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Transpilation (e.g. qubits, gates)

Transpilation is the process of rewriting a quantum computer program so that it can actually run on a specific piece of hardware. When a programmer designs a quantum circuit, they often use “logical” instructions that assume a perfect environment. However, real quantum processors have physical limits: not all qubits are connected to each other, and each chip has its own specific language of “native gates.” The transpiler acts as an automated translator. It reroutes the instructions to fit the hardware’s layout and swaps complex commands for simpler ones the machine understands. This step is vital for reducing errors and ensuring the program finishes before the quantum information is lost to noise.

Topics in the Subfield Software for quantum computing hardware:

Select another topic within the subfield Software for quantum computing hardware

Error mitigation including noise learning at the software and hardware levels

Error mitigation refers to techniques that reduce the impact of noise in quantum computations without requiring a…

Resource estimation (e.g. gate count, qubit scaling, circuit depth)

Hardware benchmarks (e.g. error rates, Quantum Volume, CLOPS)

Hardware benchmarks quantify how effectively a quantum processor can execute real world workloads…

Noise models (e.g. depolarizing noise, cross talk, Hamiltonian learning)

Quantum processors are inherently fragile. Interactions with the environment and imperfect control lead to…

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Transpilation (e.g. qubits, gates)

Topics in the Subfield Software for quantum computing hardware:

Error mitigation including noise learning at the software and hardware levels

Resource estimation (e.g. gate count, qubit scaling, circuit depth)

Hardware benchmarks (e.g. error rates, Quantum Volume, CLOPS)

Noise models (e.g. depolarizing noise, cross talk, Hamiltonian learning)

Partners

Trans­pi­la­tion (e.g. qubits, gates)

Topics in the Subfield Software for quantum compu­ting hardware:

Error mitiga­tion inclu­ding noise learning at the software and hardware levels

Resource estima­tion (e.g. gate count, qubit scaling, circuit depth)

Hardware bench­marks (e.g. error rates, Quantum Volume, CLOPS)

Noise models (e.g. depola­ri­zing noise, cross talk, Hamil­to­nian learning)

Partners

Transpilation (e.g. qubits, gates)

Topics in the Subfield Software for quantum computing hardware:

Error mitigation including noise learning at the software and hardware levels

Resource estimation (e.g. gate count, qubit scaling, circuit depth)

Hardware benchmarks (e.g. error rates, Quantum Volume, CLOPS)

Noise models (e.g. depolarizing noise, cross talk, Hamiltonian learning)