Quantum Motion

(This profile is one entry in my 2025 series on quantum hardware roadmaps and CRQC risk. For the cross‑vendor overview, filters, and links to all companies, see Quantum Hardware Companies and Roadmaps Comparison 2025.)

Introduction

Quantum Motion is a London-based quantum computing company pioneering a silicon-based approach to building scalable quantum computers. Founded in 2017 by UCL’s Prof. John Morton and Oxford’s Prof. Simon Benjamin, the startup spun out of those universities to harness traditional CMOS semiconductor technology for quantum processors.

Unlike many competitors that rely on exotic fabrication, Quantum Motion’s strategy is to use industry-standard 300 mm silicon wafers and manufacturing processes – the same foundry technology behind today’s computer chips – to create quantum bits (qubits) in silicon transistors. By leveraging ubiquitous CMOS techniques, Quantum Motion aims to mass-produce qubit arrays on chips, dramatically improving scalability and cost-effectiveness.

The company’s leadership includes CEO James Palles-Dimmock, with a background in semiconductor and HPC industries, and CTO John Morton (co-founder), among others. In 2023 Quantum Motion raised a Series B round of £42 million (≈$50 million) led by Bosch and Porsche, bringing total funding to over £62 M. This capital, along with UK government grants and EU projects, has fueled a rapid expansion – the team grew from ~60 quantum engineers in early 2025 to over 100 staff across the UK, US, Australia and Spain by late 2025. Today, Quantum Motion is recognized as one of the UK’s leading quantum hardware ventures, with globally significant milestones in silicon quantum computing, including delivering the world’s first full-stack CMOS quantum computer in 2025.

At its core, Quantum Motion’s vision is to achieve fault-tolerant, utility-scale quantum computers by marrying quantum bit devices with classical control circuits on the same silicon chip. Their qubits are electron spins in silicon quantum dots – essentially tiny transistor-like structures that can hold single electrons whose spin states serve as qubits. This modality promises long-term integration advantages: billions of such quantum dot qubits could be patterned on a chip no larger than a postage stamp. Crucially, Quantum Motion develops custom cryogenic CMOS electronics that operate near absolute zero, generating and processing signals at deep cryogenic temperatures to control large qubit arrays. By co-designing the quantum processor and its control hardware, the company seeks to avoid the bottleneck of thousands of wires that plague many quantum machines. The approach has yielded record-setting results – for example, Quantum Motion designed a silicon chip (“Bloomsbury”) with 1,024 quantum dots in <0.1 mm², and demonstrated rapid automated characterization of all devices in under 5–10 minutes. This was at least 100× faster than prior state-of-the-art and proved that large qubit arrays can be uniformly manufactured and tuned using standard processes. Such achievements, published in peer-reviewed journals, underline how silicon could be “the fastest, most cost-effective and scalable way of producing the millions of qubits needed” for fault-tolerant quantum computing.

Today, Quantum Motion’s headquarters and lab are in London, with new offices opened in the US (San Francisco) and Australia to support global expansion. The company’s progress is bolstered by partnerships with major industry players – notably a semiconductor alliance with GlobalFoundries (GF). In 2024–25, GF fabricated Quantum Motion’s chips on its 22 nm FD-SOI process, validating that quantum devices can be built in a commercial foundry with features like on-chip multiplexers and bias tuning for 1 K operation. Strategic investors such as Bosch (automotive) and Porsche SE signal interest in the applied potential of Quantum Motion’s technology (e.g. for battery materials, chemistry and logistics optimization). With strong institutional backing (including the UK’s National Security Strategic Investment Fund) and academic ties, Quantum Motion blends cutting-edge quantum science with real-world engineering.

The delivery in September 2025 of a full-stack quantum computer to the UK National Quantum Computing Centre (NQCC) stands as a major validation of their approach.

Milestones & Roadmap

Quantum Motion has methodically transitioned from foundational R&D to deployed prototypes in just a few years. Key milestones on its roadmap include:

