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IEEE/ACM International Symposium on Microarchitecture (MICRO)

Ensemble of Diverse Mappings

Swamit Tannu, Moinuddin K. Qureshi

Published Oct 11, 2019• 100 citations
Publisher

Abstract

Near-term quantum computers do not have the ability to perform error correction. Such Noisy Intermediate Scale Quantum (NISQ) computers can produce incorrect output as the computation is subjected to errors. The applications on a NISQ machine try to infer the correct output by running the same program thousands of times and logging the output. If the error rates are low and the errors are not correlated, then the correct answer can be inferred as the one appearing with the highest frequency. Unfortunately, quantum computers are subjected to correlated errors, which can cause an incorrect answer to appear more frequently than the correct answer.

DOI
10.1145/3352460.3358257

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Final Program

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Near-term quantum computers do not have the ability to perform error correction. Such Noisy Intermediate Scale Quantum (NISQ) computers can produce incorrect output as the computation is subjected to errors. The applications on a NISQ machine try to infer the correct output by running the same program thousands of times and logging the output. If the error rates are low and the errors are not correlated, then the correct answer can be inferred as the one appearing with the highest frequency. Unfortunately, quantum computers are subjected to correlated errors, which can cause an incorrect answer to appear more frequently than the correct answer.
Despite the current progress in quantum computing, the reliability of quantum computers is very challenging. Near-term quantum computers referred to as Noisy Intermediate-Scale Quantum (NISQ) computers are expected to operate in the presence of errors. To run a quantum circuit on a NISQ computer, the circuit should be mapped to satisfy the physical constraints of the quantum architecture. The mapping process takes into account the error rates of the quantum hardware. It selects physical qubits and their movements, which minimize the circuit error rates. The output of the quantum circuit can be obtained through several runs on NISQ computers.
Near-term quantum computers contain noisy devices, which makes it difficult to infer the correct answer even if a program is run for thousands of trials. On current machines, qubit measurements tend to be the most error-prone operations (with an average error-rate of 4%) and often limit the size of quantum programs that can be run reliably on these systems. As quantum programs create and manipulate correlated states, all the program qubits are measured in each trial and thus, the severity of measurement errors increases with the program size. The fidelity of quantum programs can be improved by reducing the number of measurement operations.
Quantum computing promises exponential speedups for an important class of problems.While quantum computers with few dozens of qubits have been demonstrated, these machines suffer from a high rate of gate errors.Such machines are operated in the Noisy Intermediate Scale Quantum (NISQ) mode of computing where the output of the machine can be erroneous.In this talk, I will discuss some of our recent work that aims to improve the reliability of NISQ computers by developing software techniques to mitigate hardware errors.Our first work exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid qubits that are error-prone.Our second work looks at executing different versions of the programs each crafted to cause diverse mistakes so that the machine becomes less vulnerable to correlated errors.Our third work looks at exploiting the state-dependent bias in measurement errors (state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to perform the measurement in the stronger state.We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability.Finally, I will also briefly discuss the hardware aspect of designing largescale quantum computers, including cryogenic processor and cryogenic memory system.
Existing and near-term quantum computers face significant reliability challenges because of high error rates caused by noise. Such machines are operated in the Noisy Intermediate Scale Quantum (NISQ) model of computing. As NISQ machines exhibit high error-rates, only programs that require a few qubits can be executed reliably. Therefore, NISQ machines tend to underutilize its resources. In this paper, we propose to improve the throughput and utilization of NISQ machines by using multi-programming and enabling the NISQ machine to concurrently execute multiple workloads.
A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time. In the search for quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry and combinatorial optimization. The goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, we provide a thorough summary of NISQ computational paradigms and algorithms. We discuss the key structure of these algorithms, their limitations, and advantages. We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.
Noisy Intermediate-Scale Quantum (NISQ) machines are being increasingly used to develop quantum algorithms and establish use cases for quantum computing. However, these devices are highly error-prone and produce output, which can be far from the correct output of the quantum algorithm. In this paper, we propose VERITAS, an end-to-end approach toward designing quantum experiments, executing experiments, and correcting outputs produced by quantum circuits post their execution such that the correct output of the quantum algorithm can be accurately estimated.