Integrated Photonics for Quantum Computing: Scalable Platforms for Photonic Qubits and Logic Gates

By Abdul Ahad NazakatReviewed by Louis CastelAug 8 2025

Superconducting quantum computers dominate current development, but integrated photonics offers an alternative that uses photons instead of electrons for quantum information processing. Photonic qubits operate at room temperature rather than near absolute zero, maintain quantum properties longer, and resist environmental interference better than superconducting approaches. The technology applies established semiconductor manufacturing to build quantum circuits on silicon chips, addressing key challenges in scaling to millions of qubits, integrating components on single devices, ensuring reliable operations, and creating commercially viable systems. This approach suits applications where operational consistency takes precedence over raw computational speed.

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CMOS Manufacturing and Miniaturization Advantages

The biggest advantage of integrated photonics lies in its compatibility with existing semiconductor factories. CMOS fabrication techniques build quantum devices alongside classical electronics using the same infrastructure that produces computer processors and smartphone chips.¹ While other quantum approaches need entirely new manufacturing processes, photonic systems reduce production costs and development time by using established facilities.

Integrated photonic circuits shrink quantum systems to chip scale, making systems more stable and manageable compared to room-sized bulk optics setups.⁴ By placing photon sources, processors, and detectors on single chips, manufacturers eliminate lossy connections between components, improving reliability while reducing system complexity.² Traditional bulk optics require constant realignment and suffer from vibration sensitivity, while integrated systems eliminate these problems through solid-state construction that resists environmental disturbances.⁵

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Advanced Materials Enable Low-Loss Performance

Success depends on materials that barely absorb light. Silicon nitride and lithium niobate have emerged as leading choices for quantum waveguides. These materials maintain quantum information integrity over longer distances than competing approaches.²

Recent advances have achieved coupling efficiencies above 90%, substantially enhancing overall system performance.³ This means more quantum information survives processing, leading to more reliable computations.

Performance Comparison: Photons vs. Electrons

Photonic systems have different characteristics than other quantum technologies. Superconducting qubits operate faster with nanosecond gate times, while photonic systems have longer coherence times and easier connectivity between qubits. Trapped-ion systems connect all qubits directly but run slowly. Photonic qubits scale with standard manufacturing but need indirect methods for two-qubit gates. Mid-circuit measurements work straightforwardly in superconducting systems but create difficulties in photonic designs because photons cannot be stored easily. These differences suit photonic systems for certain applications rather than general-purpose quantum computing.

Essential Components for Photonic Quantum Systems

High-Brightness Single-Photon Sources: Quantum dots are a prominent technology for on-demand photon generation, with devices demonstrating brightness levels exceeding 10⁶ pairs/s/mW.³ An alternative, parametric down-conversion, produces the identical photons required for quantum interference.⁵

Ultra-Sensitive Photon Detectors: Key detection technologies include Superconducting Nanowire Single-Photon Detectors (SNSPDs), which demonstrate detection efficiencies over 98%, and Single-Photon Avalanche Diodes (SPADs), a compact alternative suitable for on-chip integration.³^,⁶

Quantum Logic Gates and Circuits: Interferometric circuits, composed of components like beam splitters and phase shifters, are the basis of photonic processors. These integrated circuits manipulate quantum states to create the interference effects necessary for computation.⁵^,⁷

Current Performance Benchmarks

Leading photonic systems have reached key performance thresholds necessary for fault-tolerance, including:³

• Single-photon source brightness: >10⁶ pairs/s/mW
• Detector efficiency: >98% (SNSPDs)
• Coupling efficiency: >90%
• Photon indistinguishability: >99%
• Logical error rates: <10⁻³

Challenges in Scaling Photonic Systems

Scaling photonic quantum systems from lab prototypes to practical architectures faces several interrelated technical hurdles, spanning fabrication precision, photon interaction limits, control complexity, and material integration:

Performance Uniformity at Scale

Although individual components perform well in laboratory settings, achieving this performance uniformly across large-scale integrated circuits is a primary obstacle. Minor fabrication variances, often negligible in classical electronics, can cause significant deviations in quantum device performance.

