Quantum computing hardware on black background

Quantum Computing Hardware Development Challenges in 2020


In today’s technology landscape, things move quickly. One day, we’re laser-focused on hardware for embedded AI, the next we’re focused on photonic integrated circuits. A recent announcement by Honeywell claiming to have produced the most powerful quantum computer now puts the spotlight back on, well, quantum computers...

This deserves some more attention as quantum computing is starting to come out of the shadows of research labs and into the public with news-making achievements. These systems are still quite esoteric and are often misunderstood. In some cases, like many new technologies, these systems evoke outright fear due to popular misconceptions. Despite the misconceptions, quantum computers are so specialized, they will likely never be available in desktop form, and greater commercialization requires addressing many quantum computing hardware challenges first. Amidst the challenges lie opportunities for electronics, photonics, and algorithmic engineers of all stripes.

Measuring Quantum Computing Power

Before diving into the current set of challenges surrounding quantum computers, it’s important to think about just what it means for a quantum computer to be "powerful." In the media, the power of a quantum computer is measured by its qubit count as its the closest possible analogue to transistor count in classical processors. This is not the correct measure of quantum computing power; a truly meaningful measure of power in quantum computing hardware reveals the challenges that lie ahead in creating more powerful quantum computers.

With Honeywell’s recent announcement that their quantum computer is the most powerful to date, most in the tech world expect it to be packed with qubits. In fact, it has only 6 qubits (compare this to Google’s Sycamore, which has 53 qubits). Despite the low qubit count, a different isolation method is used, which ensures a quantum state in Honeywell’s system has longer coherence time. Other important measures of quantum computing power include bit error rate and power efficiency. A simple method for calculating quantum volume in Python can be found at QSikit. In addition, the yardsticks used to measure the power of a quantum annealing computer or quantum simulation computer may not be so useful for a general-purpose quantum computer.

Since there’s obviously more to quantum computing than qubit count, we can identify a few key areas where quantum computing hardware needs to advance:

Scalable Qubit Architecture

If you look at the inside of a quantum computer (see the image above), it becomes quite obvious that these systems are inherently unscalable. This was described by Jim Clark in a 2019 IEEE Spectrum article as being a problem of "too many wires." Jim is right on the mark, which actually highlights the solution: integration. A change to different read/write method alongside a change in the qubit architecture could aid scaling and integration (see below).

When I say integration here, I’m really referring to the ability to place multiple components (qubits, interconnect circuitry, superconducting coaxial lines, and any photonic circuits) all on the same chip. Making these systems inherently smaller doesn’t mean they are easier to cool. In fact, the very act of packing more functions onto a chip eventually causes the system to hit a "heat wall," which is currently being felt in the embedded AI world. This then leads us to the next challenge in quantum computing hardware:

Qubit architecture in quantum computing hardware

Just like conventional computing components and circuitry, these example quantum dot qubits have transient responses that settle to a final state over time.


The qubits in a quantum processor need to be heavily isolated from light and heat, both of which will cause decoherence in a quantum state in a shorter amount of time. Physically, light at certain energies can cause a qubit to transition between states, while heat effectively broadens the distribution of possible energies within a state (this is one reason why we have a gain spectrum in lasers). This coupling with the environment needs to be prevented to ensure a qubit stays in its written state for as long as possible.

If isolation in the quantum section of the system is increased, then there is less work for the refrigeration control system to do to keep the system at low temperature. Aside from improving refrigeration systems themselves, different qubit architectures also ensure different levels of isolation. Again, look at the Honeywell computer; their system has higher quantum volume because it has greater isolation. Their system uses trapped ions rather than entangled qubits, spintronics, photonic qubits, defect states, or other types of qubits.

Accurate Readout

The readout problem in quantum computing hardware has the potential to introduce errors, just like in any other hardware system. The use of RF mode combs for clocking and mmWave signals for qubit manipulation (in non-photonic or mixed signal systems) always creates the potential for crosstalk if the RF front end of the readout system is not designed properly. PCB designers have a role to play here in ensuring readout signals are not corrupted as the system operates.

Quantum Computing Hardware Opportunities

Despite the challenges involved, there are real opportunities for innovators to create solutions in this field. The following groups can find opportunities hidden within the challenges mentioned above:

  • PCB designers. The electronics used for system control, readout, and interfacing with the cloud will need to be placed on a circuit board, just like any other electronic system.

  • Chipmakers. The major semiconductor companies are probably already working on integration for quantum computers beyond simple qubits. Currently, the readout and control systems are entirely built from custom components. This will continue to be the case until major chipmakers start releasing SoCs to help reduce part counts and board sizes.

  • Mechanical engineers. Because the main column in a quantum computer is basically a big vacuum system, mechanical engineers have a role to play in ensuring these systems operate at peak efficiency and can be somewhat miniaturized over time.

SEM image quantum computing hardware

This qubit is already quite, now the remaining hardware involved in writing and readout needs to be shrunk down to the same scale.

In addition to hardware development and integration at the PCB, chip, and system levels, fundamental quantum algorithms and system architecture will play major roles in driving commercialization of quantum computers. At some point, get ready to see the quantum analogue of firmware for new quantum hardware platforms.


At NWES, we spend our time helping innovators in private industry and government confront and solve the world’s toughest technical challenges. We’re here to help companies in the electronics industry design high speed, high frequency, and high density PCBs for a variety of applications, including quantum computing hardware. We've also partnered directly with EDA software companies and advanced PCB manufacturers, and we'll make sure your next layout is fully manufacturable at scale. Contact NWES today for a consultation.


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