Today, all the implemented quantum computers are in Noisy Intermediate-Scale Quantum (NISQ) level. In such quantum computers, when circuit length and size increase, the results become less reliable because of the increasing effect of noise. The noise is an important factor that should be handled in NISQ level quantum computers. In this study, we investigate the noise factor on 5 qubit IBM-Q computers for basic circuits. For this purpose, existing 5 qubit IBM-Q computers with different architectures are examined. Then quantum circuit equivalents corresponding to basic logic gates such as XOR, AND, and OR are presented. Quantum circuits created for XOR, AND, and OR are run 10 times on different quantum computers of 5 qubits named Santiago, Athens, Valencia, Vigo, Ourance, and IBMQX2. Statistical information such as std., mean is obtained from the results. The consistency of the results obtained and their difference from the optimum result are discussed. As a result of the study, there is no contradiction in the results obtained with quantum volume. Studies show that even small growths in XOR, AND, and OR circuits can lead to a big deflection in results.
Today, all the implemented quantum computers are in Noisy Intermediate-Scale Quantum (NISQ) level. In such quantum computers, when circuit length and size increase, the results become less reliable because of the increasing effect of noise. The noise is an important factor that should be handled in NISQ level quantum computers. In this study, we investigate the noise factor on 5 qubit IBM-Q computers for basic circuits. For this purpose, existing 5 qubit IBM-Q computers with different architectures are examined. Then quantum circuit equivalents corresponding to basic logic gates such as XOR, AND, and OR are presented. Quantum circuits created for XOR, AND, and OR are run 10 times on different quantum computers of 5 qubits named Santiago, Athens, Valencia, Vigo, Ourance, and IBMQX2. Statistical information such as std., mean is obtained from the results. The consistency of the results obtained and their difference from the optimum result are discussed. As a result of the study, there is no contradiction in the results obtained with quantum volume. Studies show that even small growths in XOR, AND, and OR circuits can lead to a big deflection in results.
Recent developments in quantum computers have been pushing up the number of qubits. However, the state-of-the-art Noisy Intermediate Scale Quantum (NISQ) computers still do not have enough qubits to accommodate the error correction circuit. Noise in quantum gates limits the reliability of quantum circuits. To characterize the noise effects, prior methods such as process tomography, gateset tomography and randomized benchmarking have been proposed. However, the challenge is that these methods do not scale well with the number of qubits. Noise models based on the understanding of underneath physics have also been proposed to study different kinds of noise in quantum computers. The difficulty is that there is no widely accepted noise model that incorporates all different kinds of errors. The realworld errors can be very complicated and it remains an active area of research to produce accurate noise models. In this paper, instead of using noise models to estimate the reliability, which is measured with success rates or inference strength, we treat the NISQ quantum computer as a black box. We use several quantum circuit characteristics such as the number of qubits, circuit depth, the number of CNOT gates, and the connection topology of the quantum computer as inputs to the black box and derive a reliability estimation model using (1) polynomial fitting and (2) a shallow neural network. We propose randomized benchmarks with random numbers of qubits and basic gates to generate a large data set for neural network training. We show that the estimated reliability from our black-box model outperforms the noise models from Qiskit. We also showcase that our black-box model can be used to guide quantum circuit optimization at compile time.
Quantum algorithms offer efficient solutions to computational problems that are expensive to solve classically. Publicly available quantum computers, such as those provided by IBM, can now be used to run small quantum circuits that execute quantum algorithms. However, these quantum computers are highly prone to noise. Here, we introduce important concepts of quantum circuit noise and connectivity that must be addressed to obtain reliable results on quantum computers. We utilize several examples to show how noise scales with circuit depth. We present Simon's algorithm, a quantum algorithm for solving a computational problem of the same name, explain how to implement it in IBM's Qiskit platform, and compare the results of running it both on a noiseless simulator and on physical hardware subject to noise. We discuss the impact of Qiskit's transpiler, which adapts ideal quantum circuits for physical hardware with limited connectivity between qubits. We show that even circuits of only a few qubits can have their success rate significantly reduced by quantum noise unless specific measures are taken to minimize its impact.
