Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2010.08057 · Physical Review Applied

Error-Robust Quantum Logic Optimization Using a Cloud Quantum Computer Interface

Andre R. R. Carvalho, Harrison Ball, Michael J. Biercuk, Michael Hush, Felix Thomsen

Published Jun 22, 2021• 51 citations
arXivHTMLPDF

Abstract

We describe an experimental effort designing and deploying error-robust single-qubit operations using a cloud-based quantum computer and analog-layer programming access. We design numerically optimized pulses that implement target operations and exhibit robustness to various error processes including dephasing noise, instabilities in control amplitudes, and crosstalk. Pulse optimization is performed using a flexible optimization package incorporating a device model and physically relevant constraints (e.g., bandwidth limits on the transmission lines of the dilution refrigerator housing IBM Quantum hardware). We present techniques for conversion and calibration of physical Hamiltonian definitions to pulse waveforms programmed via Qiskit Pulse and compare performance against hardware default derivative removal by adiabatic gate (DRAG) pulses on a five-qubit device. Experimental measurements reveal default DRAG pulses exhibit coherent errors an order of magnitude larger than tabulated randomized-benchmarking measurements; solutions designed to be robust against these errors outperform hardware-default pulses for all qubits across multiple metrics. Experimental measurements demonstrate performance enhancements up to: about a $10$ times single-qubit gate coherent-error reduction; about a $5$ times average coherent-error reduction across a five-qubit system; about a $10$ times increase in calibration window to one week of valid pulse calibration; about a $12$ times reduction gate-error variability across qubits and over time; and up to about a $9$ times reduction in single-qubit gate error (including crosstalk) in the presence of fully parallelized operations. Randomized benchmarking reveals error rates for Clifford gates constructed from optimized pulses consistent with tabulated ${T}_{1}$ limits, and demonstrates a narrowing of the distribution of outcomes over randomizations associated with suppression of coherent errors.

DOI
10.1103/physrevapplied.15.064054

Related papers

Experimental error suppression in Cross-Resonance gates via multi-derivative pulse shaping

Boxi Li, Tommaso Calarco, Felix Motzoi

Abstract While quantum circuits are reaching impressive widths in the hundreds of qubits, their depths have not been able to keep pace. In particular, cloud computing gates on multi-qubit, fixed-frequency superconducting chips continue to hover around the 1% error range, contrasting with the progress seen on carefully designed two-qubit chips, where error rates have been pushed towards 0.1%. Despite the strong impetus and a plethora of research, experimental demonstration of error suppression on these multi-qubit devices remains challenging, primarily due to the wide distribution of qubit parameters and the demanding calibration process required for advanced control methods. Here, we achieve this goal, using a simple control method based on multi-derivative, multi-constraint pulse shaping, which acts simultaneously against multiple error sources. Our approach establishes a two to fourfold improvement on the default calibration scheme, demonstrated on four qubits on the IBM Quantum Platform with limited and intermittent access, enabling these large-scale fixed-frequency systems to fully take advantage of their superior coherence times. The achieved CNOT fidelities of 99.7(1)% on those publically available qubits come from both coherent control error suppression and accelerated gate time.

