IBM’s quantum systems powered 46 non-IBM presentations at APS March Meetings, helping push the field of quantum information science forward overall.
Today, computation is central to the way we carry out the scientific method. High-performance computing resources help researchers generate hypotheses, find patterns in large datasets, perform statistical analyses, and even run experiments faster than ever before. Logically, access to a completely different computational paradigm — one with the potential to perform calculations intractable for any classical computer — could open up an entirely new realm for scientific discovery.
As quantum computers extend our computational capabilities, so too do we expect them to extend our ability to push science forward. In fact, access to today’s limited quantum computers has already provided benefits to researchers worldwide, offering an unprecedented look at the inner workings of the laws that govern how nature works, as well as a new lens through which to approach problems in chemistry, simulation, optimization, artificial intelligence, and other fields.
Here, we demonstrate the utility of IBM Quantum hardware as a tool to accelerate discoveries across scientific research, as shown at the American Physical Society’s March Meeting 2021. The APS March Meeting is the world’s largest physics conference, where researchers present their latest results to their peers and to the wider physics community. As a leading provider of quantum computing hardware, IBM’s quantum systems powered 46 non-IBM presentations in order to help discover new algorithms, simulate condensed matter and many-body systems, explore the frontiers of quantum mechanics and particle physics, and push the field of quantum information science forward overall. With this year’s APS March Meeting in mind, we believe that research access to quantum hardware — both on-site and via the cloud — will become a core driver for exploration and discovery in the field of physics in the coming years.
At IBM Quantum, we build universal quantum computing systems for scientists, engineers, developers, and businesses. Our initiative operates a fleet of over two dozen full-stack quantum computing systems ranging from 1 to 65 qubits based on the transmon superconducting qubit architecture. These systems incorporate state of the art control electronics and a continually evolving software in order to offer the best-performing quantum computing services in the world. Our team released our development roadmap, demonstrating how we plan not only to scale processors up, but how to turn these devices into transformative computational tools.
IBM offers access to its quantum computing systems through several avenues. Our flagship program is our IBM Quantum Network, including our hubs, which collaborate with IBM on advancing quantum computing research, our industry partners, who explore a broad set of potential applications, and our members, who seek to build their general knowledge of quantum computing. At the broadest level, members of our community use the IBM Quantum Composer and IBM Quantum Lab programming tools, as well as the Qiskit open source software development kit to build and visualize quantum circuits and run quantum experiments on a dozen smaller devices. Researchers can also receive priority system access through our IBM Quantum Researchers Program.
Through the Network, the Researchers Program, and the quantum programming tools available to the broader community, IBM offers a range of support in order to facilitate the research and discovery process. This includes, but is not limited to, direct collaboration with our quantum researchers on projects, consultation on potential topic-specific use cases, and fostering the open source community passionate to advance the field of quantum computation.
As quantum computers mature, their physical requirements will necessitate that most users remotely access them and can program them in a frictionless way — that is, reap their benefits without needing to be a quantum mechanics expert. Quantum computing outfits across the industry are developing quantum systems in anticipation of this developing ecosystem. Access to these cloud-based computers will be of chief importance to three key developer segments: quantum kernel developers, seeking to understand quantum computers and their underlying mechanics to the level of logic circuits; quantum algorithm developers, employing these circuits to find potential advantages over existing classical computing algorithms and to push the limits of computing overall, and model developers, who apply these algorithms to perform research on real-world use cases in fields like physics, chemistry, optimization, machine learning, and others.
While IBM is developing our own ecosystem through accessible services on the IBM Cloud, we think that quantum access is important beyond our own communities. We’ve developed Qiskit to run application modules on any quantum computing platform, even other architectures such as trapped-ion devices. Ultimately, our goal is to democratize access to quantum computing, while providing the best hardware and expertise to all of those who hope to do research with and on our devices.