Abstract With the development in the field of quantum physics, several methods for building a quantum computer have emerged. These differ in qubit technologies, interaction topologies, and noise characteristics. In this article, insights are given into the circuit‐centric architecture design of Noisy Intermediate‐Scale Quantum (NISQ) devices. The dependence of the circuit size, circuit depth on the interaction and connection between different qubits present in quantum hardware are discussed. A noise‐aware procedure is presented which helps in determining the optimal interactions between different qubits of a quantum chip to execute a given circuit in the most efficient way possible. In this article, the 5‐qubit hardware in a noiseless setting is illustrated with an example. Also, a benchmark‐driven analysis is performed to show the importance of noise adaptivity in determining the hardware reliability. It is concluded that a generalized and flexible procedure such as this approach can aid in determining the design of hardware accurately for which the circuit runs efficiently, that is, with the least number of clock cycles, the lowest gate operations, and noise‐based errors.
Noisy intermediate-scale quantum (NISQ) computers face significant reliability challenges because they are vulnerable to quantum noise, which severely limits their fidelity in quantum applications. In particular, deep circuits with a large number of quantum gates are susceptible to errors as they are more likely to lose their states in a deep circuit. In this paper, we propose a quantum circuit for square root operation that generates correct results on NISQ devices. The square root operation is used in many applications such as complex number computations, computer graphics, etc. While there have been limited studies on quantum square root circuits, none of them can be implemented on NISQ devices. In this work, we simplify the structure of an exact quantum square root circuit and reduce the number of quantum gates. The proposed design reduces the complexity of the square root circuit while maintaining the same level of precision in outputs. In addition, we exploit approximate computing to simplify the circuit further and run it on a real quantum computer. Approximate computing is used in classical computers to enhance the power and/or performance in exchange for accuracy. We exploit approximate computing for a different purpose, which is reducing the depth as well as the number of quantum gates in the square root circuit. To validate the effectiveness of our approach, we conduct experiments on an IBM quantum computer, where our circuit produces meaningful results. Furthermore, we present examples of error-resilient applications to demonstrate the validity of our approximate circuit.
In recent years, significant progress has been made in building quantum computers by several companies. Despite the progress, these noisy intermediate-scale quantum (NISQ) computers still suffer from several noises and errors such as measurement errors, multi-qubit gate errors, and worse, short decoherence times. Though quantum computer vendors are releasing better quantum computers in terms of Quantum Volume, the quantum device still remains far from quantum supremacy in practical problems. The Quantum Approximate Optimization Algorithm (QAOA) was suggested to potentially demonstrate a computational advantage in combinatorial optimization problems on NISQ computers. In this paper, we optimize the QAOA circuits and to scale the problem size on IBM quantum processors. In addition, we study the effect of the length of the QAOA ansatz on IBM quantum processors and discuss optimal implementation methods for scalable QAOA. We test our implementations on the MaxCut problems.
In recent years, Quantum Computing (QC) has progressed to the point where small working prototypes are available for use. Termed Noisy Intermediate-Scale Quantum (NISQ) computers, these prototypes are too small for large benchmarks or even for Quantum Error Correction, but they do have sufficient resources to run small benchmarks, particularly if compiled with optimizations to make use of scarce qubits and limited operation counts and coherence times. QC has not yet, however, settled on a particular preferred device implementation technology, and indeed different NISQ prototypes implement qubits with very different physical approaches and therefore widely-varying device and machine characteristics. Our work performs a full-stack, benchmark-driven hardware-software analysis of QC systems. We evaluate QC architectural possibilities, software-visible gates, and software optimizations to tackle fundamental design questions about gate set choices, communication topology, the factors affecting benchmark performance and compiler optimizations. In order to answer key cross-technology and cross-platform design questions, our work has built the first top-to-bottom toolflow to target different qubit device technologies, including superconducting and trapped ion qubits which are the current QC front-runners. We use our toolflow, TriQ, to conduct {\em real-system} measurements on 7 running QC prototypes from 3 different groups, IBM, Rigetti, and University of Maryland. From these real-system experiences at QC's hardware-software interface, we make observations about native and software-visible gates for different QC technologies, communication topologies, and the value of noise-aware compilation even on lower-noise platforms. This is the largest cross-platform real-system QC study performed thus far; its results have the potential to inform both QC device and compiler design going forward.