Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2312.09679 · Physical Review A

Optimal joint cutting of two-qubit rotation gates

Christian Ufrecht, Laura S. Herzog, Daniel D. Scherer, Maniraman Periyasamy, Sebastian Rietsch, Axel Plinge, Christopher Mutschler

Published May 30, 2024• 16 citations
arXivHTMLPDF

Abstract

Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here, we introduce a scheme for joint cutting of two-qubit rotation gates based on a virtual gate-teleportation protocol. With that, we significantly lower the previous upper bounds on the sampling overhead and prove the optimality of the scheme. Furthermore, we show that no classical communication between the circuit partitions is required. For parallel two-qubit rotation gates we derive an optimal ancilla-free decomposition, which includes cnot gates as a special case.

DOI
10.1103/physreva.109.052440

Related papers

Cutting multi-control quantum gates with ZX calculus

Christian Ufrecht, Maniraman Periyasamy, Sebastian Rietsch +3 more

Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-intermediate scale quantum (NISQ) era. While previous work focused on cutting qubit wires or two-qubit gates, in this work we introduce a method for cutting multi-controlled Z gates. We construct a decomposition and prove the upper bound O(62K) on the associated sampling overhead, where K is the number of cuts in the circuit. This bound is independent of the number of control qubits but can be further reduced to O(4.52K) for the special case of CCZ gates. Furthermore, we evaluate our proposal on IBM hardware and experimentally show noise resilience due to the strong reduction of CNOT gates in the cut circuits.

arXiv:2302.00387•Quantum•Oct 23, 2023•29 citations

Decomposition of Multi-Qubit Gates for Circuit Cutting

Ryota Tamura, Tomoya Kashimata, Yohei Hamakawa +2 more

A large-scale quantum circuit can be partitioned into multiple subcircuits through circuit cutting, where each subcircuit is executed multiple times and the expectation value of the original circuit is reconstructed by classical post-processing from their measurement (sampling) results. In this process, appropriate cut locations are identified after the user-designed quantum circuit, including multi-qubit gates that act on three or more qubits, has been decomposed into single-qubit gates and two-qubit gates such as the CNOT gate. Here, we present a method for reducing the sampling overhead, which refers to the increase in the number of samples required due to the cutting process, by modifying the decomposition strategy of multi-qubit gates. Using MCX and CCCX gates as representatives of multi-qubit gates, we demonstrate that the proposed decomposition method, which introduces a small number of ancilla qubits according to the identified cut locations, effectively decreases the sampling overhead.

quant-pharXiv:2603.26278•Mar 30, 2026•0 citations

Scalable Circuit Cutting: A Framework for Combined Gate and Wire Cuts Using Gate Groups

Fiona Jiali Fröhler, Yannick Stade, Christian Ufrecht +2 more

Quantum circuit cutting enables the execution of large circuits on devices with a limited number of qubits by partitioning circuits into independent subcircuits. However, this introduces a sampling overhead, which grows exponentially with the number of cuts, rendering the choice of cut placements critical for practical circuit cutting. Determining optimal cut placements remains computationally challenging, particularly as circuits grow in size. Additionally, existing circuit cutting approaches typically treat gate and wire cuts independently. Those combining both cutting approaches, however, do not take advantage of joint cutting, i.e., identifying common gate groups and cutting them jointly for a reduced overhead. This work presents a unified framework that combines gate and wire cutting within a single partitioning strategy, enabling more efficient circuit decompositions. Moreover, our approach incorporates joint cutting via a novel gate grouping technique, further reducing sampling overhead. By formulating the cut placement problem as a scalable graph partitioning task, our method efficiently identifies near-optimal cut placements for large circuits, also providing diagnostic feedback on whether circuits are suitable for cutting.

