How should problem-specific compilation be counted in the Q-CTRL Fermi-Hubbard quantum advantage claim?
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I am trying to understand how to fairly interpret the recent Q-CTRL Fermi-Hubbard practical quantum advantage claim https://arxiv.org/abs/2605.04025.My question is not whether the experiment is impressive. It is. What I am asking is how we should count the problem specific co design that makes the experiment work.The paper's performance seems to rely on several pieces of structure that are not generic black-box compilation: a pair-interleaved Jordan-Wigner ordering, a fermionic fSWAP network, wi
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How should problem-specific compilation be counted in the Q-CTRL Fermi-Hubbard quantum advantage claim?
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I am trying to understand how to fairly interpret the recent Q-CTRL Fermi-Hubbard practical quantum advantage claim https://arxiv.org/abs/2605.04025.My question is not whether the experiment is impressive. It is. What I am asking is how we should count the problem specific co design that makes the experiment work.The paper's performance seems to rely on several pieces of structure that are not generic black-box compilation:
a pair-interleaved Jordan-Wigner ordering,
a fermionic fSWAP network, with fSWAP = SWAP * CZ,
a snake like embedding of the effective fermion line into the heavy-hex device,
calibration aware layout selection and bad-qubit avoidance,
a native circuit structure with very low two-qubit depth,
and a hardware specific error suppression and mitigation stack.
According to the paper this is the right thing to do for this problem. But it also makes the benchmarking question more subtle. Is the claimed advantage mainly an algorithmic advantage, a hardware execution time advantage, or a compiler/hardware/physics co-design advantage? I built a much smaller "poor man's" version to test the basic idea:
https://github.com/BramDo/fermi-hubbard-60q-tdvp
This is not a reproduction of the full paper. It is only:
60 qubits / 30 sites,
8 Trotter steps, dt = 0.2, total time t = 1.6,
IBM hardware,
native fswap,cz circuit family,
local observables: occupation, charge, spin, double occupancy,
comparison against ITensorMPS TDVP at chi = 64.
Even at this smaller scale, the circuit can be run directly on IBM hardware and the speed separation is already easy to see. My local ITensorMPS TDVP chi64 reference took 821.277 seconds on a laptop/desktop CPU setup. The corresponding IBM main job used about 3 jobs of 2 quantum seconds. This is an order 100 QPU-execution-time separation for a simplified version of the same workflow.
The best hardware route in my small run is DD + Pauli twirling + readout-corrected local observables. Against the TDVP chi64 reference I get:
occupation RMSE: 0.02061,
charge RMSE: 0.01471,
spin RMSE: 0.03850.
I am not calling my run quantum advantage. The reference is chi64, not chi4096; the run is 60q/8, not the paper's 120q/30 case; and end-to-end timing including classical preprocessing, calibration search, queueing, mitigation, and post-processing has to be treated carefully.
But the small run makes the main issue concrete: a q qauntum enthousiast can now reproduce a simplified version where the quantum processor executes the relevant circuit much faster than a tensor-network calculation on a local machine, provided the fermionic mapping, fSWAP layout, transpilation choices, and mitigation stack are already packaged correctly.
Some thoughts or questions to think over:
For a practical quantum advantage claim in this setting, should the fSWAP/snake-layout construction be counted as part of the quantum algorithm, part of the compiler, or part of the benchmark setup?
If a Qiskit Function / Fire Opal style service automates some of this, what information must be exposed for the result to be independently verifiable? For example, should a verification package include the exact fermion to qubit ordering, logical to physical layout, bad qubit avoidance.
Main question More important, is this automation even possible for different sort of problems. It seems you need to know the specifics or the circuit to get the best result.
quantum-algorithmshamiltonian-simulationquantum-advantage
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edited May 12 at 17:48
Martin Vesely
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asked May 12 at 13:32
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