• 2017–2019: Company founded (2017) with the goal of CMOS-based qubits. Early work focused on proving that standard silicon transistors can function as quantum dots at millikelvin temperatures. By 2019, the team demonstrated single-spin qubit operations in silicon MOS devices, leveraging academic labs at UCL/Oxford.
• 2020–2021: First cryo-CMOS integration breakthroughs. Quantum Motion and collaborators reported a cryo-CMOS chip integrating silicon quantum dots with multiplexed readout. This early prototype showed that control electronics (like amplifiers/multiplexers) could operate at <1 K alongside qubits – a critical step toward on-chip scaling. Around the same time, they achieved high-fidelity single-qubit control (>99%) and their first two-qubit entangling gates (~98% fidelity) in a foundry-fabricated deviceg. These results, world-leading for silicon qubits on 300 mm wafers, established a baseline for building small quantum processors.
• 2022–2023: Emphasis on scalability demonstrations and funding. Quantum Motion designed the “Bloomsbury” chip with 1,024 quantum dot sites, and in 2022–23 developed fast automated tuning techniques using RF reflectometry and machine learning to measure thousands of dots efficiently. This work culminated in a Nature Electronics paper (Jan 2025) confirming that large silicon qubit arrays are compatible with commercial CMOS processes. To accelerate development, the company closed a £42 M ($50 M) Series B round in Feb 2023, led by Bosch Ventures and Porsche SE. This brought total funding above £62 M and enabled tripling the size of its London HQ and deeper ties with manufacturing partners. By late 2023, Quantum Motion had firmly established a “silicon first” reputation, with Bosch noting that “Quantum Motion has demonstrated it can take quantum theory out of the lab into the real world to create a scalable path to a quantum future.”
• 2024: GlobalFoundries partnership & DARPA selection. In January 2024, Quantum Motion publicly announced its partnership with GlobalFoundries, which had already fabricated its 1k-dot chips. GF’s 22FDX process – supporting operation down to 1 K – was shown to produce quantum structures with at least 100× faster characterization than previous methods. This milestone demonstrated that a tier-one semiconductor fab can manufacture qubit chips at scale, bridging quantum R&D with industrial production. In parallel, Quantum Motion was one of 18 companies selected by the U.S. Defense Advanced Research Projects Agency for the Quantum Benchmarking Initiative (QBI) in mid-2024. QBI Stage A tasked participants to present credible roadmaps for “utility-scale” quantum computers by 2033. Quantum Motion’s inclusion signaled external validation of its long-term plan for a fault-tolerant silicon quantum architecture.
• 2025: Full-stack quantum computer deployment and R&D advances. 2025 has been pivotal. In April, the company hired Hugo Saleh (ex-Google/Intel) as President & CCO to drive productization and global expansion – coinciding with incorporation in the US and new offices in Europe/Australia. In September 2025, Quantum Motion delivered the industry’s first quantum computer built using standard silicon chips, installed at the UK NQCC. This full-stack system integrates a quantum processing unit (QPU) based on Quantum Motion’s silicon spin qubits with a complete control and software stack (compatible with common frameworks like Qiskit and Cirq). Remarkably, the entire setup fits in three 19-inch racks (including a dilution refrigerator plus all control electronics) – a data-center-friendly footprint. The QPU uses a scalable “tile” architecture: all necessary qubit, readout, and control elements are packed into a repeated unit cell on the chip, allowing future versions to simply print more tiles for higher qubit counts. This design means the NQCC system can be upgraded by swapping in next-generation chips (with more qubits) without changing the cryostat or rack infrastructure. The NQCC installation – the first silicon spin-qubit machine on the testbed – is now being used to evaluate real-world applications on Quantum Motion’s hardware. UK Science Minister Lord Vallance hailed it as “another step closer to commercial viability”, noting potential impacts on drug discovery and energy grids. In November 2025, DARPA advanced Quantum Motion to QBI Stage B, one of just 11 companies to progress. This decision, after rigorous assessment, means DARPA found the company’s silicon-based, fault-tolerant design credible for pursuing a utility-scale quantum computer by the 2030s. Also in late 2025, Quantum Motion and UCL began the SiQEC (Silicon Quantum Error Correction) project, a UK-funded effort to demonstrate repeated quantum error correction cycles on a spin-qubit platform – an important stepping stone toward fault tolerance.
• Near-future (2026–2030): While the company closely guards detailed timelines, its public commitments and partnerships hint at an ambitious roadmap. The SiQEC project aims to show a rudimentary fault-tolerant unit cell by ~2026, using a 2×3 qubit array with fast parity-check measurements and shuttling of spins between quantum dots. If successful, this would be the first demonstration of two-dimensional error-correcting code cycles in silicon, plus the first high-fidelity movement of qubits across a chip (important for linking larger arrays). By the late 2020s, Quantum Motion plans to connect many such cells into a larger processor. Company leadership has stated an objective to bring “commercially useful quantum computers to market this decade” – implying a target of by ~2030 for a system capable of solving meaningful problems. Participation in DARPA’s QBI suggests a goal of a utility-scale, fault-tolerant machine by 2033 at the latest. In practice, we can expect incremental scaling: e.g. a prototype with on the order of 50–100 physical qubits by ~2027 (demonstrating a small logical qubit or simple error-corrected circuit), then perhaps a thousand-qubit silicon processor around 2028–30 if manufacturing and error rates improve. Each generation will be deployed into the existing Quantum Motion system architecture (benefiting from the data-center form factor and upgradeable design proven at NQCC). By 2030, the company could feasibly reach hundreds of logical qubits if its fault-tolerance techniques mature – putting it on a competitive footing with larger players’ roadmaps. Beyond 2030, the long-term vision is a million+ qubit universal quantum computer built from tiled silicon chips, delivering fault-tolerant performance for problems like cryptography, materials science, and AI.