quant-pharXiv:2608.05287•Aug 7, 2026•0 citations

Cutting circuits with multiple two-qubit unitaries

Lukas Schmitt, Christophe Piveteau, David Sutter

Quasiprobabilistic cutting techniques allow us to partition large quantum circuits into smaller subcircuits by replacing non-local gates with probabilistic mixtures of local gates. The cost of this method is a sampling overhead that scales exponentially in the number of cuts. It is crucial to determine the minimal cost for gate cutting and to understand whether allowing for classical communication between subcircuits can improve the sampling overhead. In this work, we derive a closed formula for the optimal sampling overhead for cutting an arbitrary number of two-qubit unitaries and provide the corresponding decomposition. We find that cutting several arbitrary two-qubit unitaries together is cheaper than cutting them individually and classical communication does not give any advantage.

arXiv:2312.11638•Quantum•Feb 18, 2025•12 citations

Optimal Two-Qubit Gate-Cutting Cost and Measures of Nonlocality

Michael Hart

Circuit cutting enables quantum computations to be decomposed into smaller subcircuits at the cost of additional sampling overhead. Gate-cutting provides one such approach by replacing nonlocal gates with quasiprobability decompositions of local operations. Starting from the known optimal two-qubit gate-cutting formula, we derive complete sharp lower and upper envelopes relating the quasiprobability extent $γ$ to five established descriptors: entangling power, gate typicality, operator entanglement, Schmidt strength, and maximum product-input concurrence. We recast $γ$ as a Rényi-$1/2$ functional of the operator-Schmidt spectrum and show how the physical constraints on two-qubit spectra sharpen generic entropy bounds and select a small set of recurring extremal Cartan families. None of the five descriptors generically determines $γ$ alone, but each imposes exact constraints, revealing how substantially the optimal gate-cutting cost can vary between gates with similar nonlocal characteristics.

quant-pharXiv:2609.30141•Sep 25, 2026•0 citations

Optimal Partitioning of Quantum Circuits Using Gate Cuts and Wire Cuts

Sebastian Brandhofer, Ilia Polian, Kevin Krsulich

A limited number of qubits, high error rates, and limited qubit connectivity are major challenges for effective near-term quantum computations. Quantum circuit partitioning divides a quantum computation into classical postprocessing steps and a set of smaller-scale quantum computations that individually require fewer qubits, lower qubit connectivity, and typically incur less error. However, as partitioning generally increases the duration of a quantum computation exponentially in the required partitioning effort, it is crucial to select optimal partitioning points, so-called cuts, and to use optimal cut realizations. In this work, we develop the first optimal partitioning method relying on quantum circuit knitting for optimal cut realizations and an optimal selection of wire cuts and gate cuts that trades off ancilla qubit insertions for a decrease in quantum computing time. Using this combination, the developed method demonstrates a reduction in quantum computing runtime by 41% on average compared to previous quantum circuit partitioning methods. Furthermore, the qubit requirement of the evaluated quantum circuits was reduced by 40% on average for a runtime budget of one hour and a sampling frequency of 1 kHz. These results highlight the optimality gap of previous quantum circuit partitioning methods and the possible extension in the computational reach of near-term quantum computers.

