Mapping quantum circuits to shallow-depth measurement patterns based on graph states

• URL: http://arxiv.org/abs/2311.16223v1
• Date: Mon, 27 Nov 2023 19:00:00 GMT
• Title: Mapping quantum circuits to shallow-depth measurement patterns based on graph states
• Authors: Thierry Nicolas Kaldenbach and Matthias Heller
• Abstract summary: We create a hybrid simulation technique for measurement-based quantum computing. We show that groups of fully commuting operators can be implemented using fully-parallel, i.e., non-adaptive, measurements. We discuss how such circuits can be implemented in constant quantum depths by employing quantum teleportation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paradigm of measurement-based quantum computing (MBQC) starts from a highly entangled resource state on which unitary operations are executed through adaptive measurements and corrections ensuring determinism. This is set in contrast to the more common quantum circuit model, in which unitary operations are directly implemented through quantum gates prior to final measurements. In this work, we incorporate concepts from MBQC into the circuit model to create a hybrid simulation technique, permitting us to split any quantum circuit into a classically efficiently simulatable Clifford-part and a second part consisting of a stabilizer state and local (adaptive) measurement instructions, a so-called standard form, which is executed on a quantum computer. We further process the stabilizer state with the graph state formalism, thus enabling a significant decrease in circuit depth for certain applications. We show that groups of fully commuting operators can be implemented using fully-parallel, i.e., non-adaptive, measurements within our protocol. In addition, we discuss how such circuits can be implemented in constant quantum depths by employing quantum teleportation. Finally, we demonstrate the utility of our technique on two examples of high practical relevance: the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE).

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