Quantum computers with hundreds of qubits will be available soon. Unfortunately, high device error-rates pose a significant challenge in using these near-term quantum systems to power real-world applications. Executing a program on existing quantum systems generates both correct and incorrect outcomes, but often, the output distribution is too noisy to distinguish between them. In this paper, we show that erroneous outcomes are not arbitrary but exhibit a well-defined structure when represented in the Hamming space. Our experiments on IBM and Google quantum computers show that the most frequent erroneous outcomes are more likely to be close in the Hamming space to the correct outcome. We exploit this behavior to improve the ability to infer the correct outcome.
Quantum computers with hundreds of qubits will be available soon. Unfortunately, high device error-rates pose a significant challenge in using these near-term quantum systems to power real-world applications. Executing a program on existing quantum systems generates both correct and incorrect outcomes, but often, the output distribution is too noisy to distinguish between them. In this paper, we show that erroneous outcomes are not arbitrary but exhibit a well-defined structure when represented in the Hamming space. Our experiments on IBM and Google quantum computers show that the most frequent erroneous outcomes are more likely to be close in the Hamming space to the correct outcome. We exploit this behavior to improve the ability to infer the correct outcome.
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.
The field of quantum computing has experienced a rapid expansion in recent years, with ongoing exploration of new technologies, a decrease in error rates, and a growth in the number of qubits available in quantum processors. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction and Mitigation are rapidly advancing areas of research in the quantum computing landscape, with a goal of reducing errors. IBM has recently emphasized that Quantum Error Mitigation is the key to unlocking the full potential of quantum computing. A recent work, namely HAMMER, demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they assumed that errors occur solely in local clusters, whereas we observe that at higher average Hamming distances this structure falls away. Our study demonstrates that the correlated structure is not just limited to local patterns, but it also encompasses certain non-local clustering patterns that can be accurately characterized through a Poisson distribution model. This model takes into account the input circuit, the current state of the device, including calibration statistics, and the qubit topology. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Our Q-Beep approach delivers state-of-the-art results, thanks to its problem-aware modeling of the error distribution's underlying structure and the implementation of an Bayesian network state-graph. This has resulted in an increase of up to 234.6% in circuit execution accuracy on BV circuits and an average improvement of 71.0% in the quality of QAOA solutions when tested on 16 IBMQ quantum processors.
Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processors' qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction (in-situ error mitigation) and Quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors. IBM recently published an article stating that Quantum Error Mitigation is the path to quantum computing usefulness. A recent work, namely HAMMER, demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they assumed that errors occur solely in local clusters, whereas we observe that at higher average Hamming distances this structure falls away. In this work, we show that such a correlated structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the proposed iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, a fidelity improvement of up to 17.8% is attained. Q-Beep is a light-weight post-processing technique that can be performed offline and remotely, making it a useful tool for quantum vendors to adopt and provide more reliable circuit induction results. Q-Beep is maintained at github.com/pnnl/qbeep
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.
Despite rapid progress in quantum computing in the last decade, the limited usability of quantum computers remains a major roadblock toward its wider adoption. Current noisy intermediate-scale quantum (NISQ) computers produce highly erroneous program outputs for quantum-advantage-proven algorithms - that is, algorithms that are infeasible or orders of magnitude slower on classical supercomputing and high-performance computing (HPC) clusters. Unfortunately, currently, quantum computing programmers lack robust system software tools and methods to make meaningful use of erroneous program executions on quantum computers. This lack of capability is the core motivation behind the fundamental question this dissertation poses: "Can we build system software tools for programmers to make the quantum program execution and output meaningful on NISQ machines?" This dissertation answers this question in the affirmative - experimentally demonstrating on real-system quantum computers that it is possible to extract near-accurate program output from noisy executions on today's erroneous quantum computers, ironically using classical HPC resources and knowledge. This dissertation demonstrates how to achieve this goal without requiring user intervention, domain knowledge about quantum algorithms, or additional quantum hardware support. Unfortunately, as this dissertation uncovers, progressing toward making quantum computers usable is a double-edged sword. In the near future, only a few entities in the world may have access to powerful quantum computers, and these quantum computers will be used to solve previously-unsolved large-scale optimization problems, possibly without an explicit trust model between the service provider and the customer. Therefore, this dissertation envisions that the solutions to such large-scale optimization problems will be considered sensitive and will need to be protected. This dissertation takes the first few steps toward preparing us for that future - by developing a novel method that intelligently obfuscates near-accurate program output and quantum circuit structure to preserve a customer's privacy under a specified computation model and resource availability. The approaches introduced in this dissertation open up new research avenues for hybrid quantum-classical computing. This dissertation also aims to lower the barrier to entry for quantum computing research for the experimental computer systems and HPC community by open-sourcing multiple novel datasets and software frameworks implemented for real-system quantum computers.--Author's abstract
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.