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Conference on Lasers and Electro-Optics

Finding Decoherence Free Subspaces Without Process Tomography

Dylan H. Mahler, Lee A. Rozema, Aephraim M. Steinberg

Published Jan 1, 2011• 0 citations
Publisher

Abstract

Quantum process tomography (QPT) is a resource intensive task. We present an experiment in which a 3 dimensional decoherence free subspace is experimentally identified using 36 measurements, instead of the 256 measurements required for QPT.
DOI
10.1364/qels.2011.qtho2

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Characterizing a quantum process is the critical first step towards applying such a process in a quantum information protocol. Full process characterization is known to be extremely resource-intensive, motivating the search for more efficient ways to extract salient information about the process. An example is the identification of ``decoherence-free subspaces,'' in which computation or communications may be carried out, immune to the principal sources of decoherence in the system. Here we propose and demonstrate a protocol which enables one to directly identify a decoherence-free subspace (DFS) without carrying out a full reconstruction. Our protocol offers an up-to-quadratic speedup over standard process tomography. In this paper, we experimentally identify the DFS of a two-qubit process with 32 measurements rather than the usual 256, characterize the robustness and efficiency of the protocol, and discuss its extension to higher-dimensional systems.
We describe experiments with photon pairs to evaluate, correct for, and avoid sources of error in optical quantum information processing. It is well known that a simple beamsplitter can non-deterministicially prepare or select entangled polarization states. We use quantum process tomography (QPT) to fully characterize this effect, including loss and decoherence. The QPT results identify errors and indicate how well they can be corrected. To evade decoherence in a noisy quantum channel, we identify decoherence-free subspaces using experimental channel characterization, without need for a priori knowledge of the decoherence mechanism or simplifying assumptions. Working with pairs of polarization-encoded photonic qubits, we use tomographic and adaptive techniques to identify 2- and 3-state decoherence-free subspaces for encoding decoherence-free qubits and qutrits within the noisy channel.
We describe experiments with photon pairs to evaluate, correct for, and avoid sources of error in optical quantum information processing. It is well known that a simple beamsplitter can non-deterministicially prepare or select entangled polarization states. We use quantum process tomography (QPT) to fully characterize this effect, including loss and decoherence. The QPT results identify errors and indicate how well they can be corrected. To evade decoherence in a noisy quantum channel, we identify decoherence-free subspaces using experimental channel characterization, without need for a priori knowledge of the decoherence mechanism or simplifying assumptions. Working with pairs of polarization-encoded photonic qubits, we use tomographic and adaptive techniques to identify 2- and 3-state decoherence-free subspaces for encoding decoherence-free qubits and qutrits within the noisy channel.
Quantum computation - the use of quantum systems as bits, or qubits, to perform computation - has been proven to be exponentially faster than what is thought possible with classical computation for several tasks. Quantum computers have the unique ability to store an amount of information that is exponential in the number of constituent particles used to encode that information. This property, which is arguably where quantum computers derive their power, makes it very difficult to characterize quantum systems. In order to fully characterize an unknown quantum state, a series of measurements must be performed on it and the state deduced from the results of those measurements. This process, called quantum tomography, is known to require exponential resources in the number of qubits. Both the number of measurements - the measurement complexity - and the number of times each measurement must be repeated - the sample complexity - present serious challenges when performing tomography. In addition, the measurement apparatus itself, which is usually a quantum system, must be characterized leading to even more experimental complexity. Here, I present three experiments which aim to reduce the experimental complexity of quantum tomography. The first aims to reduce the number of measurements needed to locate the decoherence-free subspaces of a noisy quantum channel. The process of locating these subspaces, which would normally require full process tomography, is performed with quadratically fewer measurement settings. The second experiment aims to characterize both an uncalibrated measurement apparatus and an unknown quantum state at the same time. With a specific model of measurement, it is found that an uncalibrated parameter of the measurement can be directly extracted using a single measurement. In the third experiment, I perform an experiment in which the optimal sample complexity for quantum state tomography is obtained using adaptivity: the ability to change measurement settings part way through an experiment. All three experiments were performed using the polarization degree of freedom of photons generated by spontaneous parametric downconversion. The source of photon pairs and the measurement techniques used are described in detail, along with the theory of quantum distinguishability and its relevance to tomography.
Quantum Process Tomography (QPT) methods aim at identifying, i.e. estimating, a quantum process. QPT is a major quantum information processing tool, since it especially allows one to experimentally characterize the actual behavior of quantum gates, that may be used as the building blocks of quantum computers. We here consider unitary (possibly dense, i.e. without sparsity constraints) processes, which corresponds to isolated systems. Moreover, we develop a QPT method applicable to a significant number of qubits and hence to a high state space dimension, which allows one to tackle more complex problems. Using the unitarity of the process allows us to propose a method that first achieves part of QPT by performing an eigenanalysis of the estimated density matrix of a process output. We then complete the QPT by using another state differently. The relevance of our method is validated by means of simulations.