Circuit cutting was originally designed to retrieve the expectation value of an observable with respect to a large quantum circuit by executing smaller circuit fragments. In this work, however, we demonstrate the application of circuit cutting to a pure sampling task. In particular, we sample solutions to an optimization problem from a trained QAOA circuit. Here, circuit cutting leads to a broadening and shift of the bitstring distribution towards suboptimal values compared to the uncut case. To reduce this effect, we minimize the number of required cuts via integer programming methods. On the other hand, cutting reduces the circuit size and thus the impact of noise. Our experiments on quantum hardware reveal that, for large circuits, the effect of noise reduction outweighs the derogative effects on the bitstring distribution. The study therefore provides evidence that circuit cutting combined with optimized cutting schemes can both scale problem size and mitigate noise for near-term quantum optimization.
Circuit cutting was originally designed to retrieve the expectation value of an observable with respect to a large quantum circuit by executing smaller circuit fragments. In this work, however, we demonstrate the application of circuit cutting to a pure sampling task. In particular, we sample solutions to an optimization problem from a trained QAOA circuit. Here, circuit cutting leads to a broadening and shift of the bitstring distribution towards suboptimal values compared to the uncut case. To reduce this effect, we minimize the number of required cuts via integer programming methods. On the other hand, cutting reduces the circuit size and thus the impact of noise. Our experiments on quantum hardware reveal that, for large circuits, the effect of noise reduction outweighs the derogative effects on the bitstring distribution. The study therefore provides evidence that circuit cutting combined with optimized cutting schemes can both scale problem size and mitigate noise for near-term quantum optimization.
Abstract Noisy intermediate-scale quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times. An approach addressing these problems is quantum circuit cutting. It decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits with additional classical postprocessing. Since these smaller quantum circuits require fewer qubits and gates, they are more suitable for NISQ devices. To investigate the effect of quantum circuit cutting in a quantum algorithm targeting NISQ devices, we design two experiments using the quantum approximate optimization algorithm (QAOA) for the Maximum Cut (MaxCut) problem and conduct them on state-of-the-art superconducting devices. Our first experiment studies the influence of circuit cutting on the objective function of QAOA, and the second evaluates the quality of results obtained by the whole algorithm with circuit cutting. The results show that circuit cutting can reduce the effects of noise in QAOA, and therefore, the algorithm yields better solutions on NISQ devices.
Quantum Approximate Optimization Algorithms (QAOA) have demonstrated a strong potential in addressing graph-based optimization problems. However, the execution of large-scale quantum circuits remains constrained by the limitations of current quantum hardware. In this work, we introduce QuantCut, an automatic framework for circuit cutting that enables efficient execution of large quantum circuits by decomposing entangling two-qubit gates into manageable sub-circuits. Specifically, we focus on gate-cutting techniques. We apply QuantCut to a 71-qubit QAOA circuit ansatz for portfolio diversification in the S&P 500 stock market, aiming to maximize asset diversification. Our approach iteratively optimizes the expectation value while leveraging circuit-cutting strategies to reduce the qubit register size. To validate our framework, we first conduct experiments on a toy model using quantum noise simulations for the Max-Cut problem, analyzing performance improvements with an increasing number of layers. Subsequently, we extend our methodology to a real-world financial optimization scenario, showing competitive results. The results suggest that QuantCut effectively facilitates large-scale quantum computations with circuit-cutting technologies.
Near-term hardware is constrained by high error rates, small qubit counts, and relatively low output fidelity, making the execution of large, high performance quantum circuits difficult. Circuit partitioning (or circuit cutting) has emerged as a promising approach to circumvent these limitations by decomposing circuits into smaller subcircuits at two-qubit interaction points. However, it remains unclear which classes of circuits benefit the most from partitioning and under what hardware conditions it is most effective. In this work, we evaluate the suitability of quantum circuits for partitioning from three perspectives: improving fidelity, enabling distributed execution, and scaling to larger circuit sizes. Specifically, we compare uncut circuit execution against two circuit partitioning approaches: Qiskit's automatic cut finding technique and a custom performance optimized circuit cutting method. We also measure these across GHZ, QFT, brickwork, and random quantum circuits ranging from 4 to 14 qubits, using mean absolute error of expectation values and overall output fidelity. Our results show that partitioning benefits larger, highly interconnected circuits, with our custom method reducing error by up to 55\% and improve fidelity for GHZ circuits, but degrading performance for brickwork circuits at larger scales.
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.
The present era of quantum processors with hundreds to thousands of noisy qubits has sparked interest in understanding the computational power of these devices and how to leverage it to solve practically relevant problems. For applications that require estimating expectation values of observables the community developed a good understanding of how to simulate them classically and denoise them. Certain applications, like combinatorial optimization, however demand more than expectation values: the bit-strings themselves encode the candidate solutions. While recent impossibility and threshold results indicate that noisy samples alone rarely beat classical heuristics, we still lack classical methods to replicate those noisy samples beyond the setting of random quantum circuits.Focusing on problems whose objective depends only on two-body correlations such as Max-Cut, we show that Gaussian randomized rounding in the spirit of Goemans-Williamson applied to the circuit's two-qubit marginals produces a distribution whose expected cost is provably close to that of the noisy quantum device. For instance, for Max-Cut problems we show that for any depth-D circuit affected by local depolarizing noise p, our sampler achieves a recovery ratio 1 − O [ ( 1 − p ) D ] , giving ways to efficiently sample from a distribution that behaves similarly to the noisy circuit for the problem at hand. Beyond theory we run large-scale simulations and experiments on IBMQ hardware, confirming that the rounded samples faithfully reproduce the full energy distribution, and we show similar behaviour under other various noise models. Our results supply a simple classical surrogate for sampling noisy optimization circuits, clarify the realistic power of near-term hardware for combinatorial tasks, and provide a quantitative benchmark for future error-mitigated or fault-tolerant demonstrations of quantum advantage.
Significant efforts are being spent on building a quantum computer. At the same time, developments in quantum software are rapidly progressing. Insufficient quantum resources often are the problem when running quantum algorithms. New techniques can aid in using smaller quantum computers to run larger quantum algorithms. One of these techniques is circuit cutting. With this method, a circuit is broken into multiple pieces, each of which is run on quantum hardware independently and then recombined to obtain the overall answer. These circuit cutting techniques require additional circuit evaluations, which can form a bottleneck for algorithms requiring many interactions. This work explores the potential of circuit cutting techniques precisely in this regime of many interactions. We consider two different models, a standard method based on expectation values, and a novel method based on probability distribution reconstruction. Next, we compare two different training methods we call train-then-cut and cut-then-train and show that in practice, cut-then-train still produces good results. This observation brings closer the practical applicability of circuit cutting techniques, as a train-then-cut strategy is often infeasible. We conclude by implementing a cut and uncut circuit and find that circuit cutting helps achieve higher fidelity results.