Pioneering the Future of Top Quantum Computer Technology: Andrew Dzurak’s Silicon Strategy | quantum 100
- Gokul Rangarajan
- Sep 24
- 19 min read
How Andrew Dzurak and Diraq Are Driving the Silicon-CMOS Revolution in Scalable Quantum Computing
In this blog, we explore how Professor Andrew Dzurak is shaping the frontier of top quantum computer development by pioneering silicon-based spin qubits and founding Diraq, a quantum computing company spun out from UNSW Sydney. You will read about Dzurak’s foundational research, how he achieved landmark demonstrations with Andrea Morello, and how Diraq is now pushing toward utility-scale quantum processors built with CMOS architectures. This is a narrative of merging quantum physics, semiconductor engineering, and commercialization to realize next-generation quantum computers.
We begin with Dzurak’s research genesis, move through milestone breakthroughs in silicon qubit fidelity, and end with Diraq’s vision and challenges on the road to scalable top quantum computer systems.
At Pitchworks, our Quantum100 initiative is more than a directory it’s a living map of the people and ideas accelerating the quantum revolution. Through deep-dive features, we highlight leaders whose vision is shaping the enterprise adoption of quantum technologies. We’ve already explored the pioneering role of Dr. Jay Gambetta in superconducting quantum computing and the transformative leadership of Peter Chapman at IonQ. Building on that journey, this blog turns the spotlight toward another figure whose impact has redefined the pace and perception of quantum progress Hartmut Neven, the architect behind Google’s Quantum AI Lab and one of the most influential forces in bringing quantum from theory into practice. In our recent blog we spoke about Hartmut Naven and Robert Suotr Jeremy O’Brien and Christopher Monroe, Chad Rigetti
From Quantum Research to Real-World Quantum Computing
Andrew Steven Dzurak is an Australian physicist and electrical engineer whose career has orbited the intersection of nanoelectronics and quantum computing. He holds the position of Scientia Professor in Quantum Engineering at the University of New South Wales (UNSW), is an ARC Laureate Fellow, and sits on the Executive Board of the Sydney Quantum Academy. Wikipedia+2podcast.newquantumera.com
In 2022, Dzurak co-founded Diraq, a spin-out from UNSW, to commercialize the body of work his research group had built in silicon qubit technology. podcast.newquantumera.com His vision: build top quantum computer processors that can scale to millions of qubits using the same industrial infrastructure that powers classical computing silicon chips.
Dzurak’s academic lineage includes deep expertise in semiconductor devices and cryogenic nanoelectronics. His early research on hot electrons, ballistic transport, quantum dots, and nanostructures fed naturally into quantum information science. WikipediaOver time, he and collaborators—most notably Andrea Morello—pioneered spin qubits in silicon, culminating in landmark experiments of single-atom electron spin control, coherent readout, and two-qubit gates.
What makes Dzurak’s work especially compelling in the ecosystem of top quantum computer research is the marriage of quantum coherence and CMOS compatibility: qubit technologies that can leverage existing foundry processes and scale in wafer form. That approach contrasts with many alternate qubit modalities (superconducting, trapped ions, topological, etc.). The ambition is a quantum computer that can be fabricated in a way analogous to classical integrated circuits.
The Breakthroughs: Single-Atom Qubits, Two-Qubit Gates, and CMOS Spin Qubits
One of the earliest major demonstrations came in 2012, when Dzurak and Morello’s groups demonstrated the first silicon-based qubit: a single-atom electron spin in silicon. This achievement helped establish the feasibility of using silicon for quantum information — a material already foundational to classical electronics. Quantum Machines
Their approach harnessed extremely precise fabrication and control to isolate and manipulate a single electron’s spin state in silicon, and to detect its quantum state through single-shot measurement techniques. This experiment was widely recognized as a major step toward top quantum computer architectures that are silicon-based.
Subsequently, the challenge was not only single-qubit control but entangling operations, i.e. two-qubit logic gates. In 2015, Dzurak’s team published a key paper reporting a two-qubit logic gate in silicon—a necessary ingredient for universal quantum computation. Wikipedia This was considered a breakthrough because universal quantum computing requires both single-qubit and two-qubit operations. As Dzurak himself noted, “All quantum computations can be made up of one-qubit operations and two-qubit operations … the accuracy of both operations needs to be very high.” UNSW Sites
After that, quantifying the fidelity (i.e. error rates) of two-qubit gates became crucial. In 2019, Dzurak’s group provided the first rigorous measurement of two-qubit fidelity in silicon by employing randomized benchmarking techniques. They reported an average two-qubit gate fidelity of 98%. LinkedInThis result marked a leap forward in establishing silicon qubits as credible contenders in the race toward top quantum computer platforms.