Lack of Native Photon Interactions

Photons do not naturally interact with each other. This property complicates the implementation of two-qubit logic gates and requires specialized architectures, such as those based on measurement and ancillary photons, to mediate interactions.⁸

Multiplexing Overhead

Scaling to a large number of qubits depends on the complex multiplexing of many single-photon sources. This process adds considerable overhead to the system's control and timing architecture.⁹ Current approaches to manage this include time-bin and frequency-domain multiplexing.³

Hybrid Integration

The integration of different material platforms (for example, quantum dot emitters with low-loss silicon nitride waveguides) remains a significant fabrication challenge. Overcoming this is critical to producing scalable sources of high-quality photons on-chip.¹⁰

Real-World Applications

Photonic quantum computers excel at simulating molecular systems, providing insights into molecular structures and reactions too complex for classical computers.¹² Applications include designing new pharmaceuticals and developing advanced materials with specific properties.

Complex optimization problems in finance also benefit from photonic quantum processing. Portfolio optimization, risk analysis, and trading strategy development represent immediate applications, with the ability to process large datasets rapidly giving financial institutions a competitive edge in decision-making.¹³

In supply chain and logistics, route optimization, inventory management, and supply chain coordination involve complex calculations that are well-suited to quantum processing. Photonic systems can tackle these problems faster than classical computers, improving efficiency and reducing operational costs.¹³

Photonic systems naturally excel at secure communications, as quantum states of light enable unbreakable encryption for sensitive data transmission. This capability is valuable for government communications, financial transactions, and corporate data protection.

Integrated photonic platforms also enable chip-to-chip quantum networking, supporting hybrid quantum systems and enhancing the robustness of quantum networks.¹⁴

What's in store for Integrated Photonics

Current photonic gates work probabilistically, which limits system efficiency. Developing deterministic photon–photon gates could remove this constraint and substantially improve performance.

Photonic fusion-based architectures also offer the potential to reduce resource overhead for fault-tolerant operations, making large-scale photonic quantum computers more practical.

Integration with AI inference chips could lead to enhanced photonic neural networks, where hybrid systems combine quantum processing with artificial intelligence to deliver advanced computational capabilities.

On-chip quantum sensing platforms could further improve precision measurement, enabling applications in medical diagnostics, environmental monitoring, and navigation systems.

Future Outlook

The commercial outlook for photonic quantum computing is currently driven by applications in secure communications and molecular simulation. Further progress toward fault-tolerant systems is contingent upon resolving technical challenges in material performance, component integration, and error correction.

Current research efforts are focused on specific solutions to these problems. The development of deterministic photon-photon gates, for example, is pursued as a method to increase operational efficiency, while architectures like fusion-based computing aim to reduce the resource overhead required by error correction codes. Other potential applications for these platforms include on-chip quantum sensors and hybrid systems integrated with classical AI hardware.

The primary advantage of integrated photonics as a scalable platform stems from its compatibility with existing semiconductor manufacturing and its ability to operate at room temperature. A company's future market position will likely depend on its ability to address the aforementioned challenges in fabrication, integration, and fault tolerance.