arXiv:2303.01427•npj Quantum Information•Jul 2, 2024•23 citations

Qiskit pulse: programming quantum computers through the cloud with pulses

Thomas Alexander, Naoki Kanazawa, Daniel J Egger +4 more

The quantum circuit model is an abstraction that hides the underlying physical implementation of gates and measurements on a quantum computer. For precise control of real quantum hardware, the ability to execute pulse and readout-level instructions is required. To that end, we introduce Qiskit Pulse, a pulse-level programming paradigm implemented as a module within Qiskit-Terra [ 1 ]. To demonstrate the capabilities of Qiskit Pulse, we calibrate both un-echoed and echoed variants of the cross-resonance entangling gate with a pair of qubits on an IBM Quantum system accessible through the cloud. We perform Hamiltonian characterization of both single and two-pulse variants of the cross-resonance entangling gate with varying amplitudes on a cloud-based IBM Quantum system. We then transform these calibrated sequences into a high-fidelity CNOT gate by applying pre and post local-rotations to the qubits, achieving average gate fidelities of F = 0.981 and F = 0.979 for the un-echoed and echoed respectively. This is comparable to the standard backend CNOT fidelity of F CX = 0.984. Furthermore, to illustrate how users can access their results at different levels of the readout chain, we build a custom discriminator to investigate qubit readout correlations. Qiskit Pulse allows users to explore advanced control schemes such as optimal control theory, dynamical decoupling, and error mitigation that are not available within the circuit model.

arXiv:2004.06755•Quantum Science and Technology•Jul 8, 2020•182 citations

Parallel Quantum Gates via Scalable Subsystem-Optimized Robust Control

Xiaodong Yang, Ran Liu, Jun Li

Accurate and efficient implementation of parallel quantum gates is crucial for scalable quantum information processing. However, the unavoidable crosstalk between qubits in current noisy processors impedes the achievement of high gate fidelities and renders full Hilbert-space control optimization prohibitively difficult. Here, we overcome this challenge by reducing the full-system optimization to crosstalk-robust control over constant-sized subsystems, which dramatically reduces the computational cost. Our method effectively eliminates the leading-order gate operation deviations induced by crosstalk, thereby suppressing error rates. Within this framework, we construct analytical pulse solutions for parallel single-qubit gates and numerical pulses for parallel multi-qubit operations. We validate the proposed approach numerically across multiple platforms, including coupled nitrogen-vacancy centers, a nuclear-spin processor, and superconducting-qubit arrays with up to 200 qubits. As a result, the noise scaling is reduced from exponential to linear for parallel single-qubit gates, and an order-of-magnitude reduction is achieved for parallel multi-qubit gates. Moreover, our method does not require precise knowledge of crosstalk strengths and makes no assumption about the underlying qubit connectivity or lattice geometry, thereby establishing a scalable framework for parallel quantum control in large-scale quantum architectures.

quant-pharXiv:2601.01990•Jan 6, 2026•0 citations

Software tools for quantum control: improving quantum computer performance through noise and error suppression

Harrison Ball, Michael J. Biercuk, Andre R. R. Carvalho +10 more

Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of Python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the QC software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of key features including a flexible optimization toolkit, engineering-inspired filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.

arXiv:2001.04060•Quantum Science and Technology•Jan 18, 2021•113 citations

Quantum Noise Suppression at Scale with Crosstalk-Robust Gate Sets

Andy J. Goldschmidt, Emilio Peláez Cisneros, Ryan Sitler +3 more

We introduce crosstalk-robust gate sets, which are obtained using a novel, scalable optimal control problem exploiting locality. Through the suppression of pairwise quantum crosstalk, the gate sets enable robustness that extends to multi-qubit circuits. The IBM Quantum Platform devices provide a testbed for our gate sets, where we study their efficacy via error suppression protocols and randomized parallel single-qubit circuits of up to eight qubits. Furthermore, we provide the first known assessment of the impact of complete optimal control gate sets on quantum algorithms. Using a Hamiltonian simulation of a four-qubit transverse field Ising model, we show that noise-informed gates enhance median algorithmic performance by a factor of four over baseline Gaussian gates using the same calibration procedures. Lastly, we provide numerical evidence that optimized gate sets enable larger qubit-qubit coupling strengths that can cut two-qubit gate times in half. This result confirms that hardware-software co-design using quantum optimal control can create new opportunities for quantum computing architectures.

quant-pharXiv:2603.15758•Mar 18, 2026•0 citations

Tackling Coherent Noise in Quantum Computing via Cross-Layer Compiler Optimization