Focus on Fault Tolerance

From the outset, Quantum Motion’s approach has been laser-focused on fault tolerance and error correction. Rather than build intermediate “NISQ” devices with tens of noisy qubits, the company’s philosophy – much like PsiQuantum’s in photonics – is to design everything with scalable, error-corrected quantum computing in mind. Silicon spin qubits were chosen not for near-term power, but for their long-term potential to be fabricated by the millions, which is widely believed to be necessary for a fault-tolerant universal quantum computer. Quantum Motion’s recent projects directly target the prerequisites for fault tolerance:

• Error-Correcting Architecture: The SiQEC initiative (2025–26) will implement a small quantum error-correcting code using silicon spin qubits. Specifically, they are building a unit cell with two data qubits plus measure and ancilla qubits in a 2×3 quantum dot layout. This cell can perform quantum non-demolition parity measurements to catch bit-flip or phase-flip errors without destroying the encoded quantum information. Repeated rounds of such parity checks – the hallmark of quantum error correction (QEC) – will be demonstrated, marking the first time silicon qubits undergo multiple QEC cycles. The project is also pioneering spin qubit shuttling, physically moving electron spins between dots, to enable connectivity across multiple cells. If successful, these two advances (2D QEC code operation and reliable qubit transport) would be breakthroughs: “the first demonstration of repeated QEC in silicon… and of high-fidelity spin shuttling in silicon MOS devices”. They are essential steps toward a modular, tileable fault-tolerant architecture where many such cells connect into a large error-corrected network.
• Cryoelectronics & Integration: A key pillar of Quantum Motion’s fault-tolerance strategy is integrating classical control circuits with the qubits at cryogenic temperatures. To perform QEC, thousands of measurements and feedback operations per second are needed, which is infeasible if every qubit is wired out to room-temperature electronics. Quantum Motion has therefore developed cryo-CMOS logic that operates at ~1–4 K inside the refrigerator, extremely close to the qubits. For example, the 1024-dot “Bloomsbury” chip included on-chip high-frequency multiplexers that drastically reduced the number of external lines, yet allowed fast parallel readout of qubit arrays. The company’s published results show automated tuning algorithms can extract qubit parameters from these large arrays, using machine learning to characterize yield and device variability – important for identifying and routing around faulty qubits in an error-corrected system. By drawing on techniques from classical chip testing and self-calibration, they aim to make a future silicon quantum processor “plug-and-play”, where even thousands of qubits can be managed without human tweaking. On-chip readout and control also minimize latency for error correction: a classical logic layer at cryo can process syndrome measurements and apply qubit corrections far faster than if signals had to travel to room temperature and back. Overall, this integration is about achieving the “extreme scaling” needed for fault tolerance. As CEO James Palles-Dimmock put it, “the clearest path to practical, large-scale QC is through silicon… leveraging the same CMOS tech as modern electronics” – meaning the path to fault tolerance is to build error-corrected qubits using the very infrastructure that scaled classical bits to billions.
• Focus on High Fidelity & Uniformity: Quantum Motion has been steadily improving the gate fidelities and uniform control of its qubits, knowing that fault tolerance demands error rates below certain thresholds (~10^-3 or better). They reported single-qubit gate fidelities > 99% and two-qubit (exchange) gate fidelities of ~98% on natural silicon devices – the latter a record achievement on a 300 mm process. Those numbers are already within striking distance of error-correcting thresholds (especially with future material improvements like isotopically enriched ²⁸Si, which can reduce magnetic noise). The variability of qubit device parameters across a large chip is another challenge for uniform error correction. Here, Quantum Motion’s data-driven approach helps: their studies found correlations between room-temperature transistor behavior and low-temperature quantum dot performance. This means chip fabrication can be optimized (or screened) using standard electrical tests to ensure more consistent qubits – a huge advantage of using CMOS technology. In essence, by emphasizing high fidelity operations and uniform mass-manufacturing, Quantum Motion is laying the groundwork for qubits that behave predictably in the tens of thousands or more, a necessity for any fault-tolerant quantum computer.

In summary, Quantum Motion’s entire program is architected around achieving fault tolerance. Rather than treat error correction as a distant add-on, they are building it into the hardware now: from qubit connectivity for logical qubits, to cryo-control for fast feedback, to ensuring every qubit on a chip can be calibrated and monitored efficiently. This philosophy mirrors the thinking of larger players like IBM (which targets a million-qubit error-corrected machine by ~2030) and PsiQuantum (aiming for a million-photon fault-tolerant machine). Quantum Motion, with its silicon-first approach, is bringing a new modality into the fault-tolerance race – one that could prove to be highly scalable if the current momentum continues.

