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Leon Rullkötter

Publications and source records attributed to Leon Rullkötter.

3 recordsLinked to original sources

Experimental demonstration that qubits can be cloned at will, if encrypted with a single-use decryption key

The no-cloning theorem forbids the creation of identical copies of qubits, thereby imposing strong limitations on quantum technologies. A recently-proposed protocol, encrypted cloning, showed, however, that the creation of perfect clones is theoretically possible - if the clones are simultaneously encrypted with a single-use decryption key. It has remained an open question, however, whether encrypted cloning is stable under hardware noise and thus practical as a quantum primitive. This is nontrivial because spreading quantum information widely could dilute it until barely exceeding the noise level, leading to catastrophic fidelity decay. Given the complexity of hardware noise, theory and classical simulation are insufficient to settle this. Here, we settle this question experimentally, on IBM Heron-R2 superconducting processors using up to 154 qubits. We find that encrypted cloning is stable under hardware noise, even when used as a module, namely in parallel, series or interleaved, while preserving pre-existing entanglement. This establishes it as a versatile quantum primitive for practical use, and it necessitates a refinement to our understanding of the no-cloning theorem: quantum information can be spread at will, in theory and in practice, without dilution or degradation, if encrypted or obscured. The actual constraint is that the decryption mechanism must be single-use.

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Resource-efficient Variational Compilation of Block-Encodings

Block-encoding operators are one of the essential components in quantum algorithms based on Quantum Signal Processing. Their gate complexity largely determines the overall gate complexity of the full algorithm. Using variational methods, we compile single-ancilla block-encoding unitaries with near-optimal resource requirements for a large range of input matrices. We find that the number of variational parameters in the parameterized quantum circuit approaches the number of free parameters in the input matrices, depending on whether they are real, complex and/or hermitian. Additionally, symmetries present in the input matrix can be incorporated into the ansatz circuit, reducing the parameter count further and enhancing optimizability. While performing a variational compilation of block-encodings ceases to be computationally feasible for large system sizes, the constructed operators can be used as components of larger block-encodings via a linear combination of block-encodings.

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Unlocking Quantum Optimization: A Use Case Study on NISQ Systems

The major advances in quantum computing over the last few decades have sparked great interest in applying it to solve the most challenging computational problems in a wide variety of areas. One of the most pronounced domains here are optimization problems and a number of algorithmic approaches have been proposed for their solution. For the current noisy intermediate-scale quantum (NISQ) computers the quantum approximate optimization algorithm (QAOA), the variational quantum eigensolver (VQE), and quantum annealing (QA) are the central algorithms for this problem class. The two former can be executed on digital gate-model quantum computers, whereas the latter requires a quantum annealer. Across all hardware architectures and manufactures, the quantum computers available today share the property of being too error-prone to reliably execute involved quantum circuits as they typically arise from quantum optimization algorithms. In order to characterize the limits of existing quantum computers, many component and system level benchmarks have been proposed. However, owing to the complex nature of the errors in quantum systems these benchmark fail to provide predictive power beyond simple quantum circuits and small examples. Application oriented benchmarks have been proposed to remedy this problem, but both, results from real quantum systems as well as use cases beyond constructed academic examples, remain very rare. This paper addresses precisely this gap by considering two industrial relevant use cases: one in the realm of optimizing charging schedules for electric vehicles, the other concerned with the optimization of truck routes. Our central contribution are systematic series of examples derived from these uses cases that we execute on different processors of the gate-based quantum computers of IBM as well as on the quantum annealer of D-Wave.

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