Recent devices, using hundreds of superconducting quantum bits, claim to perform quantum computing. However, it is not an easy task to determine and quantify the degree of quantum coherence and control used by these devices. Namely, it is a difficult task to know with certainty whether or not a given device (e.g., the D-Wave One or D-Wave Two) is a quantum computer. Such a verification of quantum computing would be more accessible if we already had some kind of working quantum computer, to be able to compare the outputs of these various computing devices. Moreover, the verification process itself could strongly depend on whether the tested device is a standard (gate-based) or, e.g., an adiabatic quantum computer. Here we do not propose a technical solution to this quantum-computing “verification problem”, but rather outline the problem in a way which would help both specialists and non-experts to see the scale of this difficult task, and indicate some possible paths towards its solution.
Recent devices, using hundreds of superconducting quantum bits, claim to perform quantum computing. However, it is not an easy task to determine and quantify the degree of quantum coherence and control used by these devices. Namely, it is a difficult task to know with certainty whether or not a given device (e.g., the D-Wave One or D-Wave Two) is a quantum computer. Such a verification of quantum computing would be more accessible if we already had some kind of working quantum computer, to be able to compare the outputs of these various computing devices. Moreover, the verification process itself could strongly depend on whether the tested device is a standard (gate-based) or, e.g., an adiabatic quantum computer. Here we do not propose a technical solution to this quantum-computing “verification problem”, but rather outline the problem in a way which would help both specialists and non-experts to see the scale of this difficult task, and indicate some possible paths towards its solution.
Highlights• Generalized for many common quantum characterization, verification, and validation models • Identify if a model is consistent with data, and if not, where the inconsistencies are • Explore trends of inconsistencies with various features, such as circuit depth or gate types • Report quantifiable differences among experiment, model, and target SummaryWe are developing methods to determine and report characterization performance on circuit prediction tasks using statistical tests and simulation.We are demonstrating this process in experiment using gate set tomography to evaluate performance on circuit prediction tasks.We aim to create a validation process that is an effective tool in identifying characterization shortcomings and upgrading methodologies to create more accurate characterizations of quantum devices.
Today's computers work on bits that exist as either 0 or 1. Quantum computers aren't limited to two states; they encode information as quantum bits, or qubits, which can exist in superposition. Qubits represent atoms, ions, photons or electrons and their respective control devices that are working together to act as computer memory and a processor. Because a quantum computer can contain these multiple states simultaneously, it has the potential to be millions of times more powerful than today's most powerful supercomputers. A processor that can use registers of qubits will be able to perform calculations using all the possible values of the input registers simultaneously. This superposition causes a phenomenon called quantum parallelism, and is the motivating force behind the research being carried out in quantum computing. Due to technical obstacles, till date, a quantum computer has not yet been realized. But the concepts and ideas of quantum computing has been demonstrated using various methods like NMR, Ion Trap, Quantum Dot, Optical Methods, etc. A quantum computer manipulates qubits by executing a series of quantum gates, each a unitary transformation acting on a single qubit or pair of qubits. In applying these gates in succession, a quantum computer can perform a complicated unitary transformation to a set of qubits in some initial state. The qubits can then be measured, with this measurement serving as the final computational result. Research must devise a way to maintain decoherence and other potential sources of error at an acceptable level. Probably the most important idea in this field is the application of error correction in phase coherence as a means to extract information and reduce error in a quantum system without actually measuring that system. Thereby, quantum computers will emerge as the superior computational devices and perhaps one day make today's modern computer obsolete.
Quantum computing is a new and promising technology with the potential of exponentially powerful computation - if only a large-scale one can be built. There are several challenges in building a large-scale quantum computer - fabrication, verification, and architecture. The power of quantum computing comes from the ability to store a complex state in a single bit. This also what makes quantum systems difficult to build, verify, and design. Quantum states are fragile, so fabrication must be precise, and bits must often operate at very low temperatures. Unfortunately, the complete state may not be measured precisely, so verification is difficult. Imagine verifying an operation that is expected to not always get the same answer, but only an answer with a particular probability! Finally, errors occur much more often than with classical computing, making error correction the dominant task that quantum architectures need to perform well. We provide a basic tutorial of quantum computation for the system designer and examine the fundamental design and verification issues in constructing scalable quantum computers. We find the primary issues to be the verification of precise fabrication constraints, the design of quantum communication mechanisms, and the design of classical control circuitry for quantum operation.
Quantum computers promise to efficiently solve not only problems believed to be intractable for classical computers, but also problems for which verifying the solution is also considered intractable. This raises the question of how one can check whether quantum computers are indeed producing correct results. This task, known as quantum verification , has been highlighted as a significant challenge on the road to scalable quantum computing technology. We review the most significant approaches to quantum verification and compare them in terms of structure, complexity and required resources. We also comment on the use of cryptographic techniques which, for many of the presented protocols, has proven extremely useful in performing verification. Finally, we discuss issues related to fault tolerance, experimental implementations and the outlook for future protocols.
Quantum computing devices can now perform sampling tasks that, according to complexity-theoretic and numerical evidence, are beyond the reach of classical computers. This raises the question of how one can efficiently verify that a quantum computer operating in this regime works as intended. In 2008, Shepherd and Bremner proposed a protocol in which a verifier constructs a unitary from the comparatively easy-to-implement family of so-called and challenges a prover to execute it on a quantum computer. The challenge problem is designed to contain an obfuscated secret, which can be turned into a statistical test that accepts samples from a correct quantum implementation. It was conjectured that extracting the secret from the challenge problem is -hard, so that the ability to pass the test constitutes strong evidence that the prover possesses a quantum device and that it works as claimed. Unfortunately, about a decade later, Kahanamoku-Meyer found an efficient classical secret-extraction attack. Bremner, Cheng, and Ji very recently followed up by constructing a wide-ranging generalization of the original protocol. Their has been explicitly designed to circumvent the known weakness. They also suggested that the original construction can be made secure by adjusting the problem parameters. In this work, we develop a number of secret-extraction attacks that are effective against both new approaches in a wide range of problem parameters. In particular, we find multiple ways to recover the 300 -bit secret hidden in a challenge data set published by Bremner, Cheng, and Ji. The important problem of finding an efficient and reliable verification protocol for sampling-based proofs of quantum advantage thus remains open.
Quantum computing is emerging at a meteoric pace from a pure academic field to a fully industrial framework. Rapid advances are happening both in the physical realisations of quantum chips, and in their potential software applications. In contrast, we are not seeing that rapid growth in the design and verification methodologies for scaled-up quantum machines. In this work we describe the field of verification of quantum computers. We discuss the underlying concepts of this field, its theoretical and practical challenges, and state-of-the-art approaches to addressing those challenges. The goal of this paper is to help facilitate early efforts to adapt and create verification methodologies for quantum computers and systems. Without such early efforts, a debilitating gap may form between the state-of-the-art of low level physical technologies for quantum computers, and our ability to build medium, large, and very large scale integrated quantum circuits (M/L/VLSIQ).