CRQC Implications

Because Quantum Motion is targeting large-scale, error-corrected quantum computing, its progress will directly influence the timeline for Cryptographically Relevant Quantum Computing (CRQC) – i.e. quantum machines capable of breaking today’s cryptography (often called “Q-Day” when such capability emerges). If this UK startup succeeds in building a fault-tolerant silicon quantum computer on the aggressive schedule it envisions, it could accelerate the arrival of Q-Day or at least broaden the front of who might achieve it.

Current estimates suggest that breaking a strong encryption like RSA-2048 would require on the order of 1,400 logical qubits (with very low error rates) to factor the number in a reasonable time (e.g. within days or weeks). That translates to perhaps a few million physical qubits if using standard error correction codes and today’s error rates. The world’s largest quantum efforts (IBM, Google, etc.) are hoping to reach hundreds of logical qubits by ~2030, which likely won’t be enough to crack RSA-2048 outright, but would be uncomfortably close to that ballpark. Quantum Motion’s roadmap – achieving a utility-scale, fault-tolerant system by the early 2030s – fits into this global timeline. In fact, DARPA’s QBI program (in which Quantum Motion is a participant) explicitly asks: can any approach deliver a useful, cost-effective quantum computer by 2033? Quantum Motion being among the finalists indicates yes, their silicon-based approach is considered one of the credible paths to a CRQC-capable machine within the next ~8–10 years.

If Quantum Motion meets its milestones, the Q-Day timeline could indeed be pulled forward or made more certain. For instance, should they demonstrate even on the order of ~100 logical qubits by 2029–2030, that achievement – coming from a smaller startup outside Big Tech – would signal that multiple independent hardware paths are reaching CRQC-relevant scales. Quantum Motion’s plan aligns the UK-government-backed goal of “1 trillion quantum operations in error-corrected systems by 2035” which implies a large, stable quantum computer by that date.

Early evidence of fault tolerance in silicon – such as a working error-corrected qubit or small logical register within a few years – would reinforce that CRQC is no longer a distant theoretical possibility but an engineering project nearing fruition. It’s notable that Quantum Motion provides a non-U.S., CMOS-centric route to CRQC, complementing efforts by U.S. tech giants and other modalities.

Just as Europe (with IQM, etc.) is pursuing superconducting and photonic quantum systems, the UK via Quantum Motion could ensure that silicon spin qubits are also in the mix of technologies racing toward cryptographic disruption. This diversification increases the chances that at least one approach will hit the CRQC threshold on an aggressive timetable.

In practical terms, if Quantum Motion (and peers) succeed with fault-tolerant designs by ~2030, we might see RSA-2048 factoring by the early- to mid-2030s. Even if their first logical qubits are used for chemistry or optimization applications, the mere fact of achieving hundreds of stable logical qubits would mean that scaling to break cryptography is “only” an order-of-magnitude away. In that scenario, Q-Day could arrive swiftly once engineering resources are directed at cryptographic algorithms.

Modality & Strengths/Trade-offs

Quantum Motion’s technology modality is gate-based spin qubits in silicon. Each qubit is an electron trapped in a silicon quantum dot, whose spin-up/spin-down states represent the |1⟩ and |0⟩ states of a quantum bit. These quantum dots are implemented using standard CMOS transistor structures – essentially, tiny MOSFET-like devices patterned on a silicon chip. By leveraging well-established silicon fabrication, these qubits are extremely small: the company fit 1,024 of them in 0.1 mm² in one chip, meaning millions could, in principle, reside on a single wafer. Qubits are controlled via gate electrodes (to tune quantum dot potentials) and possibly microwave pulses for spin rotations, and are read out through charge or spin-sensitive circuits (often via RF reflectometry through a sensor dot). This solid-state, all-electrical control is a key differentiator from, say, superconducting qubits that require microwave resonators or ion traps that need lasers. In short, Quantum Motion’s platform is akin to building a quantum processor that looks much like a classical microchip, except operated at millikelvin temperatures.

Strengths

The most obvious strength of silicon spin qubits is scalability through manufacturing. Quantum Motion’s qubits piggyback on the trillion-dollar semiconductor industry: they can use existing 300 mm fabs, design CAD tools, and integration techniques developed for classical chips. This gives a path to mass production that other approaches struggle to match. The company explicitly notes its quantum chip can be made in a commercial foundry with “robust and reliable processes, paving the way for high-volume production of quantum chips”. In practical terms, once the qubit design is optimized, thousands of identical quantum processor chips could be printed, drastically reducing cost per qubit – a crucial factor for scaling to millions of qubits.

