
Compiling Quantum Circuits to Realistic Hardware Architectures using Temporal Planners
To run quantum algorithms on emerging gatemodel quantum hardware, quant...
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Multiobjective design of quantum circuits using genetic programming
Quantum computing is a new way of data processing based on the concept o...
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Quantum pixel representations and compression for Ndimensional images
We introduce a novel and uniform framework for quantum pixel representat...
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An efficient quantum circuits optimizing scheme compared with QISKit
Recently, the development of quantum chips has made great progress the...
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Improved FRQI on superconducting processors and its restrictions in the NISQ era
In image processing, the amount of data to be processed grows rapidly, i...
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Memcomputing for Accelerated Optimization
In this work, we introduce the concept of an entirely new circuit archit...
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A Curious New Result of Resolution Strategies in NegationLimited Inverters Problem
Generally, negationlimited inverters problem is known as a puzzle of co...
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A polynomial size model with implicit SWAP gate counting for exact qubit reordering
Due to the physics behind quantum computing, quantum circuit designers must adhere to the constraints posed by the limited interaction distance of qubits. Existing circuits need therefore to be modified via the insertion of SWAP gates, which alter the qubit order by interchanging the location of two qubits' quantum states. We consider the Nearest Neighbor Compliance problem on a linear array, where the number of required SWAP gates is to be minimized. We introduce an Integer Linear Programming model of the problem of which the size scales polynomially in the number of qubits and gates. Furthermore, we solve 131 benchmark instances to optimality using the commercial solver CPLEX. The benchmark instances are substantially larger in comparison to those evaluated with exact methods before. The largest circuits contain up to 18 qubits or over 100 quantum gates. This formulation also seems to be suitable for developing heuristic methods since (near) optimal solutions are discovered quickly in the search process.
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