arXiv:2308.09567•IEEE Transactions on Quantum Engineering•Dec 26, 2023•30 citations
Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here, we introduce a scheme for joint cutting of two-qubit rotation gates based on a virtual gate-teleportation protocol. With that, we significantly lower the previous upper bounds on the sampling overhead and prove the optimality of the scheme. Furthermore, we show that no classical communication between the circuit partitions is required. For parallel two-qubit rotation gates we derive an optimal ancilla-free decomposition, which includes cnot gates as a special case.
Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-intermediate scale quantum (NISQ) era. While previous work focused on cutting qubit wires or two-qubit gates, in this work we introduce a method for cutting multi-controlled Z gates. We construct a decomposition and prove the upper bound O(62K) on the associated sampling overhead, where K is the number of cuts in the circuit. This bound is independent of the number of control qubits but can be further reduced to O(4.52K) for the special case of CCZ gates. Furthermore, we evaluate our proposal on IBM hardware and experimentally show noise resilience due to the strong reduction of CNOT gates in the cut circuits.
A large-scale quantum circuit can be partitioned into multiple subcircuits through circuit cutting, where each subcircuit is executed multiple times and the expectation value of the original circuit is reconstructed by classical post-processing from their measurement (sampling) results. In this process, appropriate cut locations are identified after the user-designed quantum circuit, including multi-qubit gates that act on three or more qubits, has been decomposed into single-qubit gates and two-qubit gates such as the CNOT gate. Here, we present a method for reducing the sampling overhead, which refers to the increase in the number of samples required due to the cutting process, by modifying the decomposition strategy of multi-qubit gates. Using MCX and CCCX gates as representatives of multi-qubit gates, we demonstrate that the proposed decomposition method, which introduces a small number of ancilla qubits according to the identified cut locations, effectively decreases the sampling overhead.
Quantum circuit cutting enables the execution of large circuits on devices with a limited number of qubits by partitioning circuits into independent subcircuits. However, this introduces a sampling overhead, which grows exponentially with the number of cuts, rendering the choice of cut placements critical for practical circuit cutting. Determining optimal cut placements remains computationally challenging, particularly as circuits grow in size. Additionally, existing circuit cutting approaches typically treat gate and wire cuts independently. Those combining both cutting approaches, however, do not take advantage of joint cutting, i.e., identifying common gate groups and cutting them jointly for a reduced overhead. This work presents a unified framework that combines gate and wire cutting within a single partitioning strategy, enabling more efficient circuit decompositions. Moreover, our approach incorporates joint cutting via a novel gate grouping technique, further reducing sampling overhead. By formulating the cut placement problem as a scalable graph partitioning task, our method efficiently identifies near-optimal cut placements for large circuits, also providing diagnostic feedback on whether circuits are suitable for cutting.
Quasiprobabilistic cutting techniques allow us to partition large quantum circuits into smaller subcircuits by replacing non-local gates with probabilistic mixtures of local gates. The cost of this method is a sampling overhead that scales exponentially in the number of cuts. It is crucial to determine the minimal cost for gate cutting and to understand whether allowing for classical communication between subcircuits can improve the sampling overhead. In this work, we derive a closed formula for the optimal sampling overhead for cutting an arbitrary number of two-qubit unitaries and provide the corresponding decomposition. We find that cutting several arbitrary two-qubit unitaries together is cheaper than cutting them individually and classical communication does not give any advantage.
Circuit cutting enables quantum computations to be decomposed into smaller subcircuits at the cost of additional sampling overhead. Gate-cutting provides one such approach by replacing nonlocal gates with quasiprobability decompositions of local operations. Starting from the known optimal two-qubit gate-cutting formula, we derive complete sharp lower and upper envelopes relating the quasiprobability extent γγγ to five established descriptors: entangling power, gate typicality, operator entanglement, Schmidt strength, and maximum product-input concurrence. We recast γγγ as a Rényi-1/21/21/2 functional of the operator-Schmidt spectrum and show how the physical constraints on two-qubit spectra sharpen generic entropy bounds and select a small set of recurring extremal Cartan families. None of the five descriptors generically determines γγγ alone, but each imposes exact constraints, revealing how substantially the optimal gate-cutting cost can vary between gates with similar nonlocal characteristics.
A limited number of qubits, high error rates, and limited qubit connectivity are major challenges for effective near-term quantum computations. Quantum circuit partitioning divides a quantum computation into classical postprocessing steps and a set of smaller-scale quantum computations that individually require fewer qubits, lower qubit connectivity, and typically incur less error. However, as partitioning generally increases the duration of a quantum computation exponentially in the required partitioning effort, it is crucial to select optimal partitioning points, so-called cuts, and to use optimal cut realizations. In this work, we develop the first optimal partitioning method relying on quantum circuit knitting for optimal cut realizations and an optimal selection of wire cuts and gate cuts that trades off ancilla qubit insertions for a decrease in quantum computing time. Using this combination, the developed method demonstrates a reduction in quantum computing runtime by 41% on average compared to previous quantum circuit partitioning methods. Furthermore, the qubit requirement of the evaluated quantum circuits was reduced by 40% on average for a runtime budget of one hour and a sampling frequency of 1 kHz. These results highlight the optimality gap of previous quantum circuit partitioning methods and the possible extension in the computational reach of near-term quantum computers.