Another strength is the compact integration of classical and quantum electronics. Silicon spin qubits can be co-fabricated with CMOS transistor circuits on the same die or stack, allowing Quantum Motion’s approach to include on-chip multiplexers, amplifiers, and eventually even error-correcting logic. This tight integration yields systems like the NQCC prototype, where the entire quantum computer (qubits + control) fits in a few racks. Competing modalities often require bulky infrastructure (superconducting qubits need extensive cryogenic wiring and separate control hardware; ion trap systems need optical tables and vacuum systems). By contrast, a silicon quantum processor in a fridge can be quite physically small – potentially scaling without enlarging the footprint, as Quantum Motion’s tile-able design exemplifies. This “data-center friendly” form factor is a big plus for real-world deployment: it’s conceivable to install a silicon-based quantum accelerator alongside classical servers in an HPC center, as the company is already doing (e.g. their systems at NQCC and planned deliveries to other labs).

Silicon spin qubits also benefit from long coherence times and high fidelities observed in research. In isotopically purified silicon (Si-28), single-electron spins can maintain coherence for seconds to minutes (with decoupling techniques), far longer than superconducting qubits’ microseconds. Even in natural silicon, Quantum Motion has achieved gate fidelities around 99%, as noted. These robust qubits owe much to decades of silicon transistor development: extremely low-noise materials, precise fabrication, and the ability to operate many qubits in parallel. Additionally, thermal management is a relative strength – while the qubits need ~20 mK temperatures, the use of FD-SOI technology that works at 1 K for control means much of the heat load can be handled at higher temperature stages, keeping the cooling requirements feasible. The fact that their initial system packs the fridge into a single rack is testament to efficient thermal engineering, suggesting that even as qubit count grows, they may not require vastly larger dilution refrigerators (they’ve essentially “shrink-wrapped” the quantum hardware into a server form factor).

Furthermore, industrial and strategic support is a strength that, while not technical per se, bolsters the modality: being aligned with the semiconductor industry means companies like Bosch, GF, and even government chip programs (like the US CHIPS Act, which funded GF’s fabs) have a stake in Quantum Motion’s success. This could translate into easier access to cutting-edge fabrication processes and maybe custom optimizations (e.g. special low-noise fab steps or isotopically enriched wafers at scale). It also means a talent pipeline from classical chip experts can feed into their team (as seen with hires like Hugo Saleh from Intel/Google). In short, Quantum Motion’s modality sits at the intersection of quantum tech and the huge existing ecosystem of CMOS microelectronics – arguably a very advantageous position for long-term scaling.

Trade-offs

The flip side of using silicon CMOS is that quantum performance can be limited by semiconductor imperfections. Unlike some pristine laboratory qubits, silicon devices have variability – threshold voltages, charge noise, interface defects – which can disturb delicate spin qubits. Quantum Motion’s need to calibrate 1,024 quantum dots and observe parameter spreads illustrates this challenge. They addressed it via fast characterization and observed correlations to transistor behavior, but nonetheless, scaling to millions of qubits means ensuring fab yield and uniformity at levels far beyond conventional chips (since a single bad transistor might disable a qubit). This is manageable with redundancy (e.g. having extra qubits, and the ability to route around bad ones), yet it adds complexity.

Another trade-off is the ultra-low-temperature operation. While Quantum Motion has minimized the physical footprint, the requirement of ~millikelvin temperatures for the qubits and ~1–4 K for the cryo-CMOS still means each quantum computer needs a dilution refrigerator. These are expensive and power-hungry pieces of hardware. Competing modalities like photonic qubits (room-temperature) or even ion traps (cryogenic but higher temperature) might have easier cooling in some respects. However, superconducting qubits share the dilution fridge requirement, so silicon qubits are in a similar boat there. The company’s integration of fridge + control in a rack shows they can make it as turn-key as possible, but deploying thousands of such fridges for huge quantum data centers might face practical limits. Quantum Motion will likely work on increasing operating temperature of some components – for instance, if qubits could operate at 1–4 K (as some SpinQon/SemiQon efforts suggest), then cheaper cryocoolers could be used. For now, though, the necessity of extreme cooling is a constraint and a cost factor.

Another challenge is that silicon spin qubits are still catching up in qubit count and multi-qubit operation. To date, academic groups have achieved on the order of 6–10 entangled spin qubits in a linear array. Quantum Motion’s own publicly disclosed experiments have been at the 2-qubit gate level so far (with the heavy lifting focused on large arrays of potential qubits that were characterized, but not yet all used simultaneously in computation). This lags behind superconducting and ion trap platforms that have demonstrated dozens or more qubits performing algorithms. The company is effectively betting that a leapfrog directly to many qubits (with error correction) will bypass the need to show, say, a 50-qubit NISQ computer. However, it remains unproven that spin qubits can be controlled in large entangled circuits as easily as those other modalities. There may be hidden hurdles in crosstalk, calibration time, or gate synchrony when scaling up. Quantum Motion’s heavy use of automated tuning and machine learning is intended to mitigate this, but it will be truly tested when they attempt a full multi-qubit algorithm on their hardware.

There’s also technical complexity in design: packing qubits and classical circuits together means dealing with electromagnetic interference (a switching transistor could decohere a nearby qubit if not properly isolated) and power dissipation (every microwatt dissipated at cryo is a load on the fridge). The GlobalFoundries chip results were promising in showing low static power dissipation for their multiplexer (essentially “less-than-detectable” leakage at cryo), but as they add more complex logic on chip (like error-correcting decoders), managing cryo power will be critical. Quantum Motion will need to innovate in ultra-low-power circuit design, perhaps leveraging technologies like single-flux quantum logic or reversible computing at low temperatures, to avoid overheating the qubit environment.

Finally, competition and ecosystem maturity are considerations. Silicon spin qubits have a growing ecosystem (e.g. Intel, HRL, Diraq, and academic efforts in Netherlands, Australia, etc.), but it’s still smaller compared to superconducting qubits’ ecosystem. Quantum Motion is among the front-runners here, and arguably their delivery of an integrated system places them ahead of most silicon-focused peers in terms of a complete product. Still, if a tech giant (say Intel) suddenly achieves a breakthrough in spin qubits, they could pour resources into catching up quickly. That said, Quantum Motion’s partnerships and head start in demonstrating a working silicon quantum computer give it a strong competitive edge for now. They also differentiate by focusing on spin qubits (electronics-based) vs. photonic or neutral-atom startups; each approach has its niche, and Quantum Motion’s niche is compatibility with the semiconductor supply chain, which is a formidable strength as outlined.

In summary, Quantum Motion’s silicon-CMOS modality offers unrivaled scalability and integration prospects – potentially delivering mass-manufacturable quantum chips and convenient form factors. The trade-offs lie in the demanding cryogenic and fabrication precision requirements, as well as the need to prove multi-qubit performance at scale. The company’s recent accomplishments (like fast tuning of 1k-dot arrays and a deployed 3-rack system) provide evidence that these challenges are being met step by step. If silicon qubits can be tamed at large scale, Quantum Motion’s approach could yield quantum computers that slot seamlessly into existing tech infrastructure – a huge win for the practical adoption of quantum computing.

Track Record

Assessing Quantum Motion’s track record, we find a history of making bold claims and then delivering on them in short order. Since its founding, the company has set clear technical goals (e.g. demonstrating CMOS qubit chips, integrating cryo-control, delivering a prototype machine) and systematically achieved them, often on schedule or earlier, which lends credibility to its future roadmap:

• Academic Foundations to Prototype (2017–2021): The company came out of leading academic groups, and initial expectations were that it would take years to show viability of silicon qubits in industry. However, within roughly four years of founding, Quantum Motion co-authored results showing integrated silicon qubits and electronics, and had developed proprietary techniques for rapid qubit characterization. This indicated a faster pace than many expected for the traditionally slow, cautious academic domain of spin qubits. The leadership leveraged academic partnerships effectively (publishing in top journals) while keeping an eye on engineering scale-up.
• Hitting Milestones Tied to Funding: When Quantum Motion raised seed and Series A funding (over £20 M by 2020), it promised to use the money to design cryogenic chips and prove scalable architectures. Indeed, by 2022 it announced record-breaking achievements – like the “thousands of multiplexed quantum dots” result – showing those funds were well spent on R&D. The big Series B in early 2023 was explicitly to “accelerate development of silicon quantum processors… deepen ties with manufacturers and treble the size of HQ”. Checking one year later, Quantum Motion had expanded facilities in London, forged the GlobalFoundries partnership, and delivered the promised large-scale processor demonstration. This suggests strong alignment between the company’s claims to investors and its execution – a positive sign for a deep-tech startup.
• Delivering the NQCC System: A critical test of track record was the NQCC full-stack computer delivery in 2025. The UK’s NQCC (National Quantum Computing Centre) is a government-backed program expecting tangible hardware contributions from companies. Quantum Motion was among the first to be awarded a testbed slot, and it met the challenge by installing its system by September 2025. This on-time delivery indicates robust project management and technical readiness – especially since building a “turn-key” quantum computer (racks, user interface, etc.) is a step beyond lab experiments. The system worked well enough to be publicly unveiled and lauded by officials. In the quantum industry, delays are common (many companies miss roadmap targets or only demonstrate partial results), so Quantum Motion’s ability to go from announcement (in 2024) of intent to actual deployment in 2025 stands out.
• External Validation and Consistency: Quantum Motion’s inclusion and advancement in DARPA’s QBI program is a form of external vetting. To progress to Phase 2, the company had to illustrate a credible plan for a utility-scale machine. Achieving this while hitting their technical milestones implies that their internal targets (often not public) were likely met or exceeded. For example, DARPA would have scrutinized their error correction approach, and the fact that in 2025 they indeed launched SiQEC (aligned with what DARPA expects) shows consistency between what they propose and what they implement. The company also tends to announce successes after they’ve been achieved (e.g. not hyping a 1,024-dot chip until they could actually validate it). This conservative communications approach builds trust in their track record: they haven’t over-promised in the media only to under-deliver later.
• Comparative Perspective: While Quantum Motion’s absolute qubit counts or demo algorithms may lag those of, say, Google’s 72-qubit superconducting chip or IonQ’s 29-qubit ion system, one must consider resource scale. With tens of millions in funding (versus hundreds of millions for some U.S. peers), the company has accomplished an impressive amount. They prioritized demonstrating scalability and integration over raw qubit counts, which now appears wise – as other companies with early large qubit counts still face scaling issues (wiring, crosstalk, etc.). Quantum Motion can point to the NQCC system and say: we have a path to grow this without redesigning the whole architecture. In contrast, some competitors that showed >50 qubits in one approach have had to rethink their architectures to go further. Thus, in terms of meeting the spirit of milestones (toward scalability), Quantum Motion’s track record is arguably stronger than some firms that achieved flashy intermediate milestones but with less long-term plan.

One area to watch is that, to date, Quantum Motion has not publicly demonstrated a multi-qubit algorithm or a high-profile quantum benchmark (such as quantum advantage experiments). Their track record is heavy on engineering proof-of-concept (which they excel at) and lighter on quantum computations performed. This is by design – they focus on the foundations first. However, as they move into 2026–27, they will need to start showing logical qubits in action. Stakeholders will expect to see, for example, a small error-corrected logical qubit maintained longer than physical ones, or a known quantum algorithm run on their silicon chip (even at small scale) to compare performance. Given their past performance, one can be cautiously optimistic they will meet these next milestones too.

Overall, Quantum Motion’s historical performance has been strong, marked by a rapid cadence of technical achievements aligned with their roadmap. They have built credibility through delivering results (partnerships, prototypes, publications) on roughly the timeline they predict. Importantly, they have balanced scientific rigor (peer-reviewed breakthroughs) with system-level progress (an actual quantum computer installation) – a combination that few startups manage so early. This track record suggests that if they commit to a target (e.g. demonstrating a logical qubit by year X), they have a fair chance of hitting it. Of course, the toughest challenges lie immediately ahead, but their execution so far bodes well.

Challenges

Despite its impressive progress, Quantum Motion faces a number of significant challenges as it strives toward a fault-tolerant silicon quantum computer. These challenges are both technical and strategic, reflecting the steep road from a working prototype to a large-scale, commercial quantum solution:

• Scaling Physical Qubits to Logical Qubits: The leap from a handful of physical qubits to the thousands or millions needed for algorithms like Shor’s is daunting. Quantum Motion’s current system likely contains only a small number of physical qubits (the exact count hasn’t been disclosed) – perhaps on the order of 4–8 qubits initially active, given it’s a first-of-kind installation. To implement even a simple error-correcting code (like a [[7,1,3]] Steane code, or a small surface code patch), dozens of physical qubits are required. Their SiQEC project will attempt a repetition code on a 2×3=6 dot array, which is a start, but scaling beyond that will test their engineering. The challenge is to show that adding more qubits doesn’t exponentially increase complexity in control. Each doubling of qubit count could introduce new cross-talk or calibration overhead. While their tile architecture is meant to be repeatable, it will only truly be proven when, say, two tiles (then four tiles, etc.) are run together. Ensuring that qubits on one tile don’t interfere with those on another, and that global control signals can synchronize across a large chip, is non-trivial. In short, the path to a first logical qubit, then to a few logical qubits, and onward will involve solving many hidden coupling problems. This is a challenge all quantum hardware companies face, but for Quantum Motion it’s particularly crucial since their approach hinges on tiling many qubits in dense proximity.
• Improving Two-Qubit Gate Fidelity: While ~98% two-qubit fidelity is excellent for silicon’s first demonstrations, it is still below the >99.9% often cited as needed for efficient error correction (depending on code). The company will have to push fidelity higher via both materials and control optimization. This might mean switching to isotopically enriched silicon wafers for future chips (to eliminate nuclear spin noise) – something not yet explicitly mentioned in their releases, but a logical step. It could also require more sophisticated calibration algorithms to cancel out tiny errors, or implementing dynamical decoupling and advanced pulse shaping in their control electronics. They’ll also need high-fidelity measurement operations, since reading out qubits without error is part of QEC; their use of RF reflectometry is fast, but the signal-to-noise needs to support >99% readout fidelity (some labs have achieved 99.9% in single-shot spin readouts using improved amplifiers). Thus, one challenge is a purely technical one of squeezing out errors: can their engineering keep improving to hit the fault-tolerance threshold? The track record is encouraging, but as fidelities approach 99.9% each additional “9” becomes harder.
• Cryogenic Engineering and Throughput: Running a million-qubit quantum computer will demand a massive cryogenic infrastructure or breakthroughs in cooling. Quantum Motion’s current fridge-in-a-rack is impressive, but scaling that to house far more qubits (or multiple chips) might require larger dilution refrigerators or parallel fridges networked together. One can imagine needing a cluster of fridges, each with thousands of qubits, all orchestrated as one computer – a formidable systems engineering task. The company will have to work closely with cryostat manufacturers (e.g., Oxford Instruments, Bluefors) to ensure cooling power scales. Additionally, the throughput of control electronics at cryo becomes critical. Quantum Motion uses multiplexing to reduce wiring, but as qubit numbers grow, they may need more layers of multiplexing or even in-fridge FPGAs/ASICs to handle the deluge of signals. Designing custom cryogenic ASIC controllers (application-specific chips) could be on the horizon – which is a challenge in itself, marrying two cutting-edge fields (quantum and advanced ASIC design). There’s also the question of error correction overhead: performing real-time QEC on thousands of qubits could overwhelm classical processors if not carefully optimized. Achieving the timing and bandwidth for stabilizing qubits continuously is a challenge that goes hand-in-hand with scaling.
• Resource and Funding Needs: As a startup, Quantum Motion will require substantial resources to go the distance. Their £62 M funding to date, while large for a UK startup, is small compared to competitors like PsiQuantum (~$700 M) or IonQ (> $500 M). Building a million-qubit machine could easily run into the hundreds of millions or more. The company will likely need additional large investment rounds or government support (or strategic partnerships with big industry) to finance new fabrication runs, cryo infrastructure, and a growing engineering team. The challenge will be to secure this funding while meeting milestones so that investors remain confident. There is a risk of a funding gap if, for example, broader economic conditions tighten or if investors favor software over hardware in coming years. Quantum Motion’s affiliation with programs like NQCC and DARPA QBI is helpful (providing non-dilutive funding and credibility), but the bulk of capital must come from venture or industry. Convincing stakeholders that silicon qubits are the horse to bet on in the crowded quantum race is an ongoing challenge – especially as other modalities show progress. The company will have to continue highlighting its unique wins (like the NQCC system) to differentiate and attract funding.
• Competition and Pace of Innovation: While Quantum Motion is a front-runner in silicon spin qubits, others are close on their heels. For instance, Diraq in Australia (a spin-out of UNSW) is also developing CMOS-compatible spin qubits and recently claimed 99.5% two-qubit fidelity in silicon. Tech giants like Intel and TSMC have quantum spin qubit research programs; if they ramp up, they could leverage enormous fab resources. There’s also the possibility of hybrid approaches emerging – e.g. someone might combine photonics and silicon spins, or superconducting control with spin qubits, potentially leapfrogging pure-play approaches. Quantum Motion must maintain a brisk pace of innovation to stay ahead. A particular competitive challenge: demonstrating clear quantum advantage or utility. Companies like IBM and Google are already exploring small error-corrected codes and quantum advantage experiments. Quantum Motion will need to showcase a compelling application on its hardware by the time others do, lest it be seen as purely a research project with distant payoff. Balancing the focus on long-term fault tolerance with some near-term milestones (like a useful quantum simulation on 10–20 qubits) may be necessary to keep momentum and public interest. In essence, they have to prove the commercial value of their approach on the way to the endgame.
• Supply Chain and Manufacturing Challenges: Even though using standard CMOS processes is a core strength, it also means reliance on external fabs (like GF). As quantum designs push the limits (e.g. needing isotopically enriched Si, or special interconnects), the company might face the need for custom process development at fabs. This can be slow and costly. There’s also the risk of being tied to one foundry; if GF’s priorities change or if capacity becomes an issue (say, competing with classical chip production), Quantum Motion might need alternatives. The global semiconductor supply chain issues (as seen in recent years) could also pose delays for them – for example, getting the latest process node time, or securing sufficient cryogenic chip testing capacity. However, being fabless also has an upshot: they are not sinking capital into building fabrication facilities themselves, which is prudent for a startup. It’s a challenge of managing partnerships and technical dependencies rather than an insurmountable problem, but worth noting.

In summary, Quantum Motion’s challenges are those of moving from an impressive prototype to a world-changing product. They must scale up the number of qubits and their reliability (technical scalability), scale up their organization and funding (business scalability), and stay competitive on the global stage. None of these challenges are trivial – any one could slow them down or, if mismanaged, derail progress. That said, the company has shown a proactive stance toward challenges: e.g., recognizing the need for a seasoned commercial leader (hiring Hugo Saleh) as they transition to product, and addressing technical issues like variability with automation and machine learning early on. Their deep integration in both the quantum research community and the semiconductor industry ecosystem gives them tools to tackle these issues.

If Quantum Motion can continue the trajectory of anticipating hurdles and solving them methodically, it stands a solid chance of surmounting the above challenges and making silicon-based quantum computing a reality.