Q-CTRL at APS March Meeting 2020 seemed to go by in the blink of an eye and it was that time of year again, APS March Meeting. We were disappointed that once again we were not able to see you all in person, although the team at Q-CTRL were incredibly excited to share our ground-breaking quantum research with the APS community. We presented seven talks at APS March Meeting 2021 across a range of topics, from optimized quantum algorithms for vehicle routing problems through to characterizing noise in quantum hardware using efficient machine learning tools. For those that were unable to attend APS March Meeting this year, below are the presentations delivered by the Q-CTRL team at APS March Meeting 2021:

  • Dr Andre Carvalho, Head Quantum Control Solutions at Q-CTRL: Pulse optimization for error-robust control on cloud-based superconducting hardware.

  • Dr Yuval Baum, Lead Quantum Automation Scientist at Q-CTRL: Reinforcement learning for error-robust control on cloud-based superconducting hardware [Part I].

  • Dr Mirko Amico, Senior Quantum Control Engineer at Q-CTRL: Reinforcement learning for error-robust control on cloud-based superconducting hardware [Part II].

  • Dr Anurag Mishra, Senior Quantum Control Engineer at Q-CTRL: Error-robust controls in quantum algorithms.

  • Dr Li Li, Senior Quantum Control Engineer at Q-CTRL: Noise reconstruction in quantum hardware via convex optimization.

  • Dr Christopher Bentley, Senior Quantum Control Engineer at Q-CTRL: Optimized quantum solutions for vehicle routing problems.

  • Dr Christopher Bentley, Senior Quantum Control Engineer at Q-CTRL: Fast, noise-robust pulses for parametric entangling gates in superconducting qubits.

APS March Meeting: Dr Andre Carvalho, Head Quantum Control Solutions at Q-CTRL. Pulse optimization for error-robust control on cloud-based superconducting hardware.

Pulse optimization for error-robust control on cloud-based superconducting hardware

In this APS March Meeting presentation we describe an experimental effort designing and deploying error-robust single-qubit operations on IBM Quantum hardware. In realistic circuits, coherent errors dominate default DRAG-pulse performance, manifesting as circuit errors an order of magnitude larger than suggested by randomized benchmarking.

Using optimized pulses these errors are suppressed under both serial and parallel (including crosstalk) execution of single-qubit gates on multi-qubit hardware. 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). Calibration techniques to accurately deploy optimized pulses via cloud-access controls, and maximize performance gains on ‘grey-box’ hardware, are presented. Performance gains of ~10x are achieved across multiple metrics relevant to system-level performance, including achievable circuit depth, stability over time, error-rate variability across qubits, and resilience to cross-talk.

APS March Meeting: Dr Yuval Baum, Lead Quantum Automation Scientist at Q-CTRL and Dr Mirko Amico, Senior Quantum Control Engineer at Q-CTRL. Reinforcement learning for error-robust control on cloud-based superconducting hardware [Part I] and [Part 2].

Reinforcement learning for error-robust control on cloud-based superconducting hardware [Part I]
Reinforcement learning for error-robust control on cloud-based superconducting hardware [Part 2]

The noisy nature of today's quantum hardware limits the ability to realize functioning quantum computers. Yet, a careful design of the systems' controls allows researchers to narrow the gap between current and desired hardware capabilities. In these APS March Meeting 2021 presentations we study a black-box optimization technique based on reinforcement learning for the discovery of high-performance gate-sets on a cloud quantum computer. We show that by employing RL, where intermediate information is used to optimize a long term goal, we are able to generate single-qubit gates which, when implemented on a real device, outperform existing model-based optimized pulses. We demonstrate the performance of our learner on IBM quantum hardware accessed via Qiskit Pulse programming. The entire learning process occurs on the quantum device itself, aiming to suit the low-level gate implementation to the underlying details of the specific hardware. This allows gate optimization without any prior knowledge or assumptions on the noise model, hardware limitations or any other undesired effect that exists in real devices. Our experiments demonstrate gates up to 3 times faster than the IBM default, with performance of less than 3e-4 errors per gate and stability up to 10 days, as compared to the single-day calibration window for IBM pulses.

APS March Meeting: Dr Anurag Mishra Senior Quantum Control Engineer at Q-CTRL. Error-robust controls in quantum algorithms.