References and Further Reading
 

1. Won, R. (2019). Integrated solution for quantum technologies. Nature Photonics, 13(2), 77–79. https://doi.org/10.1038/S41566-019-0357-Y
2. Taheriniya, S., Varri, A., Jo, S., Lenzini, F., & Pernice, W. H. P. (2024). Exploring novel photonic platforms for quantum computing through Si ion implantation. 1–4. https://doi.org/10.1109/icton62926.2024.10647892
3. Wayo, D. D. K., Goliatt, L., & Ganji, D. D. (2025). Linear Optics to Scalable Photonic Quantum Computing. https://doi.org/10.48550/arxiv.2501.02513
4. Diamanti, E., Karinou, F., Liu, J., Moody, G., Pingault, B., Sorace-Agaskar, C., Srinivasan, K., & Zhu, D. (2022). Guest Editorial Integrated Photonics for Quantum Applications. Journal of Lightwave Technology, 40(23), 7482–7484. https://doi.org/10.1109/jlt.2022.3222586
5. Montaut, N., Roztocki, P., Yu, H., Sciara, S., Chemnitz, M., Jestin, Y., MacLellan, B. R., Fischer, B., Kues, M., Reimer, C., Romero Cortes, L., Wetzel, B., Young Zhang, Y., Loranger, S., Kashyap, R., Cino, A. C., Chu, S. T., Little, B. E., Moss, D. J., … Morandotti, R. (2023). Scalable Quantum Signal Processing with Integrated Photonics and Fiber-based Modules. 1–4. https://doi.org/10.1109/icton59386.2023.10207524
6. Giordani, T., Hoch, F., Carvacho, G., Spagnolo, N., & Sciarrino, F. (2023). Integrated photonics in quantum technologies. 46(2), 71–103. https://doi.org/10.1007/s40766-023-00040-x
7. Moody, G., Sorger, V. J., Juodawlkis, P. W., Loh, W., Sorace-Agaskar, C., Davanco, M., Chang, L., Bowers, J. E., Quack, N., Galland, C., Aharonovich, I., Wolff, M. A., Schuck, C., Sinclair, N., Loncar, M., Komljenovic, T., Weld, D., Mookherjea, S., Buckley, S., … Camacho, R. M. (2021). Roadmap on Integrated Quantum Photonics. arXiv: Quantum Physics. https://arxiv.org/abs/2102.03323
8. Bourassa, J. E., Alexander, R. N., Alexander, R. N., Vasmer, M., Vasmer, M., Patil, A., Tzitrin, I., Matsuura, T., Su, D., Baragiola, B. Q., Guha, S., Dauphinais, G., Sabapathy, K. K., Menicucci, N. C., & Dhand, I. (2020). Blueprint for a Scalable Photonic Fault-Tolerant Quantum Computer. arXiv: Quantum Physics. https://doi.org/10.22331/Q-2021-02-04-392
9. Aghaee Rad, H., Ainsworth, T. L., Alexander, R. N., Altieri, B., Falamarzi Askarani, M., Baby, R., Banchi, L., Baragiola, B. Q., Bourassa, J. E., Chadwick, R. S., Charania, I., Chen, H., Collins, M. J., Contu, P., D’Arcy, N., Dauphinais, G., De Prins, R., Deschenes, D., Di Luch, I., … Zhang, Y. (2025). Scaling and networking a modular photonic quantum computer. Visual Education. https://doi.org/10.1038/s41586-024-08406-9
10. Sartison, M., Camacho Ibarra, O., Caltzidis, I., Reuter, D., & Jöns, K. D. (2022). Scalable integration of quantum emitters into photonic integrated circuits. Materials for Quantum Technology, 2(2), 023002. https://doi.org/10.1088/2633-4356/ac6f3e
11. AbuGhanem, M. (2024). Photonic Quantum Computers. https://doi.org/10.48550/arxiv.2409.08229
12. Wang, J., Sciarrino, F., Laing, A., & Thompson, M. G. (2020). Integrated Photonic Quantum Technologies. arXiv: Quantum Physics. https://doi.org/10.1038/S41566-019-0532-1
13. Xu, X., & Jin, X.-M. (2023). Integrated Photonic Computing beyond the von Neumann Architecture. ACS Photonics. https://doi.org/10.1021/acsphotonics.2c01543
14. 2022 Roadmap on integrated quantum photonics. (2022). 4(1), 012501. https://doi.org/10.1088/2515-7647/ac1ef4
15. Pelucchi, E., Fagas, G., Aharonovich, I., Englund, D., Figueroa, E., Gong, Q., Hannes, H., Liu, J., Lu, C., Matsuda, N., Pan, J.-W., Schreck, F., Sciarrino, F., Silberhorn, C., Wang, J., & Jöns, K. D. (2021). The potential and global outlook of integrated photonics for quantum technologies. Nature Reviews Physics, 4(3), 194–208. https://doi.org/10.1038/s42254-021-00398-z

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