Cloud-based quantum computers have become a reality with a number of companies allowing for cloud-based access to their machines with tens to more than 100 qubits. With easy access to quantum computers, quantum information processing will potentially revolutionize computation, and superconducting transmon-based quantum computers are among some of the more promising devices available. Cloud service providers today host a variety of these and other prototype quantum computers with highly diverse device properties, sizes, and performances. The variation that exists in today‘s quantum computers, even among those of the same underlying hardware, motivate the study of how one device can be clearly differentiated and identified from the next. As a case study, this work focuses on the properties of 25 IBM superconducting, fixed-frequency transmon-based quantum computers that range in age from a few months to approximately 2.5 years. Through the analysis of current and historical quantum computer calibration data, this work uncovers key features within the machines, primarily frequency characteristics of transmon qubits, that can serve as a basis for a unique hardware fingerprint of each quantum computer.
Cloud-based quantum computers have become a reality with a number of companies allowing for cloud-based access to their machines with tens to more than 100 qubits. With easy access to quantum computers, quantum information processing will potentially revolutionize computation, and superconducting transmon-based quantum computers are among some of the more promising devices available. Cloud service providers today host a variety of these and other prototype quantum computers with highly diverse device properties, sizes, and performances. The variation that exists in today‘s quantum computers, even among those of the same underlying hardware, motivate the study of how one device can be clearly differentiated and identified from the next. As a case study, this work focuses on the properties of 25 IBM superconducting, fixed-frequency transmon-based quantum computers that range in age from a few months to approximately 2.5 years. Through the analysis of current and historical quantum computer calibration data, this work uncovers key features within the machines, primarily frequency characteristics of transmon qubits, that can serve as a basis for a unique hardware fingerprint of each quantum computer.
Numerous public cloud infrastructure providers today allow for access to Noisy Intermediate-Scale Quantum (NISQ) computers. Changes in the environment or the machine configuration may affect their dependability. Through analysis of real quantum computer calibration data, this work demonstrates that quantum computers available from IBM Quantum experience periods of fluctuation or abrupt qubit frequency changes. This work further analyzes the correlation between the frequency change events, decoherence times, gate errors, and machine maintenance or offline periods. The results highlight that the properties of NISQ computers change over time, affecting their dependability, but not all of the changes can be explained with publicly available data.
Quantum computing may be the key to solving many of the problems our modern world faces today. Just as regular computers boosted our computing power to scales never imagined before, so too could quantum computers allow us to solve problems that we just do not yet have the computational power for. However, a properly functioning quantum computer that outclasses current classical computers is not yet a reality. Many di_erent platforms for quantum computing are being researched. Experiments are being done with diamond vacancies (see, for example, [1]), trapped ions [2], nuclear spins in NMR [3] and silicon quantum dots [4], to name a few. One of the most promising types of qubits is based on superconducting circuits [5]. The most widely studied and most promising superconducting qubit is the Transmon qubit [6]. One of the advantages of this type of qubit is that it can be fabricated using currently available lithographic patterning equipment. However, when fabricating Transmon chips, there is a problem of non-uniformity: when using the exact same method to make two chips, they can be vastly di_erent. As such, every chip needs to be characterised after fabrication. Characterising a chip means _nding speci_c important parameters for the qubits, such as their frequency and coherence times. For use in a quantum computer, qubit frequencies will have te be targeted with more precision than is currently possible [7] [8]. As such, a technique that allows change a qubit's frequency after its fabrication is very useful. In this thesis, such a technique is presented. The characterisation process of a Transmon chip is then explained. The data of the three chips that were subjected to the aforementioned technique is also presented.
