Distributed quantum computing combines the computational power of multiple devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations through classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these subcircuits on separate devices and combining their results. However, the number of shots required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in shots. Nevertheless, each teleportation procedure requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. To address this, we propose a novel circuit cutting technique that leverages non-maximally entangled qubit pairs, effectively reducing the cost associated with circuit cutting. By considering the degree of entanglement in the pre-shared qubit pairs, our technique provides a continuum between existing circuit cutting methods and quantum teleportation, adjusting the cost of circuit cutting accordingly.
Distributed quantum computing combines the computational power of multiple devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations through classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these subcircuits on separate devices and combining their results. However, the number of shots required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in shots. Nevertheless, each teleportation procedure requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. To address this, we propose a novel circuit cutting technique that leverages non-maximally entangled qubit pairs, effectively reducing the cost associated with circuit cutting. By considering the degree of entanglement in the pre-shared qubit pairs, our technique provides a continuum between existing circuit cutting methods and quantum teleportation, adjusting the cost of circuit cutting accordingly.
Distributed quantum computing supports combining the computational power of multiple quantum devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations via classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these sub circuits on separate devices and combining their results. However, the number of circuit executions required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in circuit executions. Nevertheless, each teleportation requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. Addressing this, our work explores utilizing non-maximally entangled qubit pairs in wire cutting, a specific form of circuit cutting, to mitigate the associated costs. The cost of this cutting procedure reduces with the increasing degree of entanglement in the pre-shared qubit pairs. We derive the optimal sampling overhead in this context and present a wire cutting technique employing pure non-maximally entangled states that achieves this optimal sampling overhead. Hence, this offers a continuum between existing wire cutting and quantum teleportation.
Abstract Distributed quantum computing leverages multiple quantum devices collectively to perform computations exceeding each device's capabilities. A currently studied technique to enable this distributed approach is wire cutting, which decomposes a quantum circuit into smaller subcircuits by cutting connecting wires. These subcircuits can be executed on distributed devices, and their results are then classically combined to reconstruct the original computation's result. However, wire cutting requires additional circuit executions to preserve result accuracy, with their number growing exponentially with each cut. Thus, minimizing this sampling overhead is crucial for reducing execution time. Employing shared non‐maximally entangled (NME) states between distributed devices reduces this overhead for single wire cuts, approaching ideal teleportation with maximally entangled states. Extending this approach to jointly cutting multiple wires using NME states remained unexplored. This study addresses this gap by investigating the use of NME states for joint wire cuts, aiming to reduce the sampling overhead further. The three main contributions include (i) determining the minimal sampling overhead for this scenario, (ii) analyzing the overhead when using composite NME states constructed from smaller NME states, and (iii) introducing a wire cutting technique that achieves the optimal sampling overhead with pure NME states, advancing toward wire cutting with arbitrary NME states.
Circuit cutting is a novel technique in the field of quantum computing that offers a promising approach for breaking up large quantum circuits into smaller ones that can be executed on smaller quantum computers. Circuit cutting offers a scalable and hybrid computing approach, by combining classical and quantum computers to run large quantum circuits. At the same time, quantum circuits represent the fundamental building blocks of quantum algorithms, and obfuscation techniques are crucial for protecting the sensitive information and intellectual property encoded in these circuits. This paper constitutes the first security analysis of circuit cutting techniques as a means to protect quantum circuits, and not just for cutting bigger quantum circuits to fit on smaller devices. As cloud providers are in control of quantum computers and give access to users, utilizing cloud-based quantum computers for executing quantum circuits involves considerable security risks. This work demonstrates that quantum circuits can be effectively obfuscated by using quantum circuit cutting techniques. If users are worried about an adversary who attempts to reverse-engineer the original quantum algorithm, circuit cutting can be used as a security measure. To further provide configurable obfuscation level, i.e. number of possible circuits that the cloud provider would have to guess, this work proposes a novel dummy-subcircuits technique. This technique introduces dummy subcircuits with additional qubit cut points to create more confusion for the cloud provider and further help to obfuscate the user's original circuits with limited cost.
Currently, quantum researchers are facing the limitations of NISQ (Noisy Intermediate-Scale Quantum) computers, such as limited qubit counts and high sensitivity to external interference. In response to these challenges, two techniques have recently been proposed, among others, to enhance the execution of quantum circuits: circuit cutting and shot-wise distribution. Circuit cutting partitions large quantum circuits into smaller fragments that can be executed on today's NISQ devices. Shot-wise distribution leverages multiple heterogeneous quantum computers by distributing the shots of a single quantum circuit execution across various NISQ devices, improving resiliency and reducing overall execution time, in a flexible and customisable way. In this work, we investigate the synergic effect of a seamless composition of these two techniques into a unified pipeline of our own design, termed Cut&Shoot. This pipeline fragments a single quantum circuit into multiple independent sub-circuits, distributes the shots of each fragment across various quantum computers, and finally merges the results to reconstruct the output for the original circuit. Noisy simulation experiments seem to indicate that circuit cutting effectively reduces error rates, without the need to increase the number of shots performed. Shot-wise distribution, instead, while offering several qualitative advantages, does not significantly affect error rates, with negligible time overhead.
This paper addresses the challenge of scaling quantum computing by employing distributed quantum algorithms across multiple processors. As quantum circuits grow in complexity, efficiently managing the entanglement resources required for communication between quantum processing units (QPUs) becomes increasingly critical. We propose novel methods to optimize entanglement consumption while accounting for network constraints. Our approach leverages graph partitioning techniques to optimize both qubit teleportation and gate teleportation, minimizing the number of Einstein-Podolsky-Rosen (EPR) pairs required to execute general quantum circuits. Additionally, we formulate an integer linear program (ILP) to further reduce entanglement requirements by mapping the logical resources of partitioned circuits to the physical constraints of the underlying quantum network. Finally, we analyze the entanglement cost for implementing the Quantum Fourier Transform (QFT) across multiple QPUs, employing an approach that exploits the circuit's structure to minimize total entanglement consumption. These contributions provide a pathway to scalable and resource-efficient distributed quantum computing.
Quantum computing has shown great potential in addressing traditional computational challenges, but due to its high error rates and noise issues, classical simulation has become an essential tool for verifying its performance. However, the superposition and entanglement properties of quantum systems pose significant challenges for simulation, especially when memory is limited. Although circuit cutting methods can decompose large-scale quantum circuits into smaller computational tasks to reduce computational load, previous research primarily focused on their application to quantum computers, without fully considering their effectiveness in quantum circuit simulation. This study fills that gap by proposing an optimization scheme based on a heuristic cutting algorithm and subcircuit state vector reuse to address memory limitations in simulations. By incorporating global computational cost considerations and an integer programming model, the heuristic method proposed in this paper not only optimizes the cutting process but also combines subcircuit state vector reuse to reduce redundant calculations and memory usage. Experimental results show that compared to current popular circuit cutting methods, the proposed approach significantly improves simulation speed while reducing memory requirements, effectively addressing the challenges in quantum circuit simulation. The overall average speedup achieved 46%.