Error-robust controls in quantum algorithms

Current commercial quantum computers are prone to various kinds of noise processes, such as leakage and dephasing, which degrade the performance of quantum algorithms. These errors can be dynamically suppressed by designing quantum controls that are robust to the underlying noise processes. In this APS March Meeting presentation we focus on the impact of using such validated control techniques on the performance of the variational quantum eigensolver (VQE). VQE is a NISQ-era quantum algorithm which has been used successfully to find the ground state energy of small molecules. This particular algorithm is thought to exhibit some inherent resistance to noise; however, it is not clear how such algorithms are impacted by errors which are correlated in space and time across quantum gates. In this talk, we discuss numerical simulation of the impact of common correlated noise processes on these algorithms. We shall demonstrate how optimally designed robust quantum controls can reduce the impact of various noise sources and improve the performance of quantum algorithms on commercial devices.

APS March Meeting: Dr Li Li Senior Quantum Control Engineer at Q-CTRL. Noise reconstruction in quantum hardware via convex optimization.

Noise reconstruction in quantum hardware via convex optimization.

In this APS March Meeting presentation we outline that interactions between a quantum system and noisy control hardware, or its environment, critically limits the performance and capabilities of noisy intermediate-scale quantum (NISQ) devices, as well as future quantum computing technologies. Accurately characterizing the noise profile of these systems is of central importance in developing techniques to improve hardware performance. This includes detailed microscopic characterization of time-dependent noise processes. We introduce a novel machine-learning technique allowing the efficient, flexible, and quantitatively accurate reconstruction of a noise process’s frequency-resolved power spectral density. By reformatting PSD reconstruction based on a measurement record as a convex optimization problem, our algorithm does not need to assume any specific shape of the noise spectral and can easily incorporate physically-motivated constraints for reconstruction without the loss of numerical efficiency. Moreover, this technique permits reconstruction using an arbitrary set of measurements, relaxing constraints previously imposed to limit the introduction of numerical artefacts. We present details of the approach as well as experimental demonstrations of trapped-ion motional-mode noise characterization via spin-motional entanglement.

APS March Meeting: Dr Christopher Bentley, Senior Quantum Control Engineer at Q-CTRL. Optimized quantum solutions for vehicle routing problems.

Optimized quantum solutions for vehicle routing problems.

Vehicle routing and scheduling are examples of transportation-network operational tasks that can be cast as optimization problems. Solving these problems becomes increasingly challenging with more vehicles or larger networks, as well as when constraints such as vehicle capacity are considered. Existing literature shows that quantum algorithms like QAOA can be used to solve certain transport problems, suggesting that quantum computers may be capable of tackling these applications in the future. However, gate errors and decoherence processes in today’s quantum hardware severely limit the performance of even small-scale demonstrations.

In this APS March Meeting presentation we address this issue by constructing algorithms with tailored gates that are robust against typical hardware imperfections. We perform simulations of QAOA for the Mobility as a Service (MaaS) problem with varying network sizes using a pulse-level description of gates and realistic noise models. We probe performance bounds in the presence of different errors including incoherent T1 processes, coherent over-rotation errors, and coherent dephasing, as are common on superconducting quantum computers. Finally, we discuss the results from the standard and robust MaaS QAOA algorithms and compare their performance for each type of error.

APS March Meeting: Dr Christopher Bentley, Senior Quantum Control Engineer at Q-CTRL. Fast, noise-robust pulses for parametric entangling gates in superconducting qubits.

Fast, noise-robust pulses for parametric entangling gates in superconducting qubits.

For Noisy Intermediate-Scale Quantum (NISQ) devices, incorporating robustness into computing operations is a critical target for enhancing computational capability. Superconducting transmon qubits are a world-leading quantum computing platform, which incorporate both active and passive strategies to improve performance, such as operating in flux-control “sweet-spots” for entangling parametric gates.

In this APS March Meeting 2021 presentation we demonstrate that even when starting from a set of parameters outside the sweet-spot condition, the introduction of numerically optimized flux-modulation waveforms can restore gate robustness.

Our approach involves specifiable deviation from the sinusoidal flux waveforms conventionally applied in parametric gates, up to the limit of arbitrary waveform optimization. This modulation enables the realization of faster gates while retaining the flux-noise robustness provided by dynamical sweet spots. We also demonstrate that this approach provides additional robustness to ambient dephasing noise by combining the modulated flux drive with optimized single-qubit drives applied concurrently. Finally, we show initial experimental results from the application of robust entangling gates to flux-tunable transmon qubits, validating the performance of this approach to gate construction.

If you have any questions on any of the topics presented you can contact our team directly.