Quantum Computing (QC) has evolved from a few custom quantum computers, which were only accessible to their creators, to an array of commercial quantum computers that can be accessed on the cloud by anyone. Accessing these cloud quantum computers requires a complex chain of tools that facilitate connecting, programming, simulating algorithms, estimating resources, submitting quantum computing jobs, retrieving results, and more. Some steps in the chain are hardware dependent and subject to change as both hardware and software tools, such as available gate sets and optimizing compilers, evolve. Understanding the trade-offs inherent in this process is essential for evaluating the power and utility of quantum computers. ARLIS has been systematically investigating these environments to understand these complexities. The work presented here is a detailed summary of three months of using such quantum programming environments. We show metadata obtained from these environments, including the connection metrics to the different services, the execution of algorithms, the testing of the effects of varying the number of qubits, comparisons to simulations, execution times, and cost. Our objective is to provide concrete data and insights for those who are exploring the potential of quantum computing. It is not our objective to present any new algorithms or optimize performance on any particular machine or cloud platform; rather, this work is focused on providing a consistent view of a single algorithm executed using out-of-the-box settings and tools across machines, cloud platforms, and time. We present insights only available from these carefully curated data.
Noisy intermediate-scale quantum computers are widely used for quantum computing (QC) from quantum cloud providers. Among them, superconducting quantum computers, with their high scalability and mature processing technology based on traditional silicon-based chips, have become the preferred solution for most commercial companies and research institutions to develop QC. However, superconducting quantum computers suffer from fluctuation due to noisy environments. To maintain reliability for every execution, calibration of the quantum processor is significantly important. During the long procedure to calibrate physical quantum bits (qubits), quantum processors must be turned into offline mode. In this work, we propose a real-time calibration framework (RCF) to execute quantum program tasks and calibrate in-demand qubits simultaneously, without interrupting quantum processors. Across a widely used noisy intermediate-scale quantum (NISQ) evaluation benchmark suite such as QASMBench, RCF achieves up to 18% reliability improvement for applications. For reliability on different physical qubits, RCF achieves an average gain of 15.7% (up to 36.7%). For cloud quantum machines, the throughput can be improved up to 9.5 throughput per minute (6.5 on average) based on baseline calibration time. In conclusion, RCF offers a reliable solution for large-scale, long-serving quantum machines.
Superconducting transmon qubits are a leading technology for quantum information processing, yet their reliable operation rests on meticulous calibration and characterization routines. These processes have been fine-tuned and are relatively well understood by the quantum computing community. Nevertheless, it is often challenging for newcomers to compile all the available information into a practical experimental flow. In this tutorial, we present a comprehensive walkthrough for the characterization and optimization of tunable transmon qubits, demonstrated on a commercial five-qubit processor. Moving beyond theoretical description, we detail in a straightforward manner the complete workflow, from cryogenic setup and wiring to parametric amplifier optimum operation, flux sweet-spot identification, pulse calibration, and readout optimization. We also demonstrate the characterization of qubit-qubit coupling, covering all steps before multiqubit operations. This guide serves as a reference for experimentalists seeking to efficiently bring up transmon-based quantum devices.
Quantum Computing (QC) has recently achieved significant technological progress, attracting growing interest from both academia and industry. As most users currently access QC resources through cloud platforms, concerns around the security and accountability of quantum services have become increasingly prominent. In particular, quantum noise, typically considered a source of error, can also reveal device-specific signatures that enable Quantum Device Fingerprinting (QDF). While QDF has legitimate applications such as anomaly detection and device accountability, it also carries dual-use risks, including potential misuse for unauthorized device tracking or targeted attacks. In this paper, we propose a novel Task-Driven Quantum Device Fingerprinting (TD-QDF) identification method based on quantum task outputs; we extract the fingerprint features of quantum devices from noisy quantum computing results. Unlike previous research, our method does not require any additional information (e.g., hardware details or noise information), thereby enhancing its practical applicability and accessibility. We conducted large-scale experiments using six QNN circuits on 10 IBM quantum computers, extended four classical quantum algorithms to validate generality, and demonstrated scalability on three 127-qubit processors. Specifically, the highest accuracy can reach up to 94.32% in the 3-classification device fingerprint recognition task and up to 82.4% and 61.6% in the 7-classification and 10-classification tasks, respectively. This research contributes to advancing quantum fingerprinting technologies and holds promising implications for enhancing the security and accountability of quantum computing systems and quantum cloud services.