The multitude of presentations leveraging IBM Quantum at the APS March Meeting demonstrate not only adoption of IBM’s quantum computers as a platform for research by institutions outside of IBM, but more importantly, that the ability to access and run programs on these devices via the cloud is already advancing science and research today. Experiments on our systems spanned each of our projected developer segments, from kernel developers researching quantum computing itself, algorithm developers, as well as model developers employing quantum computing as a means to approach other problems in physics and beyond.
The innately quantum nature of qubits means that even noisy quantum computers serve as powerful analog and digital simulators of quantum mechanics, such as those studied in quantum many-body and condensed matter physics. Quantum computers are arguably already providing a quantum advantage to researchers in these fields, who are able to tackle problems with a simulator whose properties more closely align with the systems they wish to study versus a classical computer. IBM Quantum systems played a central role in many of these cutting-edge studies at APS March.
For example, in her presentation, “Scattering in the Ising Model with the Quantum Lanczos Algorithm”,1 Oak Ridge National Lab’s Kubra Yeter Aydeniz simulated one-particle propagation and two-particle scattering in the ubiquitous one-dimensional Ising model of particles in one of two spin states, here with periodic boundary conditions. Her team employed an algorithm to calculate the energy levels and eigenstates of the system, gathering information on particle numbers for spatial sites and transition amplitudes as well as the transverse magnetization as a function of time.
Benchmarking and characterizing noisy quantum systems
As quantum computers grow in complexity, simulating their results classically will grow more difficult, in turn hampering our ability to tell whether they’ve successfully run a circuit. Researchers are therefore devising methods to characterize and benchmark the performance of near-term quantum computers overall — and hopefully develop methods that will continue to be applicable as quantum computers increase in size and complexity. A series of APS March talks demonstrated benchmarking methods applied to IBM’s quantum devices.
In one such talk, “Scalable and targeted benchmarking of quantum computers” Sandia National Lab’s Timothy Proctor presented his scalable and flexible benchmarking technique that expanded on the IBM-devised Quantum Volume metric,2 in order to capture the potential tradeoff between increasing a circuit’s depth (the number of time-steps worth of gates) versus its width (the number of qubits employed). By employing randomized mirror circuits — those composed of a random series of one- and two-qubit operations, followed by the inverse of those operations — the team developed a benchmarking strategy that would efficiently work on quantum computers of 100s or perhaps 1,000s of qubits.
We hope that, one day, quantum computers will employ superposition, entanglement, and interference in order to provide new ways to solve traditionally difficult problems. Today, scientists are working to develop algorithms that will provide those potential speedups—with an eye toward what sorts of benefits they may gather from algorithms they can run on present-day devices. IBM’s quantum devices served as the ideal testbed for teams looking for a system with which to develop hardware-aware algorithms.
For example, in the NSF-funded work “Rodeo Algorithm for Quantum Computation”,3 Jacob Watkin presented a new approach to the ubiquitous quantum phase estimation algorithm called the Rodeo Algorithm, targeted at near-term quantum devices. The algorithm, meant to generalize the famous Kitaev Phase Estimation Algorithm, employs stochastically varying phase shifts in order to achieve results at short gate depths.
Advancing Quantum Computing
Perhaps the most popular use of IBM’s quantum systems at the APS March Meeting was as a foundation upon which to study the inner workings of quantum devices, including characterizing noise, testing the fidelity of the chips, developing error correction and mitigation strategies, and other research meant to advance the field as a whole. We hope that the advances gleaned from studying our devices will benefit the field overall.
In “Error mitigation with Clifford quantum-circuit data”,4 Piotr Czarnik from Los Alamos National Laboratory proposed a new error mitigation method for gate-based quantum computers. The method begins by generating training data from quantum circuits built only from Clifford group gates, then creates a linear fit to the data that can predict noise free observables for arbitrary noisy circuits. Czarnik’s team demonstrated an order-of-magnitude error reduction for a ground state energy problem by running their error mitigation strategy on the 16 qubit ibmq_melbourne system.
