# IBM Research’s path to serverless quantum computing

**Summary:** IBM is on the way to fault-tolerant quantum computing, but before we get to that holy grail of quantum computing, there’s a lot of useful work that can be done using error-mitigation techniques. .

I recently had the chance to visit IBM’s Quantum Research Laboratories in Yorktown Heights, New York, to chat with Jay Gambetta, IBM Fellow and Vice President of Quantum Computing, IBM Research, and his team working to advance the quantum computing. IBM is making steady progress on its quantum roadmap (reference previous article) but it is still a nascent technology and there is still a lot of experimentation needed to advance the quantum computing market, that is why research is so important.

The goal of IBM researchers is to make quantum computing as ubiquitous as possible to solve unique problems. To make quantum systems more accessible, they must become “cloud-native” or “serverless,” in the sense that they become a cloud resource billed according to usage. In the era of disaggregated data centers, quantum may be one of the specialized computing elements available to mainstream computers, much like GPUs are today.

In an effort to scale quantum systems beyond 1 million qubits, IBM Research is following a path similar to that taken with classical computers: putting more and faster qubits on a chip using scaling. at the silicon scale; interconnect several quantum chips in the form of tiles; and build clusters of quantum computers working together. I’ve written before about IBM’s roadmap for building systems with more qubits and connecting multiple quantum systems together.

The cryostat (the chamber that cools the quantum chip to near absolute zero) also needed to expand to accommodate the larger chips with more I/O. IBM has partnered with Blue Force to help build an ecosystem needed for larger System 2 cryostats.

IBM has also worked on increasing the density of cryogenic infrastructure for the input and output of radio frequency signals by leveraging commercial technology.

**Quantum Computing’s Journey to Quantum Advantage**

While the goal is to build systems with millions of raw qubits for fault-tolerant quantum computing, there is a lot of work that can be done in the interim to improve the performance of raw qubits to do more work earlier using quantum error mitigation as shown in the figure below.

To get better quantum results using today’s relatively noisy, short-lived qubits requires a few workarounds. IBM Research has developed a few error mitigation techniques that are helpful. Current quantum hardware is subject to different sources of noise degradation. This includes qubit decoherence, individual gate errors, and measurement errors. These problems limit the number of stages that can be implemented in a quantum circuit today. Even shallow circuits can be subject to noise which can lead to erroneous estimates. For a more in-depth discussion of error mitigation, IBM has published a blog post recently.

Noise abatement techniques are very technical. Excerpt from an IBM review letter from 2017 Error mitigation for quantum circuits at shallow depth to the American Physical Society: “The first method, extrapolation to the zero noise limit, then cancels the powers of the noise disturbances by an application of Richardson’s deferred approach to the limit. The second method cancels errors by resampling randomized circuits according to a quasi-probability distribution. Like I said, it’s technical, but IBM researchers can hide the details in the Qiskit Runtime software environment.

The ultimate goal of practical quantum computing is to provide an edge over classical computing to solve important problems in a reasonable time. The most obvious advantage is to solve the problem in much less time. To do this, the problem must be represented as a quantum circuit and not simulated on a classical system, which means that quantum computers are not going to replace classical computers or even GPU computing but are there to solve a unique class of problems .

For the quantum computer to have an advantage over the classical computer (so-called quantum advantage), one must map the problem to quantum circuits with solutions that are better than classical approaches and be able to obtain reliable results with faster turnaround times. IBM researchers work with industry partners to identify problems that require better solutions.

To measure progress, IBM has a measure of qubit quality called Quantum Volume (QV) and circuit speed called Circuit Layer Operations Per Second (CLOPS). These provide a more complete picture of advances in quantum computing than just pure qubit numbers.

There is still a lot to be done by mixing classical and quantum computing. With a process called circuit knitting, quantum circuits are broken down into smaller circuits and leverage classical computing to evaluate intermediate outcomes. This can take advantage of quantum workflow on available qubits.

One application of mixing classical computing and quantum computing is computational chemistry. To calculate the electron valence, the electron cloud can be divided into inactive and active parts. The inactive cloud is calculated with classical computers and the active part of the cloud uses a quantum mechanical modeling method called Density-Functional Theory (DFT).

IBM is continuously working with partner companies to explore areas where quantum computing can make a difference in solving difficult problems.

**Quantum Needs Software**

IBM Research is also building a middleware stack for quantum, using lessons learned from classical and GPU computing. IBM moved from a static language and added dynamic circuits, where the output of measurements taken at mid-circuit is used to define future gates in the same circuit. Latest developments include support for conditional circuits in the Open QASM3 quantum development platform.

Programming challenges for quantum circuits include optimizing quantum circuit depth, finding alternative models, and adding parity checking for quantum error corrections. IBM is also adding more basic function calls and primitives: sampler and estimator. These additions help to shorten development times. The results are improved accuracy and reduced costs by reducing circuit run times.

**Summary**

The future of quantum computing is approaching and much of the work is about improving the quality of qubits, not just increasing the number of qubits. It takes a complete systems approach to building quantum systems with hardware, middleware, and libraries. We also expect to see more interaction between Quantum and AI processing in the near future.

_{Tirias Research follows and advises companies across the entire electronics ecosystem, from semiconductors to systems and sensors to the cloud. Members of the Tirias research team consulted with IBM, Nvidia, Qualcomm and other companies in the AI and Quantum ecosystems.}

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