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IEEE International Conference on Quantum Computing and Engineering (QCE)

Automated Cut Finding and Circuit Knitting on Large Quantum Circuits

Ibrahim Shehzad, Edwin Pednault, James R. Garrison, Caleb Johnson, Bryce Fuller, Jennifer R. Glick

Published Sep 15, 2024• 1 citation
Publisher

Abstract

Circuit knitting (see, e.g., [1]) is a technique that allows circuits that are too large to run on a given quantum processing unit (QPU) to be decomposed into an equivalent series of smaller subcircuits that can run on the QPU, where the resulting measurement outcomes of the subcircuits are then classically combined via post-processing. Circuits are decomposed by cutting gates and/or wires. To obtain results to the same accuracy as those of the original problem, each cut incurs additional sampling overhead (denoted γ2) that grows exponentially with the number of cuts. It is therefore desirable to find optimal sets of cuts that minimize the sampling overhead. To this end, we present the “cut-finder” (Sec. I) module of the Circuit Knitting Toolbox (CKT) [2], which performs an automated search for LO gate and wire cuts that minimize the sampling overhead. Circuit knitting can also be used to engineer gates between distant qubits which would otherwise require a large swap overhead to implement. In Sec II, we show how this capability can be used to calculate certain expectation values on a graph state with a cylindrical topology to a better degree of accuracy than if the experiment were to be performed without circuit knitting.

DOI
10.1109/qce60285.2024.10328

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Circuit knitting (see, e.g., [1]) is a technique that allows circuits that are too large to run on a given quantum processing unit (QPU) to be decomposed into an equivalent series of smaller subcircuits that can run on the QPU, where the resulting measurement outcomes of the subcircuits are then classically combined via post-processing. Circuits are decomposed by cutting gates and/or wires. To obtain results to the same accuracy as those of the original problem, each cut incurs additional sampling overhead (denoted γ2) that grows exponentially with the number of cuts. It is therefore desirable to find optimal sets of cuts that minimize the sampling overhead. To this end, we present the “cut-finder” (Sec. I) module of the Circuit Knitting Toolbox (CKT) [2], which performs an automated search for LO gate and wire cuts that minimize the sampling overhead. Circuit knitting can also be used to engineer gates between distant qubits which would otherwise require a large swap overhead to implement. In Sec II, we show how this capability can be used to calculate certain expectation values on a graph state with a cylindrical topology to a better degree of accuracy than if the experiment were to be performed without circuit knitting.
Circuit knitting is the process of partitioning large quantum circuits into smaller subcircuits such that the result of the original circuits can be deduced by only running the subcircuits. Such techniques will be crucial for near-term and early fault-tolerant quantum computers, as the limited number of qubits is likely to be a major bottleneck for demonstrating quantum advantage. One typically distinguishes between gate cuts and wire cuts when partitioning a circuit. The cost for any circuit knitting approach scales exponentially in the number of cuts. One possibility to realize a cut is via the quasiprobability simulation technique. In fact, we argue that all existing rigorous circuit knitting techniques can be understood in this framework. Furthermore, we characterize the optimal overhead for wire cuts where the subcircuits can exchange classical information or not. We show that the optimal cost for cuttingnwires without and with classical communication between the subcircuits scales asO(16n) andO(4n), respectively.
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.
Distributing quantum workloads over many Quantum Processing Units (QPUs) is a crucial step in scaling up quantum computers toward practical quantum advantage due to the limitations in size of a single QPU. In the absence of high-fidelity quantum interconnects, circuit knitting could provide a path to computing certain properties of large quantum systems on many QPUs of limited size in a distributed fashion using only classical communication. Circuit knitting partitions large quantum circuits into manageable sub-circuits, however, reconstructing observables in a straightforward manner comes at an exponential cost in sampling and classical post-processing. To mitigate the overhead this technique incurs, we introduce an Adaptive Circuit Knitting (ACK) method that finds efficient partitions of quantum circuits by discovering regions of minimal entanglement between subsystems. We simulate 1D and 2D disordered mixed-field Ising models up to 60 qubits and show that the ACK approach can reduce circuit knitting sampling overheads by up to four orders of magnitude for observables of interest. We highlight our parallel GPU-accelerated implementation and discuss the need for efficient classical simulators to enable distributed quantum algorithm development. Our techniques could enable efficient distribution of quantum simulation for both near-term and fault-tolerant architectures.
Circuit knitting emerges as a promising technique to overcome the limitation of the few physical qubits in near-term quantum hardware by cutting large quantum circuits into smaller subcircuits. Recent research in this area has been primarily oriented towards reducing subcircuit sampling overhead. Unfortunately, these works neglect hardware information during circuit cutting, thus posing significant challenges to the follow on stages. In fact, direct compilation and execution of these partitioned subcircuits yields low-fidelity results, highlighting the need for a more holistic optimization strategy.
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.
Circuit cutting is a technique for simulating large quantum circuits by partitioning them into smaller subcircuits, which can be executed on smaller quantum devices. The results from these subcircuits are then combined in classical postprocessing to accurately reconstruct the expectation value of the original circuit. Circuit cutting introduces a sampling overhead that grows exponentially with the number of gates and qubit wires that are cut. Many recently developed quasiprobabilistic circuit cutting techniques leverage classical side information, obtained from intermediate measurements within the subcircuits, to enhance the postprocessing step. In this work, we provide a formalization of general circuit cutting techniques utilizing side information through quantum instruments. With this framework, we analyze the advantage that classical side information provides in reducing the sampling overhead of circuit cutting. Surprisingly, we find that in certain scenarios, side information does not yield any reduction in sampling overhead, whereas in others it is essential for circuit cutting to be feasible at all. Furthermore, we present a lower bound for the optimal sampling overhead with side information that can be evaluated efficiently via semidefinite programming and improves on all previously known lower bounds.