Over time, improvements in device engineering, pulse calibration, materials, and control techniques led to further enhancements. For example, research has shown that single-qubit gate operations in isotopically enriched silicon can exceed 99.9% fidelity. arXiv+2Keio
In more recent work, teams have pushed these devices to operate at higher temperatures (above 1 Kelvin), which is critical for easing cryogenic constraints when scaling. In 2024, a notable paper demonstrated spin qubit operations in silicon above 1 K, achieving readout and initialization fidelities up to 99.34% and two-qubit gate fidelity of 98.92%. Nature
Also in 2024, Diraq announced that their SiMOS (silicon metal-oxide-semiconductor) quantum dot platform had achieved above 99% fidelity for two-qubit gates in a consistent, repeatable manner — a milestone for CMOS-compatible quantum processing. The Quantum Insider
These fidelity thresholds are significant because they approach or cross the error rates needed for quantum error correction and fault tolerance, a must for building top quantum computer systems that can compute reliably at scale.
Beyond fidelity improvements, Dzurak’s group and collaborators have also contributed to theoretical and simulation work to support device design. For instance, a recent paper describes a path-integral Monte Carlo algorithm to simulate exchange interactions in CMOS spin qubit devices, helping to assess robustness and disorder tolerance in real devices. Physical Review
Diraq’s Commercialization and Architecture Strategy
Dzurak’s decision to spin out Diraq stemmed from the need to transition from laboratory demonstrations to well-engineered, scalable top quantum computer architectures. Diraq’s goal is bold: to build utility-scale quantum processors that host millions (and eventually billions) of qubits on standard silicon wafers, with integrated quantum-classical electronics. podcast.newquantumera.com
One of Diraq’s key strategic advantages is leveraging existing CMOS and silicon foundry infrastructure. By aligning qubit fabrication and design with industrial standards (e.g., 300 mm wafer processing), Diraq aims to scale qubit arrays far faster than bespoke quantum hardware approaches. LinkedInMachines
In doing so, Diraq is also investing in the co-integration of quantum and classical control electronics on the same silicon platform. That means deploying cryogenic, low-power control circuits, fast signal routing, and calibration systems in close proximity to qubits—minimizing latency, wiring overhead, and energy dissipation. Quantum Machines.
In 2025, Diraq won the Technology Platform category at the NSW iAwards, an acknowledgment of its innovation in quantum technologies. UNSW Sites
Another element of Diraq’s strategy is the use of AI and advanced analytics to optimize large qubit arrays. Diraq has publicly announced collaborations to employ AI and machine learning for scale optimization, error mitigation, and control calibration, with the ambition of driving qubit numbers on a single chip into the millions and even billions. Quantum Machines
In sum, Diraq is bridging the gap between quantum physics breakthroughs and practical engineering of top quantum computer systems.
Challenges, Trade-offs & the Road Ahead
While the path forward is exciting, building a top quantum computer in silicon is fraught with technical and architectural challenges.
One such challenge is error uniformity and reproducibility across many qubits. Even if one pair of qubits can reliably achieve 99% fidelity, a system of millions must maintain consistency across all pairs. A recent paper on assessing error variation in spin qubit processors highlights that devices vary over time and across replication, and that kinds of noise—slow nuclear, electrical, contextual—must be carefully mitigated. Nature
Thermal constraints rise rapidly with scale. Operating at millikelvin temperatures imposes severe cooling power limits. One strategy is to push qubit operation to higher temperatures (above 1 K), where cooling power is dramatically larger—already demonstrated in laboratory experiments. Nature But as temperature increases, maintaining very low error rates becomes harder due to thermal excitations, noise, and reduced contrast in measurement.
Another challenge is wiring and interconnect complexity. Each qubit must receive control signals and deliver readout results, so routing millions of control lines with signal integrity, cross-talk suppression, shielding, and low dissipation is a massive engineering problem. Co-integrating classical control on chip helps alleviate, but at the cost of design complexity and co-design between quantum and classical domains.
Scalable architectures must also incorporate quantum error correction and fault tolerance. That means mapping logical qubits onto many faulty physical qubits, maintaining syndrome measurement, feedback, and error suppression. The threshold for error correction is strict; achieving fidelities well above threshold is essential. Many contemporary architectures aim for topological codes, surface codes, or variations thereof. Diraq’s progress toward >99% two-qubit fidelity is a critical step in reaching those thresholds. The Quantum Insider
Integration with classical computing stacks is also key: quantum processors must be orchestrated by classical controllers, compute schedulers, and error-correction logic. Ensuring fast quantum-classical communication and control loops is crucial for any top quantum computer to function in practice.
Finally, scaling from small demonstrators (tens to hundreds of qubits) to massive systems (millions) will require ecosystem support—foundries, design automation tools, calibration infrastructures, testing, yield enhancement, and a robust supply chain.
Still, given the degree of progress already achieved, the momentum is strong.
Why Dzurak’s Approach Matters for Top Quantum Computer Roadmaps
Andrew Dzurak’s work is significant because it offers a compelling, industrially aligned route to quantum scale. Many quantum hardware approaches struggle when trying to scale beyond small qubit numbers; they often rely on bespoke fabrication, exotic materials, or highly delicate control infrastructures. Dzurak’s vision is different: treat qubits as nanoelectronic devices, design them in silicon, and build them using CMOS or foundry methods.
That philosophy brings multiple advantages:
Scalability: You can leverage existing silicon wafer processing and design ecosystems to scale qubits in large arrays.
Compatibility: Integration of control electronics on the same substrate can reduce wiring burden and latency.
Cost efficiency: Mass fabrication in mature semiconductor lines can reduce per-qubit cost.
Ecosystem synergy: The semiconductor industry already has design automation, QC testing, yield improvement, and supply chains that could benefit quantum chip manufacturing.
Dzurak’s group has published over 200 research papers, with many in Science / Nature / high-impact journals, and holds over 30 patents across multiple families, underscoring his leadership in both fundamental science and technological translation. (From your description.)
The steady improvements in fidelity, the push toward operation at elevated temperatures, the simulation of device behavior, and the transition into commercial efforts through Diraq all point toward a maturing silicon-spin qubit platform that may become one of the key pillars in the future top quantum computer landscape. From Quantum Research to Real-World Quantum Computing
Andrew Steven Dzurak is an Australian physicist and electrical engineer whose career has orbited the intersection of nanoelectronics and quantum computing. He holds the position of Scientia Professor in Quantum Engineering at the University of New South Wales (UNSW), is an ARC Laureate Fellow, and sits on the Executive Board of the Sydney Quantum Academy. Wikipedia
In 2022, Dzurak co-founded Diraq, a spin-out from UNSW, to commercialize the body of work his research group had built in silicon qubit technology. podcast.newquantumera.com His vision: build top quantum computer processors that can scale to millions of qubits using the same industrial infrastructure that powers classical computing silicon chips.
Dzurak’s academic lineage includes deep expertise in semiconductor devices and cryogenic nanoelectronics. His early research on hot electrons, ballistic transport, quantum dots, and nanostructures fed naturally into quantum information science. WikipediaOver time, he and collaborators—most notably Andrea Morello—pioneered spin qubits in silicon, culminating in landmark experiments of single-atom electron spin control, coherent readout, and two-qubit gates.
What makes Dzurak’s work especially compelling in the ecosystem of top quantum computer research is the marriage of quantum coherence and CMOS compatibility: qubit technologies that can leverage existing foundry processes and scale in wafer form. That approach contrasts with many alternate qubit modalities (superconducting, trapped ions, topological, etc.). The ambition is a quantum computer that can be fabricated in a way analogous to classical integrated circuits.
The Breakthroughs: Single-Atom Qubits, Two-Qubit Gates, and CMOS Spin Qubits
One of the earliest major demonstrations came in 2012, when Dzurak and Morello’s groups demonstrated the first silicon-based qubit: a single-atom electron spin in silicon. This achievement helped establish the feasibility of using silicon for quantum information — a material already foundational to classical electronics. Quantum Machines
Their approach harnessed extremely precise fabrication and control to isolate and manipulate a single electron’s spin state in silicon, and to detect its quantum state through single-shot measurement techniques. This experiment was widely recognized as a major step toward top quantum computer architectures that are silicon-based.
Subsequently, the challenge was not only single-qubit control but entangling operations, i.e. two-qubit logic gates. In 2015, Dzurak’s team published a key paper reporting a two-qubit logic gate in silicon—a necessary ingredient for universal quantum computation. Wikipedia This was considered a breakthrough because universal quantum computing requires both single-qubit and two-qubit operations. As Dzurak himself noted, “All quantum computations can be made up of one-qubit operations and two-qubit operations … the accuracy of both operations needs to be very high.” UNSW Sites
After that, quantifying the fidelity (i.e. error rates) of two-qubit gates became crucial. In 2019, Dzurak’s group provided the first rigorous measurement of two-qubit fidelity in silicon by employing randomized benchmarking techniques. They reported an average two-qubit gate fidelity of 98%. LinkedInThis result marked a leap forward in establishing silicon qubits as credible contenders in the race toward top quantum computer platforms.
Over time, improvements in device engineering, pulse calibration, materials, and control techniques led to further enhancements. For example, research has shown that single-qubit gate operations in isotopically enriched silicon can exceed 99.9% fidelity. arXiv
In more recent work, teams have pushed these devices to operate at higher temperatures (above 1 Kelvin), which is critical for easing cryogenic constraints when scaling. In 2024, a notable paper demonstrated spin qubit operations in silicon above 1 K, achieving readout and initialization fidelities up to 99.34% and two-qubit gate fidelity of 98.92%. Nature
Also in 2024, Diraq announced that their SiMOS (silicon metal-oxide-semiconductor) quantum dot platform had achieved above 99% fidelity for two-qubit gates in a consistent, repeatable manner — a milestone for CMOS-compatible quantum processing. The Quantum Insider
These fidelity thresholds are significant because they approach or cross the error rates needed for quantum error correction and fault tolerance, a must for building top quantum computer systems that can compute reliably at scale.
Beyond fidelity improvements, Dzurak’s group and collaborators have also contributed to theoretical and simulation work to support device design. For instance, a recent paper describes a path-integral Monte Carlo algorithm to simulate exchange interactions in CMOS spin qubit devices, helping to assess robustness and disorder tolerance in real devices. Physical Review
Diraq’s Commercialization and Architecture Strategy
Dzurak’s decision to spin out Diraq stemmed from the need to transition from laboratory demonstrations to well-engineered, scalable top quantum computer architectures. Diraq’s goal is bold: to build utility-scale quantum processors that host millions (and eventually billions) of qubits on standard silicon wafers, with integrated quantum-classical electronics. podcast.newquantumera.com
One of Diraq’s key strategic advantages is leveraging existing CMOS and silicon foundry infrastructure. By aligning qubit fabrication and design with industrial standards (e.g., 300 mm wafer processing), Diraq aims to scale qubit arrays far faster than bespoke quantum hardware approaches. LinkedIn
In doing so, Diraq is also investing in the co-integration of quantum and classical control electronics on the same silicon platform. That means deploying cryogenic, low-power control circuits, fast signal routing, and calibration systems in close proximity to qubits—minimizing latency, wiring overhead, and energy dissipation. Quantum
In 2025, Diraq won the Technology Platform category at the NSW iAwards, an acknowledgment of its innovation in quantum technologies. UNSW Sites
Another element of Diraq’s strategy is the use of AI and advanced analytics to optimize large qubit arrays. Diraq has publicly announced collaborations to employ AI and machine learning for scale optimization, error mitigation, and control calibration, with the ambition of driving qubit numbers on a single chip into the millions and even billions. Quantum Machines
In sum, Diraq is bridging the gap between quantum physics breakthroughs and practical engineering of top quantum computer systems.
Challenges, Trade-offs & the Road Ahead
While the path forward is exciting, building a top quantum computer in silicon is fraught with technical and architectural challenges.
One such challenge is error uniformity and reproducibility across many qubits. Even if one pair of qubits can reliably achieve 99% fidelity, a system of millions must maintain consistency across all pairs. A recent paper on assessing error variation in spin qubit processors highlights that devices vary over time and across replication, and that kinds of noise—slow nuclear, electrical, contextual—must be carefully mitigated. Nature
Thermal constraints rise rapidly with scale. Operating at millikelvin temperatures imposes severe cooling power limits. One strategy is to push qubit operation to higher temperatures (above 1 K), where cooling power is dramatically larger—already demonstrated in laboratory experiments. Nature But as temperature increases, maintaining very low error rates becomes harder due to thermal excitations, noise, and reduced contrast in measurement.
Another challenge is wiring and interconnect complexity. Each qubit must receive control signals and deliver readout results, so routing millions of control lines with signal integrity, cross-talk suppression, shielding, and low dissipation is a massive engineering problem. Co-integrating classical control on chip helps alleviate, but at the cost of design complexity and co-design between quantum and classical domains.
Scalable architectures must also incorporate quantum error correction and fault tolerance. That means mapping logical qubits onto many faulty physical qubits, maintaining syndrome measurement, feedback, and error suppression. The threshold for error correction is strict; achieving fidelities well above threshold is essential. Many contemporary architectures aim for topological codes, surface codes, or variations thereof. Diraq’s progress toward >99% two-qubit fidelity is a critical step in reaching those thresholds. The Quantum Insider
Integration with classical computing stacks is also key: quantum processors must be orchestrated by classical controllers, compute schedulers, and error-correction logic. Ensuring fast quantum-classical communication and control loops is crucial for any top quantum computer to function in practice.
Finally, scaling from small demonstrators (tens to hundreds of qubits) to massive systems (millions) will require ecosystem support—foundries, design automation tools, calibration infrastructures, testing, yield enhancement, and a robust supply chain.
Still, given the degree of progress already achieved, the momentum is strong.
Why Dzurak’s Approach Matters for Top Quantum Computer Roadmaps
Andrew Dzurak’s work is significant because it offers a compelling, industrially aligned route to quantum scale. Many quantum hardware approaches struggle when trying to scale beyond small qubit numbers; they often rely on bespoke fabrication, exotic materials, or highly delicate control infrastructures. Dzurak’s vision is different: treat qubits as nanoelectronic devices, design them in silicon, and build them using CMOS or foundry methods.
That philosophy brings multiple advantages:
Scalability: You can leverage existing silicon wafer processing and design ecosystems to scale qubits in large arrays.
Compatibility: Integration of control electronics on the same substrate can reduce wiring burden and latency.
Cost efficiency: Mass fabrication in mature semiconductor lines can reduce per-qubit cost.
Ecosystem synergy: The semiconductor industry already has design automation, QC testing, yield improvement, and supply chains that could benefit quantum chip manufacturing.
Dzurak’s group has published over 200 research papers, with many in Science / Nature / high-impact journals, and holds over 30 patents across multiple families, underscoring his leadership in both fundamental science and technological translation. (From your description.)
The steady improvements in fidelity, the push toward operation at elevated temperatures, the simulation of device behavior, and the transition into commercial efforts through Diraq all point toward a maturing silicon-spin qubit platform that may become one of the key pillars in the future top quantum computer landscape.
In summary, this blog has traced the journey of Professor Andrew Dzurak from pioneering silicon spin qubits to leading a commercial quantum computing venture, Diraq, aiming to build top quantum computer systems based on CMOS-compatible architectures. Dzurak’s approach harnesses decades of engineering and quantum research to chart a scalable path to millions-of-qubit quantum processors.
His collaborations with Andrea Morello yielded the first demonstrations of silicon qubits and two-qubit logic. Over time, his teams pushed fidelity thresholds upward, ventured into operation above 1 K, and addressed challenges of reproducibility, noise, and architecture. With Diraq now active in commercialization, with awards, partnerships, AI strategies, and wafer-scale ambitions, his work is no longer just academic—it is influencing the real trajectory of quantum computing.
The road ahead is steep. Scaling to millions of qubits while maintaining ultra-low error rates, integrating classical control, embedding error correction, and ensuring manufacturability are nontrivial tasks. But with the foundations already in place and continuous progress, Dzurak’s vision for top quantum computer systems based on silicon spin qubits represents one of the most promising and pragmatic pathways forward.
Year | Title / Reference | Key Contribution / Notes (especially on control, CMOS, scaling, fidelity) |
2012 | A single-atom electron spin qubit in silicon (Pla, Tan, Dehollain, Lim, Morton, Jamieson, Dzurak, Morello) (arXiv) | Demonstrated coherent control (Rabi oscillations, single-shot readout) of an electron spin bound to a phosphorus donor in silicon, with coherence times > 200 μs. This is a foundational experiment showing that atomic spins in a device-compatible silicon environment can serve as qubits. |
2013 | High-fidelity readout and control of a nuclear spin qubit in silicon (Pla et al., with Dzurak & Morello) (PubMed) | Demonstrated control and measurement of a nuclear spin qubit in silicon with high fidelity, pushing forward hybrid electron-nuclear spin approaches, and showing that the nuclear spin can be reliably interfaced and read out. |
2015 | A two-qubit logic gate in silicon (Veldhorst et al., including Dzurak & Morello) (UNSW Sites) | Realization of a controlled two-qubit gate (e.g. a CNOT-equivalent) in silicon-based qubits. This is a pivotal step toward universality (since arbitrary quantum algorithms require two-qubit gates). |
2016 | Silicon CMOS architecture for a spin-based quantum computer (Veldhorst, Eenink, Yang, Dzurak) (arXiv) | Proposed (in preprint / architecture form) a full quantum processor architecture built entirely with CMOS-compatible devices: the idea of transistor-based control, dense 2D arrays of spin qubits, control circuits and charge-storage electrodes, readout, and compatibility with surface-code error correction. |
2018 | Assessment of a Silicon Quantum Dot Spin Qubit Environment via Noise Spectroscopy (Chan et al., with Morello & Dzurak) (Physical Review) | Used a spin qubit device as a “sensor” to probe the noise spectral environment, comparing sensitivity to charge noise, electrical fluctuations, and assessing implications for coherence (T₂*, dephasing). This helps guide control strategies, shielding, and noise mitigation. |
2019 | Coherent spin control of s-, p-, d- and f-electrons in a silicon quantum dot (Leon et al., including Dzurak & Morello) (arXiv) | Demonstrated that different “shells” (orbital levels) in a quantum dot can be manipulated, and characterized how higher-shell electrons respond more strongly to electric driving fields, thereby offering insight into design of control signals, speed, and tradeoffs. |
2023 | Path integral simulation of exchange interactions in CMOS qubit devices (Cifuentes et al.) (arXiv) | Presented a path-integral Monte Carlo (PIMC) algorithm to compute exchange coupling in realistic 3D quantum-dot geometries, useful for device modeling and quantum control design, especially in dense CMOS-like geometries. |
2023 | Impact of electrostatic crosstalk on spin qubits in dense CMOS quantum dot arrays (Cifuentes, Tanttu, Gilbert, Leon, Saraiva, Yang, Hudson, Dzurak, etc.) (arXiv) | Analyzed how capacitive coupling between gates in a dense layout causes shifts in the qubit’s g-factor (via Stark shifts), which in turn affects the Larmor frequency and dephasing. This work is crucial for robust control in dense arrays. |
Year | Title / Reference | Key Contribution / Notes (especially on control, CMOS, scaling, fidelity) |
2012 | A single-atom electron spin qubit in silicon (Pla, Tan, Dehollain, Lim, Morton, Jamieson, Dzurak, Morello) (arXiv) | Demonstrated coherent control (Rabi oscillations, single-shot readout) of an electron spin bound to a phosphorus donor in silicon, with coherence times > 200 μs. This is a foundational experiment showing that atomic spins in a device-compatible silicon environment can serve as qubits. |
2013 | High-fidelity readout and control of a nuclear spin qubit in silicon (Pla et al., with Dzurak & Morello) (PubMed) | Demonstrated control and measurement of a nuclear spin qubit in silicon with high fidelity, pushing forward hybrid electron-nuclear spin approaches, and showing that the nuclear spin can be reliably interfaced and read out. |
2015 | A two-qubit logic gate in silicon (Veldhorst et al., including Dzurak & Morello) (UNSW Sites) | Realization of a controlled two-qubit gate (e.g. a CNOT-equivalent) in silicon-based qubits. This is a pivotal step toward universality (since arbitrary quantum algorithms require two-qubit gates). |
2016 | Silicon CMOS architecture for a spin-based quantum computer (Veldhorst, Eenink, Yang, Dzurak) (arXiv) | Proposed (in preprint / architecture form) a full quantum processor architecture built entirely with CMOS-compatible devices: the idea of transistor-based control, dense 2D arrays of spin qubits, control circuits and charge-storage electrodes, readout, and compatibility with surface-code error correction. |
2018 | Assessment of a Silicon Quantum Dot Spin Qubit Environment via Noise Spectroscopy (Chan et al., with Morello & Dzurak) (Physical Review) | Used a spin qubit device as a “sensor” to probe the noise spectral environment, comparing sensitivity to charge noise, electrical fluctuations, and assessing implications for coherence (T₂*, dephasing). This helps guide control strategies, shielding, and noise mitigation. |
2019 | Coherent spin control of s-, p-, d- and f-electrons in a silicon quantum dot (Leon et al., including Dzurak & Morello) (arXiv) | Demonstrated that different “shells” (orbital levels) in a quantum dot can be manipulated, and characterized how higher-shell electrons respond more strongly to electric driving fields, thereby offering insight into design of control signals, speed, and tradeoffs. |
2023 | Path integral simulation of exchange interactions in CMOS qubit devices (Cifuentes et al.) (arXiv) | Presented a path-integral Monte Carlo (PIMC) algorithm to compute exchange coupling in realistic 3D quantum-dot geometries, useful for device modeling and quantum control design, especially in dense CMOS-like geometries. |
2023 | Impact of electrostatic crosstalk on spin qubits in dense CMOS quantum dot arrays (Cifuentes, Tanttu, Gilbert, Leon, Saraiva, Yang, Hudson, Dzurak, etc.) (arXiv) | Analyzed how capacitive coupling between gates in a dense layout causes shifts in the qubit’s g-factor (via Stark shifts), which in turn affects the Larmor frequency and dephasing. This work is crucial for robust control in dense arrays. |
In summary, this blog has traced the journey of Professor Andrew Dzurak from pioneering silicon spin qubits to leading a commercial quantum computing venture, Diraq, aiming to build top quantum computer systems based on CMOS-compatible architectures. Dzurak’s approach harnesses decades of engineering and quantum research to chart a scalable path to millions-of-qubit quantum processors.
His collaborations with Andrea Morello yielded the first demonstrations of silicon qubits and two-qubit logic. Over time, his teams pushed fidelity thresholds upward, ventured into operation above 1 K, and addressed challenges of reproducibility, noise, and architecture. With Diraq now active in commercialization, with awards, partnerships, AI strategies, and wafer-scale ambitions, his work is no longer just academic—it is influencing the real trajectory of quantum computing.
The road ahead is steep. Scaling to millions of qubits while maintaining ultra-low error rates, integrating classical control, embedding error correction, and ensuring manufacturability are nontrivial tasks. But with the foundations already in place and continuous progress, Dzurak’s vision for top quantum computer systems based on silicon spin qubits represents one of the most promising and pragmatic pathways forward.
References
Wikipedia – “Andrew Dzurak” (biographical background and career).
UNSW Sydney Newsroom – “Quantum world first: Researchers can now tell how accurate two-qubit calculations in silicon really are” (2019).
TechCentral.ie – “Silicon two-qubit fidelity measured for first time” (2019).
Nature – “Operation of a silicon spin qubit above one kelvin” (2024).
The Quantum Insider – “Diraq drives two-qubit gate accuracy in CMOS to above 99%” (2024).
Quantum Machines – Interview with Andrew Dzurak on scalable quantum computing with silicon CMOS qubits (2022).
Quantum Machines – Press Release: Diraq and QM employ AI for scaling silicon-based quantum computers with NVIDIA DGX Quantum (2023).
UNSW Sydney Newsroom – “UNSW quantum computing spin-out wins industry award” (2025).
Arxiv Preprint – “Assessment of a Silicon Quantum Dot Spin Qubit Environment via Noise Spectroscopy” (2018).
Arxiv Preprint – “Path integral simulation of exchange interactions in CMOS qubit devices” (2023).
Arxiv Preprint – “Impact of electrostatic crosstalk on spin qubits in dense CMOS quantum dot arrays” (2023).
Arxiv Preprint – “Coherent spin control of s-, p-, d- and f-electrons in a silicon quantum dot” (2019).
Arxiv Preprint – “Silicon CMOS architecture for a spin-based quantum computer” (2016).
Arxiv Preprint – “A single-atom electron spin qubit in silicon” (2012).
PubMed – “High-fidelity readout and control of a nuclear spin qubit in silicon” (2013).
APS Physical Review B – “Path-integral Monte Carlo simulation of exchange interactions in CMOS qubit devices” (2023).
APS Physical Review Applied – “Noise spectroscopy with a silicon spin qubit” (2018).
Nature Physics – “Error variation in silicon spin qubits” (2024).
InspireHEP – “Silicon spin qubit control and readout circuits in 22 nm CMOS” (2024).
UNSW Research Repository – Publications list for Andrew Dzurak and Andrea Morello (multiple relevant conference and journal papers).
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