Xiangyu Ren, Junjie Wan, Zhiding Liang +1 more

Quantum computing hardware is affected by quantum noise that undermine the quality of results of an executed quantum program. Amongst other quantum noises, coherent error that caused by parameter drifting and miscalibration, remains critical. While coherent error mitigation has been studied before, studies focused either on gate-level or pulse-level -- missing cross-level optimization opportunities; And most of them only target single-qubit gates -- while multi-qubit gates are also used in practice. To address above limitations, this work proposes a cross-layer approach for coherent error mitigation that considers program-level, gate-level, and pulse-level compiler optimizations, by leveraging the hidden inverse theory, and exploiting the structure inside different quantum programs, while also considering multi-qubit gates. We implemented our approach as compiler optimization passes, and integrated into IBM Qiskit framework. We tested our technique on real quantum computer (IBM-Brisbane), and demonstrated up to 92% fidelity improvements (45% on average), on several benchmarks.

arXiv:2410.09664•Oct 12, 2024•0 citations
We describe an experimental effort designing and deploying error-robust single-qubit operations using a cloud-based quantum computer and analog-layer programming access. We design numerically optimized pulses that implement target operations and exhibit robustness to various error processes including dephasing noise, instabilities in control amplitudes, and crosstalk. Pulse optimization is performed using a flexible optimization package incorporating a device model and physically relevant constraints (e.g., bandwidth limits on the transmission lines of the dilution refrigerator housing IBM Quantum hardware). We present techniques for conversion and calibration of physical Hamiltonian definitions to pulse waveforms programmed via Qiskit Pulse and compare performance against hardware default derivative removal by adiabatic gate (DRAG) pulses on a five-qubit device. Experimental measurements reveal default DRAG pulses exhibit coherent errors an order of magnitude larger than tabulated randomized-benchmarking measurements; solutions designed to be robust against these errors outperform hardware-default pulses for all qubits across multiple metrics. Experimental measurements demonstrate performance enhancements up to: about a 101010 times single-qubit gate coherent-error reduction; about a 555 times average coherent-error reduction across a five-qubit system; about a 101010 times increase in calibration window to one week of valid pulse calibration; about a 121212 times reduction gate-error variability across qubits and over time; and up to about a 999 times reduction in single-qubit gate error (including crosstalk) in the presence of fully parallelized operations. Randomized benchmarking reveals error rates for Clifford gates constructed from optimized pulses consistent with tabulated T1{T}_{1}T1​ limits, and demonstrates a narrowing of the distribution of outcomes over randomizations associated with suppression of coherent errors.
Abstract While quantum circuits are reaching impressive widths in the hundreds of qubits, their depths have not been able to keep pace. In particular, cloud computing gates on multi-qubit, fixed-frequency superconducting chips continue to hover around the 1% error range, contrasting with the progress seen on carefully designed two-qubit chips, where error rates have been pushed towards 0.1%. Despite the strong impetus and a plethora of research, experimental demonstration of error suppression on these multi-qubit devices remains challenging, primarily due to the wide distribution of qubit parameters and the demanding calibration process required for advanced control methods. Here, we achieve this goal, using a simple control method based on multi-derivative, multi-constraint pulse shaping, which acts simultaneously against multiple error sources. Our approach establishes a two to fourfold improvement on the default calibration scheme, demonstrated on four qubits on the IBM Quantum Platform with limited and intermittent access, enabling these large-scale fixed-frequency systems to fully take advantage of their superior coherence times. The achieved CNOT fidelities of 99.7(1)% on those publically available qubits come from both coherent control error suppression and accelerated gate time.