Access to a controllable quantum system offers researchers a new way to think about problems across physics. For example, in “Collective Neutrino Oscillations on a Quantum Computer”,5 Shikha Bangar demonstrated that quantum resources can serve as an efficient way to represent a particle physics system — collective neutrino oscillations. Meanwhile, in “Quantum Sensing Simulation on Quantum Computers using Optimized Control”,6 Paraj Titum from The Johns Hopkins University Applied Physics Laboratory developed new protocols to detect signals over background noise, and demonstrated the protocol on an IBM quantum computer.
The IBM Quantum team is thrilled knowing that our hardware is accelerating scientific progress around the world—and we continue to push progress on our own hardware in order to keep these discoveries flowing. The APS March meeting also served as a venue for our researchers to present some of the ideas they’re developing for future quantum systems, including advanced packaging technologies,7 novel qubit coupling architectures,7, 8, 9, 10, 11 and even qubits a tiny fraction of the size of our current transmons.12 We also used the very same IBM Quantum systems to drive progress in improving quantum volume,13 demonstrations of algorithms and quantum advantage,14, 15, 16 and exploration of dynamic circuits and quantum error correction.17, 18 The interplay between the end-users of IBM’s systems and the researchers developing the next generation of processors helps keep IBM’s devices cutting-edge and relevant in the months and years to come.
Access to quantum computing systems is advancing science, even in this early era of noisy quantum computers. This applies to more than just IBM’s systems; scientists at the APS March meeting presented results based on access to other superconducting architectures such as Rigetti’s, as well as trapped-ion qubit systems like those built by Honeywell. Our analysis of the 2021 APS March Meeting’s results demonstrates that investment into and use of existing cloud-based quantum computing platforms provides researchers with a powerful tool for scientific discovery. We expect the pace of discovery to accelerate as quantum computing systems and their associated cloud-based quantum ecosystem matures.
Quantum Information Science: We’re exploring the fundamentals of quantum science, from entanglement and superposition to the development of novel quantum algorithms.
Yeter-Aydeniz, K., Siopsis, G. & Pooser, R. C. Scattering in the Ising model with the quantum Lanczos algorithm. New J. Phys. 23, 043033 (2021). ↩
Proctor, T. et al. Scalable and targeted benchmarking of quantum computers. APS March Meeting 2021. ↩
Czarnik, P. et al. Error mitigation with Clifford quantum-circuit data. APS March Meeting 2021. ↩
Aydeniz, K. Y. et al. Collective Neutrino Oscillations on a Quantum Computer. APS March Meeting 2021. ↩
Paraj, T. et al. Quantum Sensing Simulation on Quantum Computers using Optimized Control. APS March Meeting 2021. ↩
Suttle, J. et al. Scalable architecture for next generation superconducting quantum processors. APS March Meeting 2021. ↩ ↩2
Kandala, A. et al. A high-fidelity, two-qubit cross-resonance gate using interference couplers. APS March Meeting 2021. ↩
Stehlik, J. et al. Fast Tunable Coupler Architecture for Fixed Frequency Transmons. APS March Meeting 2021. ↩
Zajac, D. et al. Spectators Errors in Multiqubit Tunable Coupling Architectures. APS March Meeting 2021. ↩
Mamin, H. Design and Characterization of a Functional Merged Element Transmon. APS March Meeting 2021. ↩
Jurcevic, P. et al. Increasing Quantum Volume on a superconducting quantum computing system. APS March Meeting 2021. ↩
Maslov, D. Quantum advantage for computations with limited space. APS March Meeting 2021. ↩
Gujarati, T. et al. Reducing circuit size in the variational quantum eigensolver -- Part 1: Theory. APS March Meeting 2021. ↩
Eddins, A. et al. Reducing circuit size in the variational quantum eigensolver -- Part 2: Experiment. APS March Meeting 2021. ↩
Corcoles, A. et al. Exploiting real-time classical resources in a quantum algorithm. APS March Meeting 2021. ↩
Takita, M. et al. Operating a logical-qubit size system of superconducting qubits with a heavy-hexagon layout. APS March Meeting 2021. ↩