SearcharxivSearch

arXiv · 2608.21004

When More Becomes Less: Topology-Reversed Three-Qubit Gate Performance on IBM Quantum Processors

Abstract

The exact Toffoli gate admits a six-CX decomposition, denoted by $CCX_6$, that is optimal under unrestricted two-qubit connectivity. On a linear three-qubit topology, however, $CCX_6$ contains 4 nearest-neighbor CX gates and 2 non-nearest-neighbor CX gates. By contrast, an alternative exact decomposition, denoted by $CCX_8$, uses only 8 nearest-neighbor CX gates. Because CCX is locally equivalent to CCZ, we perform the experiments using the corresponding $\cczs$ and $\cczl$ circuits. We compare these circuits on sampled linear triples of the 156-qubit IBM Quantum Heron processors \texttt{ibm\_fez} and \texttt{ibm\_kingston}. Under the compilation protocol, the nominal $\cczs$ circuit becomes a twelve-CZ implementation, whereas the linear-nearest-neighbor circuit retains eight native CZ gates. Experimentally measured ensemble-feature-selection estimates favor the eight-CZ realization on nearly all retained triples. We test the same ordering by preparing a three-qubit hypergraph state, which probes the coherent conditional phase rather than only computational-basis populations. The measured hypergraph-state infidelity is lower for the $\cczl$ circuit for most triples on both processors. Phase-altered interleaved randomized benchmarking provides a complementary comparison of Clifford surrogates preserving the two compiled entangling structures. Within the scope of the tested circuits and phase-sensitive input state, the results demonstrate that hardware connectivity can reverse the operational ranking of exact decompositions: a circuit with more abstract two-qubit gates can yield the better physical implementation.

Explore related subjects

Keep this discovery

BibTeXRIS

Simona K. Grigorova, Stancho S. Stanchev, Nikolay V. Vitanov. 2026-08-21. When More Becomes Less: Topology-Reversed Three-Qubit Gate Performance on IBM Quantum Processors. https://arxiv.org/abs/2608.21004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Probing the Error-Mitigation Threshold with Matrix Product States

Quantum error mitigation relies on accurate noise characterization, but mismatches between the actual and characterized noise can be amplified and drive a sharp threshold between successful and failed mitigation. In random circuits, this threshold maps onto a random-field Ising transition, but previous exact numerics were limited to small one-dimensional and all-to-all systems, leaving explicit two-dimensional architectures unresolved. We develop a fixed-bond-dimension matrix-product-state method for the replicated transfer dynamics that extends threshold calculations beyond exact propagation while retaining the finite-size signatures of the transition. At system sizes beyond previous exact studies, we recover the predicted absence of a threshold for quenched disorder in 1D, obtain a sharper annealed all-to-all critical point, and resolve architecture-dependent finite-depth thresholds in 2D square and heavy-hex circuits. These results establish replicated tensor-network dynamics as a practical tool for probing error-mitigation thresholds in large and higher-dimensional noisy circuits.

quant-ph

Low-cost algorithm-to-execution framework for surface-code quantum computing

The execution of useful quantum algorithms on fault-tolerant processors requires more than a mapping from logical gates to encoded operations: the spatial organization, non-Clifford resource supply, and execution schedule must also be determined while keeping physical overhead within practical limits. Although the theoretical hierarchy from logical circuits to fault-tolerant operations is well established, these implementation choices are often specified and optimized separately. Here we develop a low-cost algorithm-to-execution framework for surface-code quantum computing. From hierarchical algorithm descriptions, it constructs dependency-preserving logical schedules and an executable workload capturing logical interactions, operation parallelism, and time-resolved non-Clifford demand, thereby linking logical computation to surface-code organization, resource-state preparation, and fault-tolerant execution in a traceable workflow. We apply the framework to twenty benchmark circuits across seven algorithm families and a hierarchically composed application-scale elliptic-curve discrete-logarithm workload. Physical costs vary substantially even for circuits with similar logical resource counts. Under our direct-rotation calibration, non-Clifford implementation selection reduces space-time volume by up to 241.5 times versus an all-synthesis baseline for the QAOA amplitude-amplification workload. Circuit-specific surface-code layouts reduce routed-latency estimates for all twenty benchmarks; thirteen also reduce space-time volume because communication savings outweigh added spatial overhead. These results show that low-cost fault-tolerant execution depends on computation scheduling and organization, not aggregate logical resource counts alone.

quant-ph

Sample-optimal learning of stabilizer states

It is well-known that learning a pure $n$-qubit stabilizer state $|\psi\rangle$ both requires, and can be accomplished with, access to a number of copies of $|\psi\rangle$ linear in $n$. However, the precise constant coefficient of this scaling does not appear to have been determined. Here we prove that $L_\delta(n)$, the smallest number of copies from which a quantum procedure can identify any stabilizer state with failure probability at most $0<\delta<1/8$, satisfies $n+\lceil\log_2(1/\delta)\rceil-3\leq L_\delta(n)\leq n+\left\lceil\log_2(1/\delta)\right\rceil+4$. We present a polynomial-time quantum learning algorithm that saturates this bound, achieving a constant factor improvement in sample-complexity over previously known approaches. As an immediate corollary, we obtain via the Choi-Jamiolkowski isomorphism an algorithm for learning an unknown $n$-qubit Clifford unitary from $2n+\left\lceil\log_2(1/\delta)\right\rceil+4$ queries, the $n$-dependence of which we show to be optimal. Our proof technique, which involves Fourier analysis on the abelian group $\mathbb{Z}_4^n \times \mathbb{F}_2^{n(n-1)/2}$, seems to be qualitatively different to previous approaches to stabilizer state learning, and may be of some independent interest; in particular, it admits natural generalisations to further problems in quantum learning theory.

quant-ph