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D. Cernatinschi
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Quantum Circuit Routing Optimises the Wrong Metric
Closing the Proxy Gap Between SWAP Count and Schedule Length
To run a quantum program on real hardware, a compiler must rewrite it so that every interacting pair of qubits is physically adjacent on the chip, which it does by inserting extra SWAP operations. How these SWAPs are chosen determines how reliably the program runs: each one adds gates and lengthens the schedule, and on today’s noisy devices both effects make a wrong answer more likely. Reliability thus depends on two costs at once, the program’s size and its running time, yet the routing pass that quantum compilers deploy, SABRE, optimises only the first: it minimises the number of SWAPs. The SWAP count stands in for size but not for running time, even though running time is the cost that limits reliability most on current hardware. Optimising it alone is a case of a pass tuning a convenient proxy rather than the quantity that ultimately matters.
We show this proxy gap is real and then close it. SABRE-MS keeps SABRE’s objective and adds the missing one, running time, so the router balances the two costs instead of ignoring one. A single tunable weight sets how much each of the two metrics, the program’s size and its running time, counts in the balance. It cuts a compiled program’s number of cycles by about 20% on average compared with the SABRE version that Qiskit ships. A standard reliability model shows the trade-off pays off despite the added operations, the gain holds as circuits grow to tens of qubits, and on a real 156-qubit IBM processor it raises the measured circuit fidelity by about 2.5×. The same idea, applied to a very different reinforcement-learning router, helps it in the same way, providing evidence that the benefit belongs to the objective rather than to SABRE itself. ...
We show this proxy gap is real and then close it. SABRE-MS keeps SABRE’s objective and adds the missing one, running time, so the router balances the two costs instead of ignoring one. A single tunable weight sets how much each of the two metrics, the program’s size and its running time, counts in the balance. It cuts a compiled program’s number of cycles by about 20% on average compared with the SABRE version that Qiskit ships. A standard reliability model shows the trade-off pays off despite the added operations, the gain holds as circuits grow to tens of qubits, and on a real 156-qubit IBM processor it raises the measured circuit fidelity by about 2.5×. The same idea, applied to a very different reinforcement-learning router, helps it in the same way, providing evidence that the benefit belongs to the objective rather than to SABRE itself. ...
To run a quantum program on real hardware, a compiler must rewrite it so that every interacting pair of qubits is physically adjacent on the chip, which it does by inserting extra SWAP operations. How these SWAPs are chosen determines how reliably the program runs: each one adds gates and lengthens the schedule, and on today’s noisy devices both effects make a wrong answer more likely. Reliability thus depends on two costs at once, the program’s size and its running time, yet the routing pass that quantum compilers deploy, SABRE, optimises only the first: it minimises the number of SWAPs. The SWAP count stands in for size but not for running time, even though running time is the cost that limits reliability most on current hardware. Optimising it alone is a case of a pass tuning a convenient proxy rather than the quantity that ultimately matters.
We show this proxy gap is real and then close it. SABRE-MS keeps SABRE’s objective and adds the missing one, running time, so the router balances the two costs instead of ignoring one. A single tunable weight sets how much each of the two metrics, the program’s size and its running time, counts in the balance. It cuts a compiled program’s number of cycles by about 20% on average compared with the SABRE version that Qiskit ships. A standard reliability model shows the trade-off pays off despite the added operations, the gain holds as circuits grow to tens of qubits, and on a real 156-qubit IBM processor it raises the measured circuit fidelity by about 2.5×. The same idea, applied to a very different reinforcement-learning router, helps it in the same way, providing evidence that the benefit belongs to the objective rather than to SABRE itself.
We show this proxy gap is real and then close it. SABRE-MS keeps SABRE’s objective and adds the missing one, running time, so the router balances the two costs instead of ignoring one. A single tunable weight sets how much each of the two metrics, the program’s size and its running time, counts in the balance. It cuts a compiled program’s number of cycles by about 20% on average compared with the SABRE version that Qiskit ships. A standard reliability model shows the trade-off pays off despite the added operations, the gain holds as circuits grow to tens of qubits, and on a real 156-qubit IBM processor it raises the measured circuit fidelity by about 2.5×. The same idea, applied to a very different reinforcement-learning router, helps it in the same way, providing evidence that the benefit belongs to the objective rather than to SABRE itself.