Cutting soft materials is a complex process governed by the interplay of bulk large deformation, interfacial soft fracture, and contact forces with the cutting tool. Existing experimental characterizations and numerical models often fail to capture the variety of observed cutting behaviors, especially the transition from indentation to cutting and the roles of dissipative mechanisms. Here, we combine novel experimental cutting tests on three representative materials-a soft hydrogel, elastomer, and food-based materials-with a coupled computational model that integrates soft fracture, adhesion, and frictional interactions. Our experiments reveal material-dependent cutting behaviors, with abrupt or smooth transitions from indentation to crack initiation, followed by distinct steady cutting regimes. The computational model captures these behaviors and shows that adhesion and viscous cohesive forces dominate tangential stresses, while Coulomb friction plays a negligible role due to low contact pressures. Together, these results provide new mechanistic insights into the physics of soft cutting and offer a unified framework to guide the design of soft materials, cutting tools, and cutting
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 post-processing 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 post-processing 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 wi
In the development of cooking robots, mastering the task of cutting is crucial. A significant challenge lies in the diverse properties of food, which necessitate distinct cutting policies and even different knives for optimal processing. This paper presents a perception-manipulation framework for food-cutting tasks. Our system features a knife selection module that utilizes force data from a preliminary fixed trial cut to select the appropriate knife for the given food. This is followed by an adaptive cutting phase using reinforcement learning (RL) to balance cutting speed and energy efficiency. In our experiments, the knife selection module achieved 100% successful rate on unseen food, and we compared the performances of fixed policy, RL policy, with human operators. Our method not only achieves high performance but also demonstrates comparable results to those of human participants.
Circuit cutting allows quantum circuits larger than the available hardware to be executed. Cutting techniques split circuits into smaller subcircuits, run them on the hardware, and recombine results through classical post-processing. Circuit cutting techniques have been extensively researched over the last five years and it been adopted by major quantum hardware vendors as part of their scaling roadmaps. We examine whether current circuit cutting techniques are practical for orchestrating executions on fault-tolerant quantum computers. We conduct a resource estimation-based benchmarking of important quantum applications and different types of circuit cutting techniques. Our applications include practically relevant algorithms, such as Hamiltonian simulation, kernels such as quantum Fourier transform and more. To cut these applications, we use IBM's Qiskit cutting tool. We estimate resources for subcircuits using Microsoft's Azure Quantum Resource Estimator and develop models to determine the qubit, quantum and classical runtime needs of circuit cutting. We demonstrate that while circuit cutting works for small-scale systems, the exponential growth of the quantum runtime and the cla
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.
Laser cutting is an old and multi-physical process that was quickly adopted by the metallurgical industry. However, this fast industrialisation has had a significant impact on quality control. Several studies have been carried out to characterise and minimise different types of cutting defects. Reviews published between 2008 and 2022 highlight that research often focuses on single-criterion ___quality' approaches, aiming to minimise specific defects such as the Heat-Affected Zone, surface roughness, or kerf geometry. Consequently, efforts have been directed at optimising specific aspects of quality rather than adopting a complete approach. Furthermore, these reviews reveal that cutting quality can be enhanced through the careful selection of laser manufacturing parameters and part parameters. However, while parameters such as material and thickness have been investigated, the influence of part morphology on cutting quality remains underexplored.___ Although some studies have examined the effects of material and thickness, part morphology is often limited to simple segments with varying cutting lengths or angles. While other research has investigated the impact of angle size on cutt
Cutting plane methods, particularly outer approximation, are a well-established approach for solving nonlinear discrete optimization problems without relaxing the integrality of decision variables. While powerful in theory, their computational performance can be highly variable. Recent research has shown that constructing cutting planes at the projection of infeasible points onto the feasible set can significantly improve the performance of cutting plane approaches. Motivated by this, we examine whether constructing cuts at feasible points closer to the optimal solution set could further enhance the effectiveness of cutting plane methods. We propose a hybrid method that combines the global convergence guarantees of cutting plane methods with the local exploration capabilities of first-order optimization techniques. Specifically, we use projected gradient methods as a heuristic to identify promising regions of the solution space and generate tighter, more informative cuts. We focus on binary optimization problems with convex differentiable objective functions, where projection operations can be efficiently computed via mixed-integer linear programming. By constructing cuts at points
The automation of key processes in metal cutting would substantially benefit many industries such as manufacturing and metal recycling. We present a vision-based control scheme for automated metal cutting with oxy-fuel torches, an established cutting medium in industry. The system consists of a robot equipped with a cutting torch and an eye-in-hand camera observing the scene behind a tinted visor. We develop a vision-based control algorithm to servo the torch's motion by visually observing its effects on the metal surface. As such, the vision system processes the metal surface's heat pool and computes its associated features, specifically pool convexity and intensity, which are then used for control. The operating conditions of the control problem are defined within which the stability is proven. In addition, metal cutting experiments are performed using a physical 1-DOF robot and oxy-fuel cutting equipment. Our results demonstrate the successful cutting of metal plates across three different plate thicknesses, relying purely on visual information without a priori knowledge of the thicknesses.
