Posts Tagged ‘calibration two-qubit gates’

IBM Roadmap for Quantum Computing

September 18, 2020

IBM started quantum computing in the cloud a few years back by making their small qubit machines available in the cloud and even larger ones now. In mid September, IBM released its quantum computing roadmap to take it from today to million-plus qubit devices. The first benchmark is a 1,000-plus qubit device, IBM Condor, targeted for the end of 2023. Its latest challenge: going beyond what’s possible on conventional computers by running revolutionary applications across industries.

control lots of qubits for long enough with few errors

The key is making quantum computers stable by keeping them cold. To that end IBM is developing a dilution refrigerator larger than any currently available commercially. Such a refrigerator puts IBM on a course toward a million-plus qubit processor. 

The IBM Quantum team builds quantum processors that rely on the mathematics of elementary particles in order to expand its computational capabilities running quantum circuits. The biggest challenge facing IBM’s team today is figuring out how to control large systems of qubits for long enough, and with minimal errors, to run the complex quantum circuits required by future quantum applications.

IBM has been exploring superconducting qubits since the mid-2000s, increasing coherence times and decreasing errors to enable multi-qubit devices in the early 2010s. Continued refinements allowed it to put the first quantum computer in the cloud in 2016. 

Today, IBM maintains more than two dozen stable systems on the IBM Cloud for clients and the general public to experiment on, including the 5-qubit IBM Quantum Canary processor and its 27-qubit IBM Quantum Falcon processor,  on which it recently ran a long enough quantum circuit to declare a Quantum Volume of 64, an IBM created metric. This achievement also incorporated improvements to the compiler, refined calibration of the two-qubit gates, and upgrades to the noise handling and readout based on tweaks to the microwave pulses.

This month IBM quietly released its 65-qubit IBM Quantum Hummingbird processor to its Q Network members. This device features 8:1 readout multiplexing, meaning it combines readout signals from eight qubits into one, reducing the total amount of wiring and components required for readout and improving its ability to scale.

Next year, IBM intends to debut a 127-qubit IBM Quantum Eagle processor. Eagle features several upgrades in order to surpass the 100-qubit milestone: through silicon vias, which allow electrical signals to pass through the substrates to enable smaller device sizes and a reduced signal path and multi-level wiring to effectively fan-out a large density of conventional control signals while protecting the qubits in a separate layer in order to maintain high coherence times. The qubit layout will allow IBM to implement the heavy-hexagonal error-correcting code that its team debuted last year, as it scaled up the number of physical qubits and error-corrected logical qubits.

These design principles established for its smaller processors will set it on a course to release a 433-qubit IBM Quantum Osprey system in 2022. More efficient and denser controls and cryogenic infrastructure will ensure that scaling up the processors doesn’t sacrifice the performance of the individual qubits, introduce further sources of noise, or take too large a footprint.

In 2023, IBM intends to debut the 1,121-qubit Quantum Condor processor, incorporating the lessons learned from previous processors while continuing to lower the critical two-qubit errors so that it can run longer quantum circuits. IBM presents Condor as a milestone that marks its ability to implement error correction and scale up devices while simultaneously complex enough to solve problems that can be solved more efficiently on a quantum computer than on the world’s best supercomputers, achieving the quantum Holy Grail.

Alan Radding, a veteran information technology analyst, writer, and ghost-writer, is DancingDinosaur. Follow DancingDinosaur on Twitter, @mainframeblog, and see more of his work at

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