematically reduced by recursively applying novel quantum skein relations. If the superbraid is closed (e.g. representing quantum circuits with closed-loop feedback, quantum lattice gas algorithms, loop or vacuum diagrams in quantum field theory), then one can decompose the resulting superlink into an entangled superposition of classical links. In turn, for each member link, one can compute a link invariant, e.g. the Jones polynomial. Thus, a superlink possesses a unique link invariant expressed as an entangled superposition of classical link invariants.Comment: 4 page

Enhanced Feedback Iterative Decoding of Sparse Quantum Codes

Decoding sparse quantum codes can be accomplished by syndrome-based decoding using a belief propagation (BP) algorithm.We significantly improve this decoding scheme by developing a new feedback adjustment strategy for the standard BP algorithm. In our feedback procedure, we exploit much of the information from stabilizers, not just the syndrome but also the values of the frustrated checks on individual qubits of the code and the channel model. Furthermore we show that our decoding algorithm is superior to belief propagation algorithms using only the syndrome in the feedback procedure for all cases of the depolarizing channel. Our algorithm does not increase the measurement overhead compared to the previous method, as the extra information comes for free from the requisite stabilizer measurements.Comment: 10 pages, 11 figures, Second version, To be appeared in IEEE Transactions on Information Theor

Strategy for quantum algorithm design assisted by machine learning

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a "quantum student" is being taught by a "classical teacher." In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem assisted by classical main-feedback system. Our method is applicable to design quantum oracle-based algorithm. As a case study, we chose an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte-Carlo simulations that our simulator can faithfully learn quantum algorithm to solve the problem for given oracle. Remarkably, learning time is proportional to the square root of the total number of parameters instead of the exponential dependance found in the classical machine learning based method.Comment: published versio