The quantum circuit model is an abstraction that hides the underlying physical implementation of gates and measurements on a quantum computer. For precise control of real quantum hardware, the ability to execute pulse and readout-level instructions is required. To that end, we introduce Qiskit Pulse, a pulse-level programming paradigm implemented as a module within Qiskit-Terra [ 1 ]. To demonstrate the capabilities of Qiskit Pulse, we calibrate both un-echoed and echoed variants of the cross-resonance entangling gate with a pair of qubits on an IBM Quantum system accessible through the cloud. We perform Hamiltonian characterization of both single and two-pulse variants of the cross-resonance entangling gate with varying amplitudes on a cloud-based IBM Quantum system. We then transform these calibrated sequences into a high-fidelity CNOT gate by applying pre and post local-rotations to the qubits, achieving average gate fidelities of F = 0.981 and F = 0.979 for the un-echoed and echoed respectively. This is comparable to the standard backend CNOT fidelity of F CX = 0.984. Furthermore, to illustrate how users can access their results at different levels of the readout chain, we build a custom discriminator to investigate qubit readout correlations. Qiskit Pulse allows users to explore advanced control schemes such as optimal control theory, dynamical decoupling, and error mitigation that are not available within the circuit model.
Accurate and efficient implementation of parallel quantum gates is crucial for scalable quantum information processing. However, the unavoidable crosstalk between qubits in current noisy processors impedes the achievement of high gate fidelities and renders full Hilbert-space control optimization prohibitively difficult. Here, we overcome this challenge by reducing the full-system optimization to crosstalk-robust control over constant-sized subsystems, which dramatically reduces the computational cost. Our method effectively eliminates the leading-order gate operation deviations induced by crosstalk, thereby suppressing error rates. Within this framework, we construct analytical pulse solutions for parallel single-qubit gates and numerical pulses for parallel multi-qubit operations. We validate the proposed approach numerically across multiple platforms, including coupled nitrogen-vacancy centers, a nuclear-spin processor, and superconducting-qubit arrays with up to 200 qubits. As a result, the noise scaling is reduced from exponential to linear for parallel single-qubit gates, and an order-of-magnitude reduction is achieved for parallel multi-qubit gates. Moreover, our method does not require precise knowledge of crosstalk strengths and makes no assumption about the underlying qubit connectivity or lattice geometry, thereby establishing a scalable framework for parallel quantum control in large-scale quantum architectures.
Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of Python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the QC software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of key features including a flexible optimization toolkit, engineering-inspired filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.
We introduce crosstalk-robust gate sets, which are obtained using a novel, scalable optimal control problem exploiting locality. Through the suppression of pairwise quantum crosstalk, the gate sets enable robustness that extends to multi-qubit circuits. The IBM Quantum Platform devices provide a testbed for our gate sets, where we study their efficacy via error suppression protocols and randomized parallel single-qubit circuits of up to eight qubits. Furthermore, we provide the first known assessment of the impact of complete optimal control gate sets on quantum algorithms. Using a Hamiltonian simulation of a four-qubit transverse field Ising model, we show that noise-informed gates enhance median algorithmic performance by a factor of four over baseline Gaussian gates using the same calibration procedures. Lastly, we provide numerical evidence that optimized gate sets enable larger qubit-qubit coupling strengths that can cut two-qubit gate times in half. This result confirms that hardware-software co-design using quantum optimal control can create new opportunities for quantum computing architectures.
Quantum computing hardware is affected by quantum noise that undermine the quality of results of an executed quantum program. Amongst other quantum noises, coherent error that caused by parameter drifting and miscalibration, remains critical. While coherent error mitigation has been studied before, studies focused either on gate-level or pulse-level -- missing cross-level optimization opportunities; And most of them only target single-qubit gates -- while multi-qubit gates are also used in practice. To address above limitations, this work proposes a cross-layer approach for coherent error mitigation that considers program-level, gate-level, and pulse-level compiler optimizations, by leveraging the hidden inverse theory, and exploiting the structure inside different quantum programs, while also considering multi-qubit gates. We implemented our approach as compiler optimization passes, and integrated into IBM Qiskit framework. We tested our technique on real quantum computer (IBM-Brisbane), and demonstrated up to 92% fidelity improvements (45% on average), on several benchmarks.