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Quantum Information Processing

Quantum channels over graph states using generalized measurement-based quantum computation framework

Ankur Raina, Shayan Srinivasa Garani

Published Jan 31, 2020• 0 citations
Publisher
DOI
10.1007/s11128-020-2597-7

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What happens if in QMA the quantum channel between Merlin and Arthur is noisy? It is not difficult to show that such a modification does not change the computational power as long as the noise is not too strong so that errors are correctable with high probability, since if Merlin encodes the witness state in a quantum error-correction code and sends it to Arthur, Arthur can correct the error caused by the noisy channel. If we further assume that Arthur can do only single-qubit measurements, however, the problem becomes nontrivial, since in this case Arthur cannot do the universal quantum computation by himself. In this paper, we show that such a restricted complexity class is still equivalent to QMA. To show it, we use measurement-based quantum computing: honest Merlin sends the graph state to Arthur, and Arthur does fault-tolerant measurement-based quantum computing on the noisy graph state with only single-qubit measurements. By measuring stabilizer operators, Arthur also checks the correctness of the graph state. Although this idea itself was already used in several previous papers, these results cannot be directly used to the present case, since the test that checks the graph state used in these papers is so strict that even honest Merlin is rejected with high probability if the channel is noisy. We therefore introduce a more relaxed test that can accept not only the ideal graph state but also noisy graph states that are error-correctable.
The stabilizer formalism is a central framework in quantum computing and one of the key building blocks for fault-tolerant quantum computers. According to the Gottesman–Knill theorem, we can efficiently simulate any quantum system consisting of stabilizer states, Clifford gates, and Pauli measurements on a classical computer. A key challenge in developing robust quantum technologies is that physical quantum systems are unavoidably affected by noise, which degrades quantum information. Accurate noise modeling is therefore essential. For stabilizer states, the noisy stabilizer formalism has recently been introduced as a framework for efficiently describing the evolution of graph states subject to Pauli-diagonal noise under Clifford operations and Pauli measurements. In this thesis, we explore and extend this formalism in two directions. First, we generalize the method from graph states to arbitrary stabilizer states, thereby extending the Gottesman-Knill theorem to noisy scenarios. Second, we extend the formalism to handle noise models beyond Pauli-diagonal channels. Moreover, we study the applicability of the noisy stabilizer formalism to entanglement purification protocols.
The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasistochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits using an attention network based on a powerful neural network ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits that build Greenberger-Horne-Zeilinger and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on several system sizes. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems.