Silicon Quantum Computing

(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

Silicon Quantum Computing (SQC) is an Australian quantum hardware company based in Sydney, founded in May 2017 as a UNSW Sydney spin-off by Prof. Michelle Simmons (2018 Australian of the Year). It was launched as Australia’s first quantum computing company with A$83 million in seed backing from the Australian government, UNSW, Telstra, Commonwealth Bank (CBA), and others. SQC’s mission is to develop the world’s first scalable, error-corrected quantum computer using silicon-based spin qubits – specifically phosphorus atoms “placed” in silicon with atomic precision. The company operates out of UNSW’s Centre for Quantum Computation and Communication Technology (CQC²T) labs, leveraging unique infrastructure for atomically-precise device fabrication. Prof. Simmons serves as CEO, and in 2024 SQC appointed former Arm CEO Simon Segars as its new Board Chair to help steer the transition from research to commercialization. SQC today leads a world-class team of quantum physicists and engineers, and has positioned itself at the forefront of silicon quantum computing globally.

History & Leadership: SQC was born from decades of UNSW research that pioneered silicon spin qubits – including the first single-atom transistor in 2012 and the first two-qubit logic gate in silicon in 2015. The company’s founding represented a public-private partnership to keep this breakthrough IP in Australia and translate it into a 10-qubit silicon quantum integrated circuit prototype by 2022. Early board members included Prof. Simmons, industry leaders from Telstra and CBA, and government representatives. Over the years, SQC has attracted top talent – notably, ex-Google quantum leader John Martinis joined as a consultant in 2020 – and forged partnerships to bolster its capabilities (e.g. a silicon-28 enrichment program with Silex for ultra-pure substrates). As of 2025, SQC’s leadership emphasizes both technical excellence and industrial strategy: Simmons (CEO) continues to drive the scientific vision, Segars (Chair) provides semiconductor industry expertise, and the board includes tech and finance veterans to guide SQC’s growth.

Milestones & Roadmap

Technology Achievements: SQC has steadily delivered against its technical milestones, often ahead of schedule. By 2021, the team achieved entanglement of three donor spin qubits (two electron spins and one nuclear spin) in silicon – a world-first for donor systems. In 2022, SQC announced the world’s first integrated quantum computing circuit at the atomic scale, built by precision placement of 10 quantum dots (phosphorus atoms) in silicont. This device functioned as an analog quantum processor that successfully modeled the quantum states of a small organic molecule (polyacetylene), validating SQC’s approach to quantum simulation Notably, this breakthrough came “two years ahead of schedule” relative to SQC’s original 10-qubit prototype timeline – a fact heralded by investors and Australia’s science minister as evidence of the team’s ability to hit ambitious targets. Following this, SQC turned its focus to increasing qubit count and maintaining high fidelity: by 2023-24, they had demonstrated a four-qubit programmable device (three nuclear spins with one electron ancilla) on which they ran a three-qubit Grover’s search algorithm with ~95% success probability. Importantly, all single- and two-qubit operations in that 4-qubit processor achieved error rates below the rough 1% “fault-tolerance” threshold (single-qubit fidelities >99.9%, two-qubit ~99%) – a record level of performance indicating that SQC’s qubit quality is on par with the best in the industry.

Roadmap: SQC’s publicly stated roadmap envisions a rapid scaling of qubit numbers alongside the integration of error correction. After the 10-atom prototype in 2022, the next milestone is a 100-qubit device, which the company aims to achieve later this decade. In mid-2022, SQC’s then-Chair Stephen Menzies projected that their unique approach “ensures [qubit] scalability and quality” and said “we’re on track to deliver useful commercial quantum computing by 2028.” This implies SQC hopes to have an intermediate-scale quantum computer capable of solving meaningful problems (e.g. quantum chemistry or optimization tasks) within the next few years. To reach full fault-tolerance (capable of breaking modern cryptography or tackling intractable problems), SQC recognizes that millions of physical qubits may be required; the company’s long-term vision is to leverage the atomic-level precision of its platform to eventually fabricate a silicon quantum chip with 1 million+ qubits all on one processor. As an intermediate step, SQC has set a goal of achieving the first logical (error-corrected) qubit in silicon, which it views as a key validation on the road to a scalable fault-tolerant machine. In 2023, SQC completed a Series A funding round (A$50.4M) specifically to support this next phase – the creation of a logical qubit and scaling beyond – while deferring larger capital raises until needed.

Recent Updates (2024-2025): SQC’s progress and credibility have been bolstered by participation in major international programs. In 2025, SQC was selected by the U.S. Defense Advanced Research Projects Agency (DARPA) for its Quantum Benchmarking Initiative (QBI), which aims to identify viable paths to a utility-scale quantum computer by 2033. SQC passed Stage A of QBI – wherein it presented a detailed architecture and plan for a large-scale silicon quantum computer – and in November 2025 DARPA promoted SQC to Stage B with a new contract. In Stage B, SQC is now working with DARPA to develop a “detailed research and development roadmap through to 2033,” including technical requirements and risk-mitigation prototypes. Advancing in this competitive program (which also includes IBM, IonQ, Quantinuum, PsiQuantum, Diraq, and others) serves as validation of SQC’s long-term roadmap credibility. It indicates that by the early 2030s SQC aims to construct a “utility-scale” quantum computer – defined by DARPA as one whose computational value exceeds its cost – using its all-silicon platform.

In addition to government partnerships, SQC is engaging industry early. The company has begun delivering what it calls “Quantum Machine Learning (QML) chips” and quantum chemistry modeling services to strategic partners as a way to provide value before full-scale machines are ready. For example, telecom giant Telstra (one of SQC’s investors) has tested SQC’s prototype QML processors (“Watermelon” chips) for network AI tasks – reportedly accelerating model training significantly. SQC also announced a collaboration with NVIDIA in late 2025 to develop “NVQlink” high-speed GPU–quantum interconnects, preparing for tight integration of its silicon quantum processors with classical supercomputers. These efforts underscore that SQC’s roadmap is not just about increasing qubit count, but also about building the surrounding ecosystem (software, cryo-electronics, and user applications) needed for a commercial quantum computer by the ~2028–2033 timeframe.

Focus on Fault Tolerance

From the outset, SQC’s approach has prioritized quality over sheer qubit quantity, with the explicit aim of achieving fault-tolerant quantum computing (FTQC) on a silicon chip. The choice of atomically precise donor qubits was in part to maximize coherence times and gate fidelities so that error correction becomes feasible at smaller scales. This strategy is paying off: SQC’s recent 4-qubit experiments reported gate fidelities above the commonly cited fault-tolerance threshold (~99%) for every operation. With these building blocks, SQC is now turning to implementing quantum error correction in hardware. As noted, the company’s next milestone is to demonstrate a logical qubit in silicon – likely by encoding a qubit across multiple physical donor spins with real-time error detection and correction. Achieving a high-fidelity logical qubit (e.g. using a small stabilizer code) would be a landmark: it would prove that silicon qubits can not only retain quantum information longer than any single physical qubit could, but also that errors can be actively corrected in an array of donor spins.

SQC’s donor architecture offers some intrinsic advantages for fault tolerance. Each phosphorus donor in silicon contains both an electron spin and a nuclear spin, which can serve as a built-in ancilla or long-lived memory. SQC has leveraged this in experiments – for example, using an electron spin to mediate multi-qubit controlled-Z gates between nuclear spin qubits with high fidelity. The nuclear spins have extraordinary coherence (measured in seconds) and can be read out in a quantum non-demolition manner, meaning the act of measurement need not reset the qubit. These features are highly conducive to error correction schemes. In fact, researchers have pointed out that a small register of nuclear spins linked by an electron (as SQC demonstrated) could function as a miniature error-corrected unit – and “coupling neighboring nuclear spin registers via electron–electron exchange may enable larger, fault-tolerant quantum processors.” This hints at SQC’s modular architecture vision: clusters of donor qubits (each cluster perhaps containing a few logical qubits protected by local error correction) could be interconnected by electron exchange or other coupling mechanisms into a large-scale FTQC system.

Ultimately, SQC is targeting a surface-code-like architecture implemented in silicon, where error-corrected logical qubits are realized through a 2D grid of physical qubits with nearest-neighbor couplings. Reaching that stage will require not just high fidelities, but also dense scaling and automated calibration. Company statements emphasize the integration of on-chip control and repeaters to manage errors. SQC’s manufacturing precision already allows donor placements with sub-nanometer accuracy, which is critical for uniform error rates across a large array. Looking forward, SQC’s participation in programs like DARPA QBI suggests its focus in Stage B will be to articulate how they will go from the current few-qubit prototypes to a large, fault-tolerant architecture by 2033, identifying the necessary error-correcting code, the number of physical qubits per logical qubit, and how to overcome any remaining error sources on the chip. By incrementally demonstrating a logical qubit (and eventually logical operations) in the next couple of years, SQC could position itself among the leaders in the race to build a truly fault-tolerant quantum computer.

CRQC Implications

If Silicon Quantum Computing’s technology progresses on its current trajectory, it could have significant implications for the timeline of “Q-Day” – the day when a quantum computer can break current cryptographic codes. SQC’s approach is one of several promising routes to a cryptographically relevant quantum computer (CRQC), which is typically estimated as a machine with thousands of low-error logical qubits performing billions of operations. While SQC’s near-term goal of ~100 physical qubits by 2027-2028 won’t threaten RSA encryption, their focus on error correction means even a modest-sized silicon quantum processor from SQC could execute small instances of Shor’s algorithm or other cryptographically-relevant algorithms with higher success rates than larger but error-prone machines. In other words, 100 high-fidelity silicon spin qubits (especially if some error correction is deployed) might accomplish what 1,000 noisy superconducting qubits cannot. This efficiency could “shift the landscape of global cybersecurity”, as SQC itself notes.

That said, fully cracking RSA-2048 or comparable cryptography will almost certainly require scaling to millions of physical qubits (or on the order of a few hundred logical qubits) – a scale SQC also acknowledges in its long-term plan. SQC’s participation in DARPA’s QBI, alongside companies like IBM, IonQ, Quantinuum, and PsiQuantum, underscores a broad consensus that the early 2030s are the target for achieving utility-scale quantum computers (which by definition would include CRQC capability). SQC’s own internal target of a commercial-grade, error-corrected quantum computer by ~2028 and a utility-scale machine by 2033 aligns with this timeline. If SQC meets its milestones, it could slightly accelerate the arrival of Q-Day by providing yet another pathway (beyond superconducting, ion-trap, and photonic qubits) to reach the requisite scale. In particular, silicon’s compatibility with semiconductor manufacturing suggests that once the core qubit technology is proven, scaling up could become an engineering and manufacturing exercise, potentially ramping faster than some other approaches. For instance, SQC’s team has noted that “once one can reliably fabricate and operate a single silicon qubit, making thousands on a chip is just a matter of reticle size and fabrication” – hinting that a decisive jump to large qubit counts might occur if silicon qubits reach a tipping point of stability.

However, as of 2025, SQC is still at the few-qubit prototype stage, and other industry leaders are already demonstrating devices with tens or hundreds of qubits (though with higher error rates). Companies like IBM and Quantinuum project having on the order of 100 logical qubits (many thousands of physical qubits) by 2030, which could put RSA-cracking in reach. SQC’s timeline – logical qubit ~2025-26, 100-qubit device ~2028, and scaled fault-tolerant system ~2033 – is roughly in parallel to these efforts. The impact on Q-Day therefore largely depends on execution: if SQC can exploit its high-fidelity approach to leapfrog in qubit count via chip integration, it might speed up the advent of a CRQC. Conversely, if scaling challenges slow them down, Q-Day will more likely be triggered by a superconductor-based or ion-based quantum computer from a better-resourced competitor. At the very least, SQC’s progress adds competitive pressure and technical diversity to the global quantum race. The fact that multiple modalities (trapped ions, superconductors, photonics, and silicon spins) are all advancing toward ~2030 fault-tolerance means the odds of somebody reaching Q-Day in that timeframe are increasing. SQC’s silicon-based platform, with its potentially more compact and stable qubits, could be the dark horse that pushes the industry over the finish line – or provides a critical backup approach if others falter. In summary, SQC’s technology, if successful, will contribute to making CRQCs a reality and could slightly pull forward the timeline for large-scale quantum computing, though it currently appears broadly aligned with the 2030± few years horizon that many leading players anticipate for breaking cryptographic codes.

Modality & Strengths/Trade-offs

Technology Platform: SQC’s quantum hardware modality is donor spin qubits in silicon – a form of solid-state qubit that leverages the well-studied physics of phosphorus dopant atoms in ultra-pure silicon. Each qubit in this approach is typically the spin of an electron bound to a P atom (or the spin of the P atom’s nucleus), which represents the quantum 0/1 states via spin-up vs. spin-down. Neighboring donor qubits interact via the exchange coupling of their electrons, which can be controlled by precisely positioning donors a few nanometers apart and tuning surface electrodes. SQC’s differentiator is that it can fabricate these donor qubits with atomic precision: using a scanning tunneling microscope (STM) lithography technique, they position individual P atoms in a silicon crystal with 0.1 nm accuracy. The devices are then encapsulated in silicon, aligning the donors exactly where needed to form quantum gates. This level of control is unmatched by other silicon-based efforts that rely on implanted donors or electrostatic quantum dots, and it yields qubits of exceptionally high quality.

Strengths – “The SQC Edge”: SQC touts several key advantages of its atom-scale silicon platform:

• Unrivaled Qubit Fidelity: Silicon spin qubits benefit from extremely low noise and long coherence. By using isotopically enriched $$^{28}$$Si (which has zero nuclear spin) and no interfacial oxides or stray materials near the qubits, SQC has achieved record-low charge noise in its devices. In practice, this means qubits maintain coherence for long durations (electron spin coherence times can be milliseconds, and nuclear spins seconds long). The payoff is seen in gate fidelities: SQC has demonstrated single-qubit rotation errors on the order of 0.01% and two-qubit gate errors ~0.05% in small devices. These numbers (99.99% 1-qubit fidelity, ~99.95% 2-qubit) meet or exceed the best reported in any platform. For example, a 2025 Nature Nanotechnology experiment by SQC showed all operations above 99% fidelity and a three-qubit entangled GHZ state with 96% fidelity – a level of performance that rivals state-of-the-art ion traps and far surpasses typical superconducting qubits. High fidelities reduce the overhead needed for error correction, giving SQC a potential shortcut to scale (each logical qubit can be realized with fewer physical qubits if error rates are low).
• Fast Gate Speeds: Silicon spin qubits can be manipulated very quickly. Because the electron spins have gigahertz resonance frequencies, SQC can perform single-qubit operations in sub-nanosecond timescales (on the order of 10^-10 seconds) – much faster than, say, the microsecond-scale gates in ion traps. Even two-qubit operations mediated by exchange coupling or electron ancillas are fast (tens of nanoseconds or less). SQC highlights that its gate operations as short as ~0.8 ns allow for extremely rapid circuit execution, which is advantageous for algorithms requiring many sequential gates and for reducing error accumulation over time. Fast gates combined with long coherence means a silicon qubit can undergo many more operations before losing its quantum state (enabling “deep” circuits).
• On-Chip Integration & Manufacturing Control: Unlike most quantum hardware startups that rely on external foundries or exotic fabrication, SQC controls its entire manufacturing process in-house. They have developed a rapid prototyping cycle where a new chip design can be fabricated in “1–2 weeks” and tested, enabling quick iteration. This is possible because SQC’s atomic-scale lithography, while unique, has been refined into a repeatable process. The company currently produces on the order of one quantum processor chip per week and is looking to scale that up dramatically. Owning the fab process means SQC can co-develop classical control circuitry and 3D packaging optimized for their qubits. It also means intellectual property (IP) is retained (a strategic goal noted at the company’s founding). Moreover, silicon’s compatibility with CMOS processes suggests down the line SQC can integrate cryogenic classical electronics (like amplifiers or multiplexers) alongside qubits on the same chip – an approach already being pursued in academic collaborations. This “full-stack” control (from materials to algorithms) positions SQC to be a one-stop-shop for quantum hardware solutions.
• Scalability & Density: Phosphorus donors in silicon are atom-sized – only ~0.2 nm across – which means qubits can be extremely densely packed. In theory, millions of spin qubits could fit on a square millimeter of silicon area. SQC notes that its qubits are so small that a future 1-million-qubit processor could fit in a single dilution refrigerator, unlike some approaches that require large optical setups or multiple modules. This density is not just about space; it also speaks to cost-effectiveness. Leveraging semiconductor manufacturing, once the process is proven, scaling up the number of qubits should add relatively little marginal cost per qubit. SQC’s vision is to capitalize on the decades of semiconductor industry experience – akin to leveraging a “Moore’s Law” for qubits – to scale from a few qubits to thousands and then millions on chip. This is a distinct strength: where some other modalities might hit physical limits or unwieldy wiring challenges when qubit counts grow, silicon chips are designed to handle billions of components.
• Stability and Operability: Solid-state qubits, once calibrated, can potentially operate continuously without the need for reloading particles (ions) or maintaining complex laser systems. SQC’s devices use only static magnetic fields and DC+RF electric signals; there are no lasers or moving parts, which simplifies the engineering. If kept at a stable cryogenic temperature, an SQC chip could in principle run for extended periods with minimal intervention – as long as environmental noise is shielded. The qubits being embedded in a crystal lattice means they are less sensitive to vibrations or stray fields compared to trapped particles. This inherent stability is an advantage for a future quantum computer that needs to run error correction cycles continuously over days or weeks.

Despite these strengths, trade-offs and challenges exist for SQC’s silicon approach:

• Extreme Fabrication Precision Required: The flipside of atomic precision is that it is required for the qubits to function properly. Tiny deviations in donor placement or local electrostatic environment can drastically affect qubit behavior. For instance, an error of ~1 nm in spacing can change the exchange coupling by orders of magnitude, and slight gate voltage differences can detune resonance frequencies. This means scaling up to many qubits will demand exquisite uniformity and calibration. While SQC’s STM patterning is precise, it’s also a serial process – currently each qubit is placed atom-by-atom. Achieving uniform precision across thousands of qubits (and doing so efficiently in terms of time/cost) is a major challenge. SQC is exploring ways to automate and parallelize atomic-scale fabrication, but this is cutting-edge manufacturing science. Any “tolerance for disorder is very low” in this platform, so SQC must essentially achieve near perfection in each device it builds – a non-trivial requirement as they scale production.
• Connectivity and Architecture Complexity: In SQC’s donor qubit chips, qubits typically interact only with their nearest neighbors (via exchange) unless additional coupling mechanisms are introduced. This inherently local connectivity (often a 1D or 2D grid) can incur overhead for algorithms that need distant qubits to communicate. To mitigate this, researchers have proposed shuttling electrons between donors or using intermediary elements (like quantum-dot chains or superconducting resonators) to couple far-apart spins. SQC might need to incorporate such innovations for a larger processor – which adds architectural complexity. Their 2022 molecule simulator chip was essentially an analog chain of 10 quantum dots, suitable for that specific task but not a general all-to-all gate model. For a universal quantum computer, SQC will likely adopt a 2D donor lattice for error correction (surface code), which requires nearest-neighbor gating on a plane – a natural fit, but any logical operations between distant logical qubits would rely on sequences of swap gates or shuttling. Maintaining coherence through those operations will be challenging until error correction is fully in place.
• Control and Readout Overhead: Each donor qubit may need multiple control lines (for tuning its energy levels, controlling exchange with neighbors, and performing spin flips) and a readout sensor. In current devices, SQC uses nanoelectronic sensors (e.g. single-electron transistors) to read the spin state of donors via electron tunneling – a process that can take on the order of 100 microseconds per measurement. Scaling up, SQC will have to implement multiplexed readout schemes so that many qubits can be measured efficiently, and likely integrate cryogenic electronics to manage the hundreds or thousands of wires that would otherwise be needed. This is a challenge common to all solid-state approaches, but particularly acute for silicon spin qubits where control voltages must be finely tuned for each qubit. SQC is undoubtedly working on custom control ASICs (application-specific ICs) that operate at 4 K or mK to reduce the wiring count – similar to efforts by Intel and others (indeed, SQC’s sister company Diraq has demonstrated cryo-CMOS drivers in a dilution fridge). Still, integrating these without degrading qubit performance (noise from classical circuitry, heat dissipation, etc.) is non-trivial. SQC’s advantage is their low-power qubits (controlled by voltages, not power-hungry microwaves for each qubit as in some superconducting systems), which might make co-integration easier, but engineering a full control stack remains a heavy lift.
• Manufacturing Throughput & Cost: Today, SQC’s atom-by-atom fabrication is labor-intensive and slow compared to conventional chip fab. As noted, the company produces roughly one new device per week in the lab setting. To build a million-qubit machine, this pace must increase by orders of magnitude or require massive parallelism. SQC is exploring scale-up via the Australian government’s National Reconstruction Fund – aiming to build a dedicated quantum chip fabrication facility that could output many more devices and perhaps automate the STM lithography process. Until such scaling is achieved, there is a risk that SQC’s approach could lag behind approaches that piggyback on existing mass-fab infrastructure (for example, Diraq and Intel are using modified CMOS transistor fabs to make quantum dot qubits). SQC’s reliance on very specialized equipment could become a bottleneck. On the flip side, the “patient capital” approach of its investors suggests they are aware of this and are in it for long-term gains. The company deliberately took a smaller Series A in 2023 to wait for more favorable funding conditions before a big expansion, indicating that scaling manufacturing will be tied to the next major capital infusion.

In summary, SQC’s modality of precision donor qubits in silicon offers remarkable strengths in qubit fidelity, stability, and potential integrability, but it comes with serious challenges in scaling and engineering. The company’s strategy is to tackle these challenges one by one – demonstrating capability (e.g. high-fidelity few-qubit logic), then addressing scale (through automated manufacturing and error correction). The trade-off they have made is clear: by not using standard foundry processes early on (unlike some competitors), they gained qubit performance at the cost of immediate scalability. The next few years will reveal if that trade-off pays off – if SQC can marry its superior qubit quality with a viable path to thousands or more qubits, it could leap ahead of the field. If not, easier-to-manufacture modalities (like CMOS quantum dots or photonic qubits) might outpace it in reaching large system sizes. SQC is well aware of this balance, as evidenced by their emphasis on in-house manufacturing innovation and partnerships (like the silicon-enrichment project and likely future alliances to improve production).

Track Record

Historical Performance: Since its inception in 2017, SQC has built an impressive track record of scientific firsts and has generally met the milestones it set out, instilling confidence in its approach. Key highlights of SQC’s journey include:

• 2017: Incorporated with A$83M seed funding and a mandate to build a 10-qubit prototype by 2022. Established a board blending academia, industry (Telstra, CBA), and government stakeholders to oversee this ambitious goal.
• 2018–2019: Early results as a company included refining one- and two-qubit operations in silicon. In 2019, the UNSW team (precursor to SQC) set a world record for the fastest two-qubit gate in silicon (a controlled-NOT operation in 0.8 ns, demonstrating the raw speed possible with donor qubits). They also achieved the quietest (lowest-noise) silicon qubits on record by improving material purity and device design. These advances were essential stepping stones toward high-fidelity multi-qubit control.
• 2020: SQC gained international attention by hiring Professor John Martinis, who had led Google’s quantum supremacy experiment, as a part-time advisor. His expertise in building superconducting qubit systems was brought in to help SQC with quantum processor engineering and scale-up strategies. (Martinis’ stint at SQC was relatively short, but it underscored SQC’s intent to learn from other platforms’ successes and failures.)
• 2021: Achieved three-qubit entanglement in silicon and executed simple algorithms. In a June 2021 Nature paper, Simmons’ team demonstrated a three-qubit Greenberger–Horne–Zeilinger (GHZ) state using two donor electrons and one donor nuclear spin. They followed this with quantum tomography of a 3-qubit state and showed that their system could be precisely characterized. By early 2022, they ran a basic quantum algorithm (a version of the Deutsch–Jozsa algorithm) on these three qubits, showing that a small-scale logic circuit was functional. This was a validation that donor qubits could form the building blocks of a quantum CPU, not just exist in isolation.
• 2022: Delivered the 10-“qubit” silicon integrated circuit ahead of schedule. In June, SQC announced the creation of a 10 quantum dot analog processor – effectively a quantum simulation chip – and published the results in Nature. This device’s ability to simulate molecular orbitals of a polyacetylene chain was hailed as “the biggest result of [Michelle Simmons’] career” and proof that SQC’s technology could tackle useful problems. Achieving this milestone two years earlier than planned earned SQC significant credibility. It also coincided with SQC opening a Series A funding round to fuel the next phase of development (originally seeking A$130M).
• 2023: Closed a A$50.4M Series A round in July 2023, backed by existing stakeholders (Australian government, UNSW, Telstra, CBA). While below the initial target, this raise more than doubled SQC’s valuation (to ~A$195M) and was considered an “up-round” in a tight funding climate. The strategy was to secure enough capital to reach the logical qubit and ~10+ qubit prototype stage, and postpone a larger raise until the market improved. The Series A press release also reaffirmed SQC’s focus on building an error-corrected silicon quantum computer and highlighted “the world’s first integrated atomic-scale circuit” as a key proof point. Technically, 2023 saw SQC integrate 4 qubits (3 nuclear + 1 electron) in a single device with all-around fidelity above 99%, and this work was submitted for publication (appearing in Feb 2025 in Nat. Nanotechnology). Also in late 2023, Prof. Michelle Simmons delivered the prestigious Boyer Lectures in Australia on the future of quantum computing, raising public awareness and support for SQC’s mission. In October 2023, Simmons was awarded the Prime Minister’s Prize for Science for her contributions, further cementing SQC’s reputation.
• 2024: Transition to a more commercial footing. In February, SQC appointed Simon Segars (former CEO of Arm) as its new Chair and added seasoned executive Fiona Pak-Poy to the board. This marked an inflection from pure R&D governance to adding semiconductor industry and scale-up expertise at the board level – a signal that SQC was preparing for manufacturing expansion and productization. Technologically, SQC continued to push qubit performance: a paper in Nature (Feb 2024) demonstrated high-fidelity initialization and control in a four-qubit donor device, indicating that the team was systematically solving challenges of controlling multiple qubits simultaneously. SQC also announced progress in nuclear spin initialization (an important step for using nuclear qubits reliably) in a 4-qubit chip. By mid-2024, SQC was likely working with 6-qubit and larger layouts internally (given their “14|15 qubit” platform branding), although details remain proprietary.
• 2025: Gaining global validation and expanding partnerships. In April 2025, SQC won a DARPA QBI Stage A contract and by November had advanced to Stage B, as detailed earlier, securing US government funding to refine its roadmap. This was a significant vote of confidence on the world stage, effectively ranking SQC among the top quantum hardware contenders. SQC also deepened its customer engagements: it publicized results with Telstra (using SQC chips for AI acceleration in telecom networks) and with the Australian Department of Defence (a partnership on quantum machine learning announced August 2025). In October 2025, SQC’s collaboration with NVIDIA on GPU-QPU interconnects was revealed, showing that SQC is preparing for integration into high-performance computing workflows. Technologically, by late 2025 SQC reported achieving record single-shot readout fidelities at relatively high temperatures (e.g. 1.5 K), an important engineering milestone to relax cryogenic requirements. The company also claimed a 98.9% success rate on Grover’s algorithm without error correction on a small silicon processor – essentially confirming that their 3-4 qubit system can run a simple quantum search with almost perfect accuracy just through native fidelity. Such results are world-leading and garnered media interest as indicators that SQC’s approach may leapfrog the NISQ-era noise limitations.

Funding and Investment: To date, SQC’s funding has come from a combination of government grants, strategic corporate partners, and venture investment – with a strong emphasis on domestic (Australian) support to keep the technology onshore. The initial A$83M (~USD$60M) seed in 2017 was unprecedented for a university spin-off, reflecting the national importance placed on quantum computing. Major shareholders from that round include the Australian federal government (through its innovation fund), the NSW state government, UNSW Sydney, Telstra, and CBA, each contributing significant capital (e.g. UNSW and researchers $25M, CBA $14M, Telstra $10M, Commonwealth govt $25M, NSW $8.7M). This consortium funding model has given SQC a broad base and patience to pursue long-term research without the same pressure for immediate returns that a pure VC-backed startup might face.

The Series A closed in 2023 at A$50.4M brought the total equity funding to about A$133M. Notably, it was again largely supported by existing investors (Australia’s public and private sectors) – a sign that these stakeholders remain committed despite the tighter capital environment. The round valued SQC at roughly A$195 million post-money. SQC’s CEO highlighted that raising a smaller amount in 2023 was strategic: “in periods where capital is expensive, we see it as advantageous to defer a bigger raise”. This implies SQC may seek a much larger Series B or C in the coming years (potentially tapping government funds like the National Reconstruction Fund or international partners) once it achieves the next technical milestones and when market conditions are right. Indeed, discussions with the A$15 billion NRF have been reported, aiming to scale up SQC’s manufacturing in Australia.

Additionally, SQC now benefits from non-dilutive funding via research contracts. The DARPA QBI program, for example, can award on the order of USD$15 million for Stage B efforts – a substantial boost to R&D funding. SQC is also part of Australian government initiatives (the 2023 National Quantum Strategy identified SQC as a key player) and has received Australian Defense contracts for specific projects (quantum sensing/ML). These grants and contracts likely add tens of millions in support, effectively extending SQC’s runway.

Overall, SQC’s funding approach has been deliberately conservative and strategic: they have raised money in alignment with achieving critical technical proofs, and relied on strong government backing to maintain stability. While the total funding (~US$90M equity + some grants) is modest compared to US competitors like PsiQuantum (~$650M raised) or IonQ (~$350M), SQC has stretched dollars by leveraging university infrastructure and focusing on a single, lean hardware approach. The coming years may require heavier capital investment (for a production facility, hiring, etc.), but SQC has positioned itself as a national champion in quantum tech, which likely means continued support from Australian government funds and strategic industry investors keen to see a home-grown quantum computer materialize.

Challenges

As Silicon Quantum Computing moves from demonstration to development of a large-scale quantum computer, it faces several key challenges:

• Scaling Qubit Count: Thus far, SQC has controlled up to only a handful of qubits in a single device. Scaling to tens, then hundreds, and eventually thousands of qubits will test the limits of SQC’s fabrication and control techniques. Every additional qubit requires another atom to be placed and more control lines – the complexity could grow quadratically. Even though the company can pack qubits densely, ensuring each one is functional and integrating them without performance loss is uncharted territory. The step from 10 operational qubits (analog analog simulator) to, say, 50 or 100 will likely reveal new cross-talk and calibration issues that must be solved. SQC will need to invest in automation, sophisticated calibration algorithms, and possibly new fabrication methods (like multi-tip parallel STM lithography or advanced patterning) to reach those qubit counts in a reasonable timeframe. Competing approaches (like superconductors and ions) have already hit 50+ qubits and are learning from those scaling issues now; SQC will have to play catch-up in system integration know-how.
• Manufacturing Throughput & Engineering Talent: Building a quantum chip one atom at a time is inherently slow. SQC’s plan to “dramatically” increase chip output means scaling its lab operations to an industrial process. This will involve significant capital expenditure on new facilities and equipment, and hiring of specialized engineers (nanofabrication experts, automation engineers, etc.). There is global competition for such talent, and SQC, being in Australia, must offer compelling incentives to attract/retain experts (though having a visionary founder and a unique project helps). The company’s strong ties with UNSW and government may help pipeline talent, but ramping from ~50 staff to perhaps hundreds for a production line will be a management challenge. There’s also technical risk in trying to automate STM-based manufacturing – something that has never been done at scale. If that effort falters, SQC could hit a wall in scaling, which would open the door for alternate silicon approaches (e.g. CMOS quantum dots by Diraq/Intel) to pull ahead.
• Competition and Market Timing: SQC operates in a highly competitive global race. Other leading quantum hardware companies – IBM, Google, Quantinuum, IonQ, PsiQuantum, Intel, Atom Computing, Xanadu, to name a few – each have substantial funding and momentum. Many of these are targeting similar timelines for a first generation of fault-tolerant computers (~late 2020s to early 2030s). SQC’s unique approach gives it a chance to excel in qubit quality, but competitors may compensate with sheer qubit quantity or different tech. Notably, Diraq, the UNSW spin-off formed in 2022 by Prof. Andrew Dzurak, is effectively a local competitor taking a different path to silicon quantum computing (standard CMOS-based quantum dot qubits). Diraq has outlined a multi-phase roadmap to 1,000+ qubits and a million-qubit fault-tolerant chip by 2033, similar to SQC’s goals. While SQC has better qubit fidelity today (donor qubits vs. Diraq’s quantum dots), Diraq is leveraging existing chip fab infrastructure and has hit 99%+ fidelities too. These two will be interesting to watch – they may compete or eventually collaborate (their approaches could even be complementary in a hybrid system). In any case, SQC must keep an eye on not just scientific milestones but also market relevance: being first to demonstrate a logical qubit or a particular qubit count could attract partnerships and funding, whereas if a competitor does it first, SQC might struggle to differentiate. The good news for SQC is that its inclusion in programs like DARPA QBI puts it on equal footing with the big players in the eyes of key stakeholders.
• Resource and Infrastructure Needs: Building a quantum computer company is expensive. While SQC has been frugal with funds, truly scaling up will require more money, bigger labs (or fabs), and a robust supply chain (for things like isotopically enriched silicon, cryostats, control electronics, etc.). Any delays or shortfalls in funding could slow progress. The Australian government’s support will be crucial – and it appears positive, with new initiatives aiming to grow advanced manufacturing domestically that SQC can tap. Still, compared to the billions being poured into quantum by the US, EU, and China, SQC’s war chest is relatively small. They may need to seek international investors or partnerships (without compromising on keeping IP in Australia – a delicate balance). The next few years likely involve SQC scaling its physical footprint from a research lab to a pilot fabrication line – a transition that has tripped up many hardware startups in other sectors.

Despite these challenges, SQC has several factors working in its favor: a clear vision, technical excellence, strong foundational IP, and committed stakeholders. The company has so far delivered on promises (or exceeded them), which builds trust with investors and partners. Its focus on error-correction readiness, rather than just NISQ-era demos, could pay off as the industry increasingly values progress toward fault-tolerance over raw qubit numbers. Moreover, SQC’s successes contribute to the narrative that silicon-based quantum computing is a contender, attracting interest from semiconductor giants and governments who see synergy with existing chip industries.

In conclusion, Silicon Quantum Computing is a pioneering player in quantum hardware, pursuing a bold approach that could yield one of the highest-quality qubit platforms in the world. The company’s history of milestones – from the first atomically precise quantum chip to running algorithms above error-correction thresholds – showcases a track record of innovation. Going forward, SQC’s ability to scale up (both technically and organizationally) will determine if it can compete with the best-funded quantum giants. If it succeeds, SQC’s silicon quantum processors could become a cornerstone technology, potentially delivering on the promise of an error-corrected quantum computer on a chip and influencing the timeline of quantum advantage and Q-Day.