Structural Conditions for Distributed Quantum Advantage
quant-ph, cs.DC
Submitted: 2026-10-03
Updated: 2026-10-03
Comments: 14 pages of main text + 25 pages of appendices; 18 figures, 7 tables
License: http://creativecommons.org/licenses/by/4.0/
The gist: Circuit cutting runs a quantum computation on processors smaller than the circuit, at the price of classical knitting whose cost grows exponentially with the number of cut gates.
Abstract
Circuit cutting runs a quantum computation on processors smaller than the circuit, at the price of classical knitting whose cost grows exponentially with the number of cut gates. Keeping this cost affordable is necessary but not sufficient for an advantage, since knitting classically easy subcircuits is itself classically easy. We formulate three requirements for distributed quantum advantage under a stated budget: affordable knitting, classical difficulty that survives the cut, and, for variational circuits, resolvable gradients. We prove that under stated assumptions knitting stays affordable when subcircuits grow behind a bounded interface and becomes exponentially expensive when fixed-width subcircuits multiply. We turn the requirements into screening criteria and apply them to eighteen circuit families. We identify finite local-depth circuits with bounded interfaces as a setting in which the requirements can coexist, with hardness established only in the worst case. As a classically verifiable proof of principle, we knit one gate joining two field-perturbed toric-code patches on an IBM Nighthawk processor for up to 142 spins, and confirm that the reconstruction can at least resolve the contact correlation beyond independent execution for up to 98 qubits.
Sources
- Assessing requirements to scale to practical quantum advantage
- Shor's algorithm is possible with as few as 10,000 reconfigurable atomic qubits
- Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes
- Has quantum advantage been achieved?
- Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency
- Entanglement geometry separates circuit cutting, classical hardness, and trainability
- Efficient Circuit Transpilation of Commuting Gates on 2D Grids
- A Quantum Approximate Optimization Algorithm
- Quantum Circuits For Two-Dimensional Isometric Tensor Networks
- Circuit depth versus energy in topologically ordered systems
- Efficient calculation of gradients in classical simulations of variational quantum algorithms
- Entanglement in the stabilizer formalism
- Quantum computing with Qiskit
- Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices
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