IBM Research’s journey 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 with error mitigation techniques.

I recently had the opportunity to visit IBM’s Quantum Research Labs in Yorktown Heights, New York to speak with Jay Gambetta, IBM Fellow and VP of Quantum Computing, IBM Research and his team working to advance quantum computing . IBM is making steady progress on its quantum roadmap (see previous article), but this is still a nascent technology and much experimentation remains to advance the quantum computing market, which is why research is so important.

IBM researchers aim to make quantum computing as ubiquitous as possible to solve unique problems. To make quantum systems more accessible, they need to become “cloud native” or “serverless” by becoming a cloud resource that is billed by usage. In this era of disaggregated data centers, quantum can be one of the specialized compute elements available to classical computers, much like GPUs are today.

With the goal of scaling quantum systems to over 1 million qubits, IBM Research is taking a similar path to that of classic computers: bringing more and faster qubits onto a chip using silicon scaling; connect multiple quantum chips as tiles; and build clusters of quantum computers that work together. I previously wrote about IBM’s roadmap to build systems with more qubits and to interconnect multiple quantum systems.

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

IBM has also worked to increase the density of the cryogenic infrastructure for input and output of radio frequency signals using commercial technology.

Quantum Computing Journey to Quantum Advantage

While the goal is to build systems with millions of raw qubits for fault-tolerant quantum computing, in the meantime there is much work to be done to improve the performance of raw qubits to get more work done sooner using quantum error mitigation, as shown in the picture below.

To get better quantum results with today’s relatively noisy and short-lived qubits, some workarounds are needed. IBM Research has developed some error mitigation techniques that have proven useful. Current quantum hardware is subject to various sources of noise degradation. These include qubit decoherence, single gate errors, and measurement errors. These issues limit the number of stages that can be implemented in a quantum circuit today. Even flat circuits can be subject to noise that can lead to erroneous estimates. For a deeper discussion on bug mitigation, IBM recently published a blog post.

The noise abatement techniques are very technical. From a 2017 IBM Review Letter Error Mitigation for Short-Depth Quantum Circuits to the American Physical Society: “The first method, extrapolation to the zero noise limit, then cancels the powers of the noise interference by applying Richardson’s delayed approach to the limit . The second method erases errors by resampling randomized circuits according to a quasi-probability distribution.” Again, it’s technical, but the IBM researchers can hide the details within the Qiskit runtime software environment.

The ultimate goal for practical quantum computing is to offer an advantage over classical computing in order to solve significant problems in a reasonable time frame. The most obvious benefit is solving the problem in a much shorter time. To achieve this, the problem must be represented as a quantum circuit and not simulated on a classical system, which means that quantum computing will not replace classical computing or even GPU computing, but are here to solve a unique class of problems.

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

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

There is still much that can be achieved by combining classical computing and quantum technology. In so-called circuit knitting, quantum circuits are broken down into smaller circuits and use classic computing to evaluate intermediate results. This can leverage the quantum workflow over the available qubits.

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

IBM is continually 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 quantum middleware stack, leveraging experience from both classical and GPU computation. IBM moved away from a static language and added dynamic circuits, where the output of measurements taken midway through the circuit is used to define future gates in the same circuit. Recent developments include support for conditional circuits in the Open QASM3 quantum development platform.

Quantum circuit programming challenges include optimizing the quantum circuit depth, finding alternative models, and adding a parity check for quantum error corrections. IBM also adds more function calls and core primitives: Sampler and Estimator. These additions help reduce development times. The result is higher accuracy and lower costs due to shorter circuit delays.


The future of quantum computing is upon us, and much of the work is improving the quality of qubits, not just increasing the number of qubits. It requires a full systems approach to building quantum systems with hardware, middleware and libraries. We expect to see more interaction between quantum and AI processing in the near future as well.

Tirias Research follows and advises companies across the electronics ecosystem, from semiconductors to systems and sensors to the cloud. Tirias research team members have advised IBM, Nvidia, Qualcomm and other companies across the AI ​​and Quantum ecosystem.

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