A deterministic tensor network contraction computes exact probabilities for all of IBM's doped Clifford sampling outputs in 37.3 minutes on 32 H100 nodes.
Sampling hard circuits with verifiably high fidelity
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abstract
Sampling-based proposals are prominent candidates for demonstrating quantum computations beyond the reach of classical supercomputers. However, it has been difficult to combine their complexity-theoretic hardness with two capabilities needed for scalable quantum computing more generally: suppressing hardware errors, and verifying the quantum computation itself. Here we address both issues by introducing structured circuits, which, in addition to provable hardness guarantees, admit an encoding in a quantum code. This allows us to simultaneously reach high fidelities at high circuit depths, and to certify an experimental fidelity via the circuit structure and measurement of code syndromes. The resulting certificate is device dependent, but requires substantially weaker noise assumptions than existing fidelity proxy benchmarks. We demonstrate our proposal with a $70$-qubit, depth-$70$ Clifford circuit doped with $468$ $T$ gates. We use a total of $97$ physical qubits to encode this computation in spacetime codes, effectively suppressing gate error rates by $10\times$ after syndrome post-selection, and yielding a state with a fidelity lower bound of $0.284$ with $95\%$ confidence. Our construction is a systematic method for promoting a stabilizer state to a magic state while keeping an error-detected fidelity certificate.
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2026 1verdicts
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Classical Simulation and Design Frontiers for IBM's Doped Clifford Sampling Experiment
A deterministic tensor network contraction computes exact probabilities for all of IBM's doped Clifford sampling outputs in 37.3 minutes on 32 H100 nodes.