This study investigates the ductile-to-brittle transition behavior in elliptical vibration cutting (EVC) of silicon and identifies the practical process window for ductile-regime cutting. EVC has been reported to increase the critical depth of ductile-regime cutting of silicon. This study demonstrates that the enhanced ductile cutting performance, however, is only optimal in a carefully-determined process window. The vibration amplitudes and nominal cutting velocity have significant impacts on the ductile-to-brittle transition behaviors. Systematic experiments covering a wide span of vibration amplitudes and cutting velocity have been conducted to investigate their effects. Two quantitative performance indices, the critical depth and ductile ratio, are utilized to analyze cutting performance by considering two unique characteristics of elliptical vibration cutting, i.e., the time-varying undeformed chip thickness and effective cutting direction angle. The results show that there exists a lower bound for the nominal cutting velocity to ensure the ductile-regime material removal, besides the well-known upper bound. Besides, the increases of vibration amplitudes in both the cutting an
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 subcircuits 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 pr
We design and analyze a deterministic cake cutting algorithm that achieves proportional fairness using a linear number of cuts.
Distributed quantum computing leverages the collective power of multiple quantum devices to perform computations exceeding the capabilities of individual quantum devices. A currently studied technique to enable this distributed approach is wire cutting, which decomposes a quantum circuit into smaller subcircuits by cutting their connecting wires. These subcircuits can then be executed on distributed devices, and their results are 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 the total execution time. Employing shared non-maximally entangled (NME) states between distributed devices reduces this overhead for single wire cuts, moving closer to ideal teleportation with maximally entangled states. Extending this approach to jointly cutting multiple wires using NME states remained unexplored. Our paper addresses this gap by investigating the use of NME states for joint wire cuts, aiming to reduce the sampling overhead further. Our three main contributions in
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.
The numerical simulation of metal cutting processes requires material data for constitutive equations, which cannot be obtained with standard material testing procedures. Instead, inverse identifications of material parameters within numerical simulation models of the cutting experiment itself are necessary. The intention of the present report is the provision of results of a large scale experimental study of dry orthogonal cutting experiments of Ti6Al4V (3.7165 Grade 5) and Ck45 (AISI 1045) along with their documentation and interpretation. The process forces are evaluated and each cutting insert geometry has been measured prior to the experiments to determine the cutting edge radii for each experiment. The resulting chip forms are analysed and the averaged chip thicknesses are determined. A material characterization is performed, which includes microstructural investigations on the raw materials and is reported together with tensile test results. The assembled data set can be used for parameter identification when the experimental conditions are reproduced in numerical simulations. The cutting test results are finally used to derive coefficients for Kienzle's force model. The dat
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
The curved uncut chip thickness model is presented to predict the cutting forces for general uncut chip geometries. The cutting force is assumed to be distributed along a curved path on the rake face of the cutting tool, which makes the solution computable for inserts with nose radius and more complex cutting edge geometries. The curved paths originate from a basic mechanical model (a compressed plate model), which is used to mimic the motion of the chip on the rake face of the tool without performing real cutting simulations. Consequently, actual cutting forces are predicted using orthogonal cutting data and the orthogonal-to-oblique transformations. The solution satisfies the classical observations and assumptions made on the chip formation process, it is mathematically unique, free of inconsistency and computationally effective. Case studies are presented on real cutting tests. The results highlight that cutting force components can be sensitive to modeling assumptions in case of extreme machining parameters.
Generative quantum eigensolver (GQE) is a hybrid quantum-classical algorithm that iteratively trains a classical generative machine learning model such that the model can generate quantum circuits with desired properties such as approximating molecular ground states. It offers as many potential applications and as much flexibility as variational quantum eigensolvers, while avoiding the problem of barren plateaus. Quantum circuit cutting (QCC) is a technique to perform quantum computations that require more qubits than available on single quantum devices. It comes with considerable sampling overhead depending on the structure of the circuit to be cut and how the circuit is cut. To make QCC practical, therefore, the circuits to be cut must be designed such that their execution is meaningful and QCC overhead is kept small. In this work, we extend GQE such that the generative model only produces circuits whose overhead by QCC is upper-bounded, while retaining the original purpose of GQE. Consequently, our proposal not only enhances the applicability of GQE through the use of QCC, but also provides a practical application for QCC. Using a transformer decoder implementation of GQE, we ev
In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose conducting modified Bayesian inference by "cutting feedback". Cutting feedback limits the influence of potentially misspecified modules in posterior inference. We consider two types of cuts. The first limits the influence of a misspecified copula on inference for the marginals, which is a Bayesian analogue of the popular Inference for Margins (IFM) estimator. The second limits the influence of misspecified marginals on inference for the copula parameters by using a pseudo likelihood of the ranks to define the cut model. We establish that if only one of the modules is misspecified, then the appropriate cut posterior gives accurate uncertainty quantification asymptotically for the parameters in the other module. Computation of the cut posteriors is difficult, and new variational inference methods to do so are proposed. The efficacy of the new methodology is demonstrated using both simulated data and a substantive multivariate time series application from macroeconomic forecasting. In the latter, cutting feedback
The article considers solving the problem of precision cutting of honeycomb blocks. The urgency of using arbitrary shapes application cutting from honey-comb blocks made of modern composite materials is substantiated. The problem is to obtain a cut of the given shape from honeycomb blocks. The complexity of this problem is in the irregular pattern of honeycomb blocks and the presence of double edges, which forces an operator to scan each block before cutting. It is necessary to take into account such restrictions as the place and angle of the cut and size of the knife, its angle when cutting and the geometry of cells. For this problem solving, a robotic complex has been developed. It includes a device for scanning the geometry of a honeycomb block, software for cutting automation and a cutting device itself. The software takes into account all restrictions on the choice of the location and angle of the operating mechanism. It helps to obtain the highest quality cut and a cut shape with the best strength characteristics. An actu-ating device has been developed and implemented for both scanning and cutting of honeycomb blocks directly. The necessary tests were carried out on real alu
Using finite element software developed for metal cutting by Third Wave Systems we investigate the forces involved in chatter, a self-sustained oscillation of the cutting tool. The phenomena is decomposed into a vibrating tool cutting a flat surface work piece, and motionless tool cutting a work piece with a wavy surface. While cutting the wavy surface, the shearplane was seen to oscillate in advance of the oscillation of the depth of cut, as were the cutting, thrust, and shear plane forces. The vibrating tool was used to investigate process damping through the interaction of the relief face of the tool and the workpiece. Crushing forces are isolated and compared to the contact length between the tool and workpiece. We found that the wavelength dependence of the forces depended on the relative size of the wavelength to the length of the relief face of the tool. The results indicate that the damping force from crushing will be proportional to the cutting speed for short tools, and